For new readers: Kalshi’s keyword mention contracts for earnings calls are event contracts regulated by the CFTC. They resolve based on whether management, like — Tim Cook and CFO Kevan Parekh or call operator(s) says a specific word during the call itself.

Call Structure

Apple’s earnings calls are the most scripted in big tech. Our review of 83 transcripts spanning 24 years shows a pattern that borders on mechanical.

The format rarely varies. Cook delivers prepared remarks touching each product segment in order — iPhone, Mac, iPad, Wearables, Services — then pivots to values, geopolitics, and macro positioning. Parekh follows with a financial walk: segment revenue, gross margin, guidance. Then Q&A, where eight to ten analysts from bulge-bracket firms ask pre-telegraphed questions they have been publishing in preview notes all week.

Cook is one of the most disciplined call operators and his linguistic fingerprint is just as consistent. He almost never introduces vocabulary that wasn’t in his prepared script. He does not name unannounced products. He deflects hardware roadmap questions with one rehearsed phrase: “We don’t comment on products we haven’t announced.”

The Last Dance

On April 19 — eleven days before this earnings call — Apple announced that Tim Cook will step down as CEO on September 1, handing the role to hardware chief John Ternus.

This is one of Cook’s final two earnings calls as the person who ran the most valuable company in history for fifteen years. That changes the tenor of everything.

Outgoing CEOs in their final quarters do not experiment with new language. They do not float new narratives or speculate about long-dated products. They do the opposite. They anchor to legacy, stabilize investor confidence, and deliver the story they want told on their way out. For Cook, that story has three pillars: the Services empire, the Apple Silicon transition, and the supply chain discipline that produced $600 billion in U.S. manufacturing commitments.

The Free Look: FOLDABLE Is The Cleanest NO On The Board

The foldable iPhone — rumored as “iPhone Fold” or “iPhone Ultra,” expected September 2026 — is the most-covered Apple story in tech media right now. Bloomberg, The Information, Ming-Chi Kuo, and Mark Gurman have all written extensively about it inside the last 30 days. The press coverage is everywhere. The management language is nowhere.

While we believe there is a chance an analyst may say something to the effect of

“Could you tell us about the ongoing rumors about a foldable iPhone?”

We expect Cook, or any other company rep to say the usual…

“We don’t speculate on future products.”

Cook has never named an unannounced product on an earnings call. Not in 24 years. Not once. Analysts who ask get the same deflection line every time. An outgoing CEO protecting his legacy has even less incentive to make speculative hardware promises he won’t be around to deliver on.

Our model has this contract scoring in the single digits. Wherever the market sits on this one, the directional read is clear: foldable does not appear in management’s spoken language on Thursday.

The same logic applies to GLASSES, with even stronger force. Apple’s AR/smart glasses are not expected until late 2026 at the earliest, with some estimates pushing into early 2027. There is no shipped product, no confirmed spec, no name. Press coverage is loud. Apple’s investor communications are silent. In over eight decades of cumulative earnings call transcript history, Apple has never named a glasses-category product before launch.

Both of these are the same trade structurally — the news-cycle volume is doing the work in market pricing, and the management discipline is doing the work in our analysis.

POSITION DISCLOSURE:

The author of this article holds the following positions:

• kxearningsmentionsaapl — “FOLDABLE” — NO

• kxearningsmentionsaapl — “GLASSES” — NO

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Kalshi traders are pricing Zyn US shipment volume for Q1 2026 at an implied median at or about 183 million cans. PMI reports the number on April 22. Based on our own research and modeling we believe Zyn will ship a number north of 183 million cans.

PMI’s own 2026 guidance calls for +15 to 20% full-year growth. Nielsen offtake ran +23% YoY in Q4 2025. Haypp’s US pouch category grew 35% in January. PMI’s CFO confirmed on the Q4 call that 25M cans of surplus channel inventory still need to unwind. Q1 is historically a build quarter, not a draw.

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Every primary source on the buy side, sell side, and scanner side points up. The market is pricing down. Below is our thesis:

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We’re coming off a big win on Nike’s earning call. The Truist strikes are new for our machine learning model, and our first analysis of a bank.

Truist Bank is the 7th largest bank in the nation with over 1900 branches nationwide. Their call is scheduled for pre-market on Friday April 17th it will be broadcasted here.

We believe their prepared remarks will stress heavily: revenue growth, NIM drifting upward, and managements path to 15% ROTCE by 2027. Driven by heavy investments into mid-market lending, investment banking, and AI-integrations.

Kalshi Traders currently have the odds of management talking about the following topics set at:

We ran every word listed above through our ML probability model, context classifier, and social research overlay.

Below is our research based on each strike listed above.

TAILWIND at 51¢ is the most underpriced contract on this board

Two recent Truist calls had management use the word unprompted.

In Q4 2025, Ebrahim Poonawala (BofA) asked about rate sensitivity and NIM in a lower-rate environment. Rogers responded with the phrase “natural tailwind” when describing QE and lower rates for the industry.

In Q3 2025, Ken Usdin (Autonomous) pressed on fixed-rate asset repricing timelines. Maguire described the repricing effect as a “tailwind” for NIM as higher-yielding assets replaced maturing low-yield positions. Again, his framing, not parroting.

Two different executives. Two different analysts. Same word. Both tied to rates and NIM.

Why Q1 should repeat the pattern:

The Fed is on hold. Repricing dynamics are front and center for banks this quarter. Truist’s own commentary and sector previews have NIM trajectory and balance-sheet positioning as top discussion topics.

Poonawala and Usdin are both on the Truist coverage list and almost certainly get questions again. Poonawala reliably asks about rate sensitivity and structural NIM. Usdin is the dedicated repricing/NIM mechanic. When either asks their usual question, the most natural language Rogers or Maguire have reached for historically is exactly “tailwind” — either “natural tailwind” or “repricing tailwind.”

• Management at multiple banks (JPM, regional peers) has used “tailwind” in NIM/NII and balance sheet discussions.

• Sector commentary going into this earnings week repeatedly frames loan growth and NIM as tailwinds for the group.

• Rogers used “natural tailwind” for the industry in Q4 2025 when Poonawala asked about rate sensitivity.

• Maguire used “tailwind” in Q3 2025 to describe fixed-rate asset repricing when Usdin pressed him.

Our conclusion: “tailwind” is currently common bank jargon for exactly the topics Truist will be asked about on this call (NIM, repricing, NII). Recent peer calls show both analysts and management are comfortable using it, which materially boosts the probability Truist management will say it again when similar questions arise this week.

That’s the free look. The full scorecard — including our highest-conviction position with, all eleven keyword analyses, and the complete analyst-by-keyword exposure map — is below for subscribers.

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On March 16, the CFTC published an Advance Notice of Proposed Rulemaking on prediction markets (RIN 3038-AF65).

The comment period is open. Any member of the public can submit a comment on the federal record.

Sadly, most comments are boiler plate blogs of text about how Prediction Markets are bad, evil, and scary. They’re devoid of any rational argument, and are rooted in feelings not fact; and there are dozens of copy and pasted comments like the one below. They use scary terms like terrorism, and war; in an attempt to sell a narrative that Kalshi traders are running around betting on coups and bloodshed.

Except, they aren’t.

They can’t.

Because these type of contracts are already prohibited under the law.

In an attempt to provide some more color beyond individuals spamming the public record with copy pasta. Today, we submitted a formal public comment raising five issues we believe no other commenter has raised in the docket. The full comment is linked at the end of the article. This is the short version

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What we propose

1. A prediction market Self Regulatory Organization (SRO.)

Every other regulated asset class in the U.S. has at least one self-regulatory organization between the exchange and the federal regulator. Equities have FINRA. Municipal bonds have the MSRB. Prediction markets have nothing. The NFA has no jurisdiction over exchanges — only over intermediaries like FCMs

The CFTC is supervising this entire asset class alone, directly, with no buffer. We proposed a structurally independent PM-SRO, modeled on the precedents Congress set when it created the MSRB in 1975 and consolidated FINRA in 2007. A prediction market only SRO would allow disputes to be resolved outside of Kalshi’s current format of dispute resolution. We believe this would stand to benefit traders greatly.

And a point we think is one of the most important:

The current professional exam which governs event contracts (Series 3) contains zero questions about event contracts.

Event contracts are very nuanced, settlement mechanics, listing, disputes and other variables can be confusing and highly technical. A registered commodity professional can pass the Series 3 and know nothing testable about the fastest-growing product class on CFTC-regulated exchanges. So we proposed a dedicated “Series PM” competency exam.

This is like if the bar exam covered every area of law except criminal defense, but passing it still licensed you to represent murder defendants. The courthouses are full. The caseload is exploding. And every public defender walking in the door passed a test that never once asked them what “beyond a reasonable doubt” means.

2. Prohibit branding between registered and unregistered platforms.

At least one operator runs a CFTC-registered domestic platform and an unregulated offshore platform under the same core brand name. In early 2026, a contract on the death or removal of the Supreme Leader of Iran resolved differently across these two platforms. Same brand. Different outcomes. The domestic platform cancelled the contract. The offshore platform paid out. We proposed mandatory branding separation, point-of-sale disclosure, and a prohibition on cross-platform promotion of unregistered affiliates.

Dual branding of regulated + unregulated platforms confuses the general public, especially with markets relating to events which are banned under the CEA.

3. Mandatory algorithmic trading disclosure.

The whole policy argument for prediction markets is that prices reflect distributed human judgment. That argument falls apart if an undisclosed share of volume comes from bots running arbitrage and market-making strategies. We proposed a framework modeled on the CFTC’s own previously proposed Regulation AT: registration of automated trading systems, monthly publication of aggregate automated-volume statistics, and strategy-category disclosure. No proprietary parameters exposed. Just enough transparency to know whether the “wisdom of crowds” is actually a crowd.

4. Standardized public API and data reporting.

Prediction market prices show up in news broadcasts, policy briefings, and intelligence assessments. No two registered platforms publish data in the same format. You can’t do cross-platform surveillance if every exchange speaks a different data language. We proposed a standardized public API, five-year historical archives, and daily automated surveillance reports — analogous to the Consolidated Audit Trail for equities. If this data is going to be treated as public information infrastructure, it should meet a public information standard.

Why we filed

First Strike Research is a financial research operation. We trade prediction markets. We study them. We’ve built tools to analyze them. We have a direct commercial interest in these markets functioning with integrity, and a direct analytical interest in their data being accessible and standardized.

We also think the CFTC is at an inflection point. The Commission asked the public for input. Most of the comments in this docket, from what we’ve seen, argue about whether prediction markets should exist or how wide the product scope should be. Those are fine questions. But the plumbing questions — who watches the exchanges, what data standards apply, how do you tell bots from humans in the order book — are where the actual regulatory failures are happening right now. That’s what our comment addresses.

Read the full comment

Our complete comment, in PDF Format, with legal citations and proposed remedies for each section, is linked here.

First Strike Research

outreach@firststrike.trading

First Strike Research is not registered as a Commodity Trading Advisor (CTA) with the NFA or CFTC. Past performance figures cited in this article, including win rates, directional accuracy percentages, and P&L results, are historical and are not necessarily indicative of future results. Hypothetical or simulated performance results have inherent limitations and do not represent actual trading by any specific subscriber. Individual results will vary. Subscriber testimonials reflect individual experiences and are not a guarantee of future performance. Prediction market trading involves substantial risk of loss, including the total loss of invested capital.

Our newest model just went 8-for-10 on traded positions across Nike’s Q3 FY2026 earnings call Kalshi keyword contracts, that’s an 80% win rate on traded positions, and 88% directional accuracy across all 16 contracts analyzed

And the part we’re most proud of?

Our two highest-conviction trades both hit. If you would’ve traded this alone you would’ve netted a 200% ROI.

These were positions where our ML probability model and our social research overlay converged and were in full agreement with the highest possible conviction score.

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But don’t just take my word for it, our paid subscribers took down some handsome profit—

The Three-Layer System Is Getting Sharper Every Cycle

We’ve now run our new architecture through two complete earnings cycles — CrowdStrike and Nike — and the trajectory tells a clear story.

CrowdStrike: 11/13 directional (85%)

Nike: 14/16 directional (88%), +208¢ net P&L

That’s not a fluke. That’s a system learning and improving in real time.

Layer 1 — The ML Model generates raw probability estimates based on historical transcript data and linguistic patterns. It sets the baseline. On Nike, the raw model alone tracked around 10 out of 16 — decent, but not where we want to be. Its strength is consistency: it processes every keyword through the same statistical lens and catches patterns that gut instinct misses.

Layer 2 — The Context Classifier overlays narrative lifecycle analysis. Is a term rising in management’s vocabulary or fading out? This layer caught that Consumer Demand and Amazon were losing narrative momentum even though the model’s base rates still liked them. It correctly flagged Express Lane and Nike Factory as terms drifting out of active rotation. Layer 2 brought us from roughly 10 to 12 out of 16.

Layer 3 — The Social Research Overlay is where the edge lives. Deep research into transcript history, brand strategy signals, analyst commentary, media coverage, and social sentiment catches the event-driven shifts that data alone can’t anticipate. One of our biggest social research win was with Starbuck’s Condiment Bar.

Three critical interventions on Nike show why this layer changes everything:

The Tariff flip saved 97¢. Our model had this as a BUY NO at 63¢. The market had it at 97¢. Our research identified that in the current macro environment, Nike management was almost certainly going to address tariffs head-on. We flipped the position. That single research-driven call was worth nearly a dollar per contract.

The Retailer flip earned 67¢. Research showed wholesale distribution dynamics had become a hot topic in the analyst community — something the model’s historical data underweighted. Our social and media overlay caught the shift.

The Dividend risk flag limited a loss. We identified the April 1 pay date and Nike’s stock sitting near a five-year low as catalysts that could force the word into Q&A. We sized down accordingly. The loss came in at -43¢ instead of what could have been significantly worse.

A Tale of Two Misses

We’re celebrating the wins, but it’s important to remember that no model, formula, or system will always be 100% accurate.

Holiday (-81¢) was our biggest miss - Nike’s Q2 call was the holiday quarter — management had already spent extensive airtime on seasonal performance. By Q3, they’d moved on. The model liked Holiday based on historical Q3 base rates, but the word was temporally anchored to the prior quarter. In the future, we’ll more likely than not advise NO ACTION or ‘NO’ contracts.

Dividend (-43¢) hurts less because our research actually identified the risk. We sized down instead of flipping to a full PASS or even YES. Going with the gut here, YES contracts would’ve been more appropriate given existential share price retreat.

The Scoreboard Speaks for Itself

Two earnings cycles with the new model are now in the books, and here’s what the data says about this system:

Improving accuracy — 85% to 88%. Positive and growing P&L. Highest-conviction picks landing at a perfect clip. And every miss traceable to a fixable process gap rather than a fundamental flaw in the methodology.

The ML model provides the statistical backbone. The context classifier reads the narrative landscape. The social research overlay captures the real-world signals that move markets. When all three layers agree, we haven’t been wrong yet.

First Strike Research publishes analytical commentary on prediction market contracts. This content is for informational purposes only and does not constitute a trading recommendation, financial advice, or an offer to buy or sell any contract. First Strike Research is not registered as a Commodity Trading Advisor (CTA) with the National Futures Association (NFA) or the CFTC. Prediction market contracts on platforms such as Kalshi may constitute commodity interests regulated under the Commodity Exchange Act. Trading event contracts involves substantial risk of loss, including the total loss of invested capital. Past model performance (e.g., 10/13 on CrowdStrike) is not indicative of future results. The author may hold positions in the contracts discussed above at the time of publication. Nothing in this publication should be construed as a recommendation to enter into any specific trade.

Terms of Service

Prediction markets for earnings call keywords are an interesting corner of the rapidly expanding prediction market landscape. Most traders anchor to historical base rates, and nothing else. If you look at the comments section of any Kalshi contract most traders are only going back 1 quarter.

We’re attempting to use as much technology as possible and find and deliver to you advanced insight, and we’ve invested significantly into training and tailoring our model including using Claude’s-API to read around tradeable words to understand the given context and score the odds accordingly.

Our new model went 10/13 on the CrowdStrike call.

This is our first post sharing our new fair value model on Nike’s Q3 FY2026 call which is Tuesday evening. We’ve run every strike in our model against current intelligence: recent earnings transcripts, analyst previews, press releases, product launches, and macro developments from the last 90 days.

We’ll start with the 2nd highest conviction trade free for all subscribers. Our highest conviction play, plus the full scorecard on every remaining strike is available ONLY to our paid subscribers.

2nd Highest Conviction Trade: EXPRESS LANE

Our Model: 47¢ | Market: 66¢ | Our Position: NO

This is a 19-cent edge on a contract we have high conviction the market is overpricing. Here’s the full reasoning.

What Is “Express Lane”?

Express Lane was Nike’s mid-2010s manufacturing innovation program — a supply chain acceleration framework designed to cut product time-to-market from 18 months down to a matter of weeks. Under CEO Mark Parker, it was a centerpiece of Nike’s innovation narrative and a recurring fixture in earnings call language.

It was genuinely exciting at the time. It signaled agility, responsiveness to consumer trends, and operational sophistication. Management mentioned it repeatedly. Analysts asked about it. It had a real moment.

Express Lane’s time in the spot light has come and gone.

The Graveyard

Corporate communication follows predictable lifecycle patterns. Keywords get introduced, peak, plateau, and then — when the strategy they represent is superseded — they get retired. Not with an announcement. Just quietly, by omission. Management stops reaching for the phrase. Analysts stop referencing it. It fades.

Express Lane is deep in this fade cycle. The phrase does not appear in Nike’s Q2 FY2026 earnings transcript. It does not appear in analyst preview notes for Q3. It is not referenced in any Nike press release or investor communication we can find in the last 12 months. The current turnaround vocabulary — “Win Now,” “Sport Offense,” wholesale partner relationships, brand heat — has completely displaced it.

More importantly: the person who built Express Lane isn’t running Nike anymore. Elliott Hill took over in October 2024. His operational framework is entirely different. He’s not going to reach back to a Mark Parker-era initiative to describe what his supply chain team is doing.

What The Market Is Missing

The 66¢ pricing implies a roughly 2-in-3 chance that management uses this specific phrase on Monday’s call. We think that’s dramatically wrong.

When we search for any evidence of Express Lane appearing in Nike’s recent communications — investor presentations, newsroom releases, leadership interviews — we find nothing current. The most recent substantive coverage of the program dates to mid-2010s trade press.

For a word to appear at 66¢ probability, it should be woven into the current narrative. It should be in analyst notes as something to watch. It should be part of the company’s active vocabulary. Express Lane is none of those things.

Our fair value estimate: For YES is at ~20-25¢. The market has YES priced at 66¢ represents roughly a 40-45¢ overpricing. Our NO position at 47¢ captures a real but conservative slice of that edge — leaving room for uncertainty and the small chance an analyst asks a legacy question that pulls the phrase out.

Sources for Express Lane analysis: Nike Q2 FY2026 earnings transcript (Dec 18, 2025); Nike newsroom press releases (Q1-Q3 FY2026 window); analyst Q3 preview notes from BofA, UBS, Seeking Alpha, Benzinga; Nike investor relations presentations; FashionNetwork archival coverage of the Express Lane program (2016-2017).

PAID SUBSCRIBER ONLY CONTENT BELOW

What follows: our highest conviction trade of the entire board (with a 13-18¢ edge we think is being left on the table), plus our full intelligence overlay on Tariff, Supply Chain, Holiday, Sabrina, Dividend, Nike Factory, Retailer, Consumer Demand, China, Sustainable, Direct to Consumer, Amazon, and Digital Sales. Every keyword, every data point, every recommendation.

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Coming off a whiff on our recent high conviction play, we went back and rekeyed our machine learning model; we added AI-Enhanced abilities that provide context to the actual words spoken and the new results are simply impressive.

CrowdStrike reported earnings on March 3rd. Kalshi listed 13 binary contracts on whether specific words would be mentioned during the call. Accept our apologies, as with the new model weights we were not entirely comfortable releasing our predictions to readers before we could validate the new system.

The model nailed 11 out of 13 directional calls. Every high-conviction bet was correct. Every fade was right. The only misses were two event-driven surprises that no historical model could have predicted and we caught one of those with a manual research overlay before the market opened.

Here’s exactly how we’ve improved our model, which we will be utilizing to provide fair values models on contracts to our readers going forward.

The Problem With How People Trade Mention Contracts

Most traders on Kalshi approach earnings mention contracts the same way: they pull up the last transcript, ctrl-F the keyword, and make a gut call. Maybe they check two or three transcripts if they’re diligent. The market price reflects this — a loose consensus of people eyeballing recent history.

That approach has two blind spots. First, it doesn’t weight recency properly. A keyword mentioned 8 of the last 8 quarters feels like a lock, but if 6 of those mentions were three years ago and only 2 were recent, the probability is very different than if all 8 were consecutive. Second, and more importantly, it treats all mentions equally. “We are investing heavily in our consolidation platform” and “as we’ve moved past the outage incident” are both keyword hits. But the first one is a CEO describing core strategy. The second is a CFO giving a one-sentence brush-off to an analyst’s question. One predicts future mentions. The other predicts silence.

We’ve made a tenfold technical improvement to our model, and the new and improved system moves to addresses both problems.

The Results

Our model correctly forecasted the following:

Correct YES calls (6/6):

• Falcon Flex at 95¢

• Consolidation at 93¢

• AWS at 75¢

• Hyperscaler at 73¢

• Nvidia at 68¢

• Acquisition at 60¢.

Correct NO calls (5/5):

• Outage at 45¢

• Generative AI at 35¢

• China at 28¢

• Hack at 12¢

• Shadow at 40¢

The two INCORRECT calls:

Both Microsoft and Signal model forecasts were incorrect, however, upon human review we more likely than not would’ve published these strikes as “No-Action” (e.g., do not trade it.)

The full technical breakdown of our new two-layer model, including how the LLM context classifier works, the seven adjustment signals, and the complete strike-by-strike classifications. The technical details are available below for paid subscribers.

Going forward, this is the system we’re using to generate fair value estimates on every Kalshi earnings mention contract. Paid subscribers receive our probability tables and trade signals before the market opens. Free subscribers will receive analysis on one or two keywords we decide to publish publicly.

We went 11/13 on CrowdStrike; and the next cycle is already loading.

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Paid subscribers, once more thank you for your patronage. Your subscription directly supports the financial obligations required for our machine learning system (API calls, LLM Tokens, etc.) In step with our commitment to continue providing a strong value proposition, please enjoy reading about how we’ve improved the model to help support your trades. And, in the interest of your time the following is very technical, if you’re interested in how the model you pay for actually works, read on!

How the Model Works: Two Layers

Layer 1: The Frequency Model

We trained a random forest classifier on 3,144 historical earnings call outcomes across seven public companies: CrowdStrike, Google, Meta, AMD, Costco, Tesla, and Goldman Sachs. We are actively training the model on more relevant earnings calls, and companies. The current training data spans roughly 12 years of transcripts.

For each company and keyword pair, we extracted 43 features from the historical record. The model learned which patterns predict a YES and which predict a NO. It achieved 80.4% accuracy in cross-validation with an AUC of 0.881 — meaning it correctly ranks higher-probability keywords above lower-probability ones 88% of the time.

We tested calibration across every probability bucket. When the model says there’s an 80% chance, the actual hit rate is approximately 90%. When it says 20%, the hit rate is approximately 10%. The probabilities are trustworthy at face value.

This layer alone is better than gut feel. But it has a ceiling. It counts mentions without understanding them. A keyword with a six-quarter hit streak gets a high probability regardless of whether those mentions were enthusiastic or dismissive.

Layer 2: The LLM Context Classifier

This is where we’ve improved the model after our miss on the Dominos call.

Any two-bit high school math student can probably tell you just counting the frequency of an event does not accurately showcase the context of such an event.

To account for this every keyword/strike in the last four to eight quarters of transcripts, we extract the surrounding passage — roughly 200 words of context — and send it to an LLM. The LLM reads the actual language and classifies each mention into one of four categories:

Structural — the keyword is part of the company’s core narrative. Management brings it up unprompted as a strategic priority. These mentions persist because they’re embedded in how the company explains itself. If a strike is assigned structural they’re considered a very high conviction YES.

Promotional — management is actively pushing a product name or initiative. They’re selling the story. These mentions persist as long as the product is new and growing, but they have a shelf life. In hindsight, Craveable would’ve been promotional and if we had our new model at the time our confidence score would’ve been adjusted accordingly.

Reactive — management is responding to an analyst question or external event. They didn’t choose to say this word. These mentions are fragile — once the catalyst passes, they disappear. A good example of reactive would be 'SNAP - Family Center’

Legacy — a backward-looking reference. The keyword appears while management discusses transitions or history. The word is in the transcript, but the conversation has moved on.

Each mention also receives an intensity score from 1 to 3. A passing reference scores 1. A full paragraph scores 2. A major topic that dominates a section of the call scores 3.

The distribution of these categories — and how that distribution shifts over time — reveals whether a keyword’s frequency is sustainable or an illusion.

The Killer App: Outage

This strike is the best demonstration of why the context layer exists.

The frequency model said 85%. CrowdStrike had mentioned “outage” in six consecutive quarters following the July 2024 incident. The base rate was high, the streak was long, and the recency score was strong. Any trader looking at the numbers alone would price this above 80¢.

The context classifier told a different story. Across 12 classified mentions in the four most recent quarters, 92% were reactive and 8% were legacy. Zero structural. Zero promotional. Average intensity was 1.0 — the minimum possible. Every single mention was an analyst asking a question and management giving a one-sentence answer. Nobody was choosing to talk about the outage. They were being asked about it, and the answers were getting shorter.

The volume trend confirmed the fade: four mentions in Q1, five in Q2, down to just one in Q3.

The context-adjusted fair value was 45¢. The market had already priced it at similar levels. Outage did not hit on the March 3 call. The frequency model would have lost money buying YES at any price above 45¢. The context layer prevented that mistake.

Performance by Conviction Tier

The model’s accuracy correlated directly with conviction level:

Locks above 90¢ — two trades, both correct. Falcon Flex and Consolidation were identified as near-certainties based on structural keyword profiles. Both hit.

Strong YES between 60-89¢ — four trades, all correct. AWS, Hyperscaler, Nvidia, and Acquisition were identified as likely mentions with varying degrees of promotional vs. structural support. All hit.

Speculative zone between 30-50¢ — three strikes in this range (Microsoft, Shadow, Outage). Shadow and Outage were correctly called. Microsoft was the miss.

Strong NO below 30¢ — four trades, three correct. China, Generative AI, Signal, and Hack. Signal was the miss.

The pattern is clear: when frequency and context agree, the model is nearly perfect. When they diverge or when the probability falls into the speculative middle, variance increases. The takeaway for trading is straightforward — size your bets by conviction tier.

What We Got Wrong and Why

Microsoft was priced at 30¢ in our model. It hit. The context classifier correctly identified that Microsoft mentions had been declining for quarters — from 5 mentions per call down to zero for two consecutive quarters, with 100% promotional classification. The competitive win narrative against Microsoft had run its course.

What neither layer could see was that on February 18, CrowdStrike and Microsoft announced a major Azure Marketplace partnership. This injected a completely new narrative — not competition, but collaboration — that management was eager to discuss on the call. Our model saw a dead keyword. Reality saw a resurrected one.

If we would’ve published a CrowdStrike call analysis based on the new model, we would’ve taken into consideration this partnership and would’ve advised ‘no-action.’

Signal was priced at 20¢. It hit. The model saw scattered mentions with no pattern across eight quarters. What it couldn’t know was that CrowdStrike’s $740 million acquisition of SGNL in January 2026 turned “signal” from a generic word into a product-specific term embedded in their identity security pitch.

Both misses share the same root cause: discrete corporate events that fundamentally change a keyword’s relationship to the business. These are not predictable from historical transcripts. They require a real-time news and event monitoring layer, which is we would’ve advised ‘no-action’ or a potential lean towards yes.

Complete Technical Architecture

Training Data Construction

The frequency model was trained on a dataset of 3,144 historical quarter-keyword outcomes. We began by collecting earnings call transcripts for seven companies (CRWD, GOOGL, META, AMD, COST, TSLA, GS) through a transcript API, caching results locally to avoid redundant calls.

For each company, we defined a basket of keywords matching actual Kalshi contract strikes. The full basket covered 102 unique ticker-keyword pairs across 93 distinct keywords. We filtered to 75 pairs in the 5-95% historical hit rate range — pairs outside that range provide no variance for the model to learn from. A keyword that appears 100% of the time or 0% of the time teaches the classifier nothing.

Each row in the training set represents one historical quarter for one keyword. The label is binary: did the keyword appear on the call (1) or not (0). All features are computed using only data available before the target quarter — no future leakage.

The 43 Features

The feature extractor computes features across nine groups:

Frequency features captures raw and windowed hit rates: overall hit rate, hits in the last 2, 4, and 8 quarters, and the rate over those windows. These are the baseline signals that any manual analysis approximates by eye.

Recency-weighted features apply exponential decay to historical outcomes. We compute three variants with decay factors of 0.75, 0.85, and 0.9, plus a piecewise weighting that gives the most recent quarter 4x the weight of quarters older than 8. The top-performing single feature in the model is the 0.75 exponential recency weight, which correctly captures rapid regime changes.

Streak features track whether the keyword is currently on a hit or miss streak, the length of that streak, and a binary flag for whether the most recent call included the keyword.

Trend features measure the slope of a rolling hit rate, whether the trend is monotonically increasing or decreasing, and the ratio of recent to historical mention density.

Call-level features include average call word count, mention density per 1,000 words when the keyword is present, the standard deviation of mention counts, and the maximum mentions observed in any single call. These capture whether a keyword is a passing reference or a major discussion topic.

Mention count features track the average and maximum mention counts when present, the coefficient of variation (how volatile the count is), and the correlation between call length and mention presence.

Speaker features were designed to capture whether mentions come from the CEO, CFO, or other executives, but the transcript formatting did not support reliable speaker attribution. These features remained at zero and were excluded from the model. This feature is largely dependent upon how the transcripts are published, and if they include the title of the speaker. We are actively working on improving this feature to add to the model.

After removing zero-variance features (8 sector/speaker columns) and filling 819 null values in the length-mention correlation feature, the final feature set contained 43 usable features.

LLM Context Classification

For each keyword mention in the transcript, we extract a 600-character window of surrounding text. This passage is sent to Claude’s API (Sonnet) with a classification prompt that asks for a category (structural, promotional, reactive, legacy) and an intensity score (1-3).

The quick scoring function scans the last 4 quarters by default, with an option to extend to 8 quarters for keywords with thin recent data. It aggregates classifications into a summary that includes category distribution percentages, average intensity, dominant category, and a narrative health assessment.

The Seven Adjustment Signals

The context-adjusted probability starts from the frequency model’s output and applies seven sequential adjustments:

Signal 1 — Recent silence. If the keyword had zero mentions in the most recent quarter, the probability is multiplied by 0.6. If zero mentions in the two most recent quarters, the probability is capped at 0.25. This was the single most impactful adjustment in backtesting.

Signal 2 — Volume trend (context-gated). We compare average mention count in the two most recent quarters against the two older quarters. If volume has declined by more than 70%, and the context shows legacy mentions above 15% or structural mentions below 20%, the probability is multiplied by 0.6. If volume declined by 40-70% under the same context conditions, multiplied by 0.8. If volume increased by 50% or more and current probability is below 70%, it is boosted by 30%. The context gate prevents false penalties on keywords like Falcon Flex, where raw mention volume declined from launch hype to steady-state but the narrative strengthened.

Signal 3 — Reactive dominance. If 70% or more of classified mentions are reactive, the probability is halved. If 50-70% are reactive, it is multiplied by 0.7. Reactive mentions depend on external catalysts that may not recur.

Signal 4 — Legacy migration. If 40% or more of mentions are legacy, multiply by 0.7. If 25-40% are legacy, multiply by 0.85. Legacy mentions indicate the keyword is being discussed in the past tense.

Signal 5 — Structural boost. If 70% or more of mentions are structural and average intensity is 1.5 or above, multiply by 1.1 (capped at 0.99). Structurally embedded keywords have the highest persistence.

Signal 6 — Thin data penalty. If total mentions across all scanned quarters are 2 or fewer and the probability exceeds 40%, multiply by 0.8. Small sample sizes inflate confidence.

Signal 7 — Intensity fade. If average intensity is 1.0 or below (minimum — passing mentions only) and the probability exceeds 50%, multiply by 0.8. Low-intensity mentions signal a keyword hanging on by a thread.

Strike-by-Strike Context Classifications

Falcon Flex — 20 mentions across 4 quarters. Category split: 45% structural, 55% promotional. Intensity: 2.1 (highest on the board). Quarter-by-quarter: Q4 FY2025 was 60% structural, shifting to 60% promotional by Q2 FY2026, then back to 60% structural in Q3 FY2026. This promotional-to-structural migration pattern indicates a product transitioning from launch narrative to permanent vocabulary. No adjustment applied — frequency model’s 96.9% stands. Fair value: 95¢.

Consolidation — 17 mentions across 4 quarters. Category: 88% structural, 12% reactive, zero legacy. All four quarters showed structural mentions. Q2 FY2026 was 100% structural across all 5 classified passages. Structural boost applied: 96.4% → 99%. Fair value: 93¢.

AWS — 11 mentions across 3 of 4 quarters (silent in Q2). Category: 64% promotional, 18% structural, 18% reactive. Most recent quarter was 100% promotional at intensity 2 — management actively namedropping the partnership. One silent quarter prevents a boost but the promotional energy is strong. No net adjustment. Fair value: 75¢.

Hyperscaler — 8 mentions across all 4 quarters. Category: 50% structural, 25% promotional, 12% legacy, 12% reactive. The structural foundation represents real revenue from hyperscaler customers. One legacy mention in Q2 didn’t repeat. No net adjustment. Fair value: 73¢.

Nvidia — 7 mentions across 3 of 4 quarters. Category: 100% promotional, intensity 1.9. CrowdStrike promoting the Nvidia AI security partnership. No structural anchor means this could vanish if management finds a new partnership to highlight, but recent momentum is strong. No net adjustment. Fair value: 68¢.

Acquisition — 14 mentions across 3 of 4 quarters (silent in Q1). Category: 29% structural, 29% promotional, 7% reactive, 36% legacy — the highest legacy rate on the board. Legacy penalty applied: 80.2% → 68.2%. The keyword is transitioning from active narrative to historical references. Fair value: 60¢.

Outage — 12 mentions across all 4 quarters. Category: 92% reactive, 8% legacy, zero structural, zero promotional. Intensity: 1.0 (minimum). Volume declining from 4-5 mentions to 1. Reactive penalty and intensity fade applied: 84.8% → 42.4%. This was the model’s signature call — the frequency model was dangerously wrong, and the context layer corrected it. Fair value: 45¢.

Shadow — 2 mentions in 2 most recent quarters (emerging). Category: 50% structural, 50% promotional, intensity 2.0. New product or capability entering the vocabulary. Too thin for confidence in either direction. No adjustment from thin base. Fair value: 40¢.

Generative AI — 6 mentions across 3 of 4 quarters, with the most recent quarter silent. Category: 50% structural, 17% promotional, 33% reactive. The silence penalty fired: 51.8% → 31.1%. Management absorbed the concept into “agentic AI” without using the specific Kalshi strike phrase. Fair value: 35¢.

Microsoft — 2 mentions across 2 of 4 quarters, with 2 consecutive quarters of silence. Category: 100% promotional, zero structural. Double silence penalty applied: 57.5% → 15%. The competitive win narrative had clearly run its course by Q2 FY2026. Fair value: 30¢.

China — 2 mentions across 2 of 4 quarters. Category: 100% structural, intensity 2.0. Both mentions related to nation-state threat landscape. Structural quality is high but frequency is roughly annual. Small structural boost partially offset by thin data. Fair value: 28¢.

Signal — 1 mention across 4 quarters, with the most recent quarter silent. Category: 100% promotional, intensity 1.0. Silence penalty and thin data penalty applied: 30.8% → 18.5%. Scattered appearances with no clustering or trend. Fair value: 20¢.

Hack — 1 mention across 4 quarters (only Q3 FY2026). Category: 100% structural, intensity 2.0. CrowdStrike is a cybersecurity company but actively avoids the word “hack” in favor of sanitized terms like “threat actor” and “adversary.” 48 quarters of history show near-zero usage. Thin data keeps it flat. Fair value: 12¢.

First Strike Research publishes data-driven analysis of prediction markets, short equity research, and investigative financial journalism.

Not investment advice. Trade at your own risk.

Our analysis examines why keyword frequency models — including systematic quantitative approaches — may be dramatically underestimating this particular contract.

The Surface-Level Pattern

First Strike Research maintains API access to every single public-facing company earnings call transcript, and our machine learning system poured over the data:

Across Domino’s earnings calls from 2018–2024, “craveable” received zero mentions — a complete absence spanning at least six years of quarterly calls.

The phrase then appeared for the first time on the Q3 2025 call in October 2025.

A basic recency model, seeing a single-quarter appearance after years of zeros, interprets this as a marginal or one-off usage — something to discount rather than extrapolate.

Current Kalshi YES pricing sits around 37¢, implying the market believes “craveable” is unlikely to appear on the upcoming Q4 call. We believe this to be false — and the magnitude of the mispricing is the largest we have identified in our keyword contract research to date.

Why This Matters Beyond One Trade

Our model, trained on historical mention frequencies, cannot easily detect when a word transitions from “absent from corporate vocabulary” to “the company’s new defining brand adjective.” The model notes a single appearance after years of nothing and assumes mean reversion back toward zero.

So in turn we layer qualitative analysis — event timing, social sentiment, brand strategy context, and management communication patterns — on top of our statistical framework.

First Strike’s proprietary machine learning system has the probability for “Craveable” being said at:

68.4% — YES

Bear in mind — the above is solely for raw transcript data (no social analysis).

A 31 percentage point over-discount applied to the contract on the numbers alone. When we drill into the brand context and social sentiment, we’ve found the true odds of Domino’s saying “craveable” even higher. This is based on the structural brand tailwinds and business trends we’ve found through our research.

This deep research is provided below to our paid subscribers.

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DISCLOSURE:

The author of this article holds the following position:

“CRAVEABLE” — YES

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It was a volatile market based in part on relatively low liquidity. We had a final dollar cost average of ~50 cents a “NO” contract. As we accurately predicted, Snapchat Family Center was NOT mentioned on SNAP’s earning call.

A couple considerations on why:

• The news-cycle came and went, SNAP previously announced their improvements to the Family Center on the call prior.

• The improvements were primarily focused around legal mitigations after a lawsuit. This is generally an ugly topic, and a topic management doesn’t want to bring back up once it’s been put to bed.

• SNAP was more focused on pushing the growth narrative.

• After a strong earnings beat, analysts are even less likely to waste their one question to ask about a lawsuit that has already been settled out and “priced-in” to the equity shares. If Family Center wasn’t mentioned in the prepared remarks the odds of them saying it in the Q&A are further reduced (virtually to zero.)

• This presents an appealing live trade opportunity which is why you should be dialed into all earning calls you have a position on.

  ---

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Looking forward at company earnings mention contracts

A pattern we’ve noticed is if a company has a top/bottom line miss, they are far more likely to heavily stress speaking about financial metrics in their prepared remarks. Even more so if the stock is aggressively selling off.

We believe this adds more weight to “NO” contracts around ‘fluff’ some recent examples:

• Cane Sugar “NO” - not said on the Coke call after a sales miss.

• Retro “NO” - also not said on the Coke call after a sales miss.

Given that earnings reports release roughly 30-60 minutes before the call this is basically a live trade. We will not be able to bake in this metric to our machine learning model. To avoid spamming your inbox before every earnings call if we spot something we’ll post it to either Substack Notes or our Twitter / X.

We’ve made improvements to our machine learning system

Our next market write up will include the new weights we’ve added to our machine learning algo. We’ve added overall call length to assign more weight to the YES side of trades taking into account longly winded CEOs like Elon Musk who are apt to ramble and subsequently hit more Ys

Limit Orders are Your Friend

Are you trading? Or are you gambling? You should strongly consider setting limit sells at a comfortable price target. Buying a 20c contract and selling back at 40c is a solid trade and represents a 100% percent ROI. Dumb money hits market orders, and rides out until resolution. Real traders close out their positions with a healthy ROI and avoid resolution risk. You can either trade outcomes, or probability flow. While you can be profitable in both, trading the waves of probability will make you more money in the long run.

Also be sure to check out Bernard B’s:

Bernard B.

Prediction Market Dictionary

This page is an attempt to catalog and define the rapidly evolving vernacular used in prediction markets such as Kalshi and Polymarket. Readers on desktop can use the table of contents on the left for ease of navigation…

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3 months ago · 6 likes · 3 comments · Bernard B.

Up next

By the end of the week be prepared for another Kalshi earnings mention contract our machine learning model shows to be mispriced. Our next write up will include the added calculation of average call runtime. Paid subscribers will get access to additional social sentiment and weight added to the calculation based on the questions we predict wall street analysts will ask on the call.

Kalshi traders currently have the odds of “Family Center” being said on the upcoming Snap Inc. earnings call at an 87% chance. We believe these odds are drastically overstated. We utilized our machine learning system and social sentiment analysis to provide First Strike readers more accurate odds for this market.

The market is here

Note: The earnings call is TOMORROW, Kalshi added this market almost last minute.

Disclaimer: This is research supporting a financial derivative, and governed by our terms of service.

The pattern

First Strike Research maintains API access to every single public-facing company’s earnings call transcripts, and our machine learning system pored over the data:

• Across 35 Snap earnings calls (2014–2025), “family center” received zero mentions for approximately 10 quarters after the product launched in mid-2022

• The phrase then appeared in one call—the FY2024 Q4 call on February 4, 2025

• Current Kalshi YES pricing sits around 87¢, implying an extraordinarily high probability that “family center” has become a fixture of Snap’s quarterly script

We believe this to be false (at least going forward).

The numbers

First Strike’s proprietary machine learning system has the probability for “Family Center” being said at:

9.2% — YES

Obviously, our model which is trained on historical mention frequencies cannot easily detect when a talking point has transitioned from “product launch announcement” to “quietly embedded feature that no longer warrants airtime.” The model notes a recent mention and extrapolates continued discussion.

Bear in mind—the above is solely for raw transcript data (no social analysis).

9.2% YES which equates to a roughly 78-point overconfidence premium applied to the contract on the numbers alone. When we drill into the social sentiment and regulatory context, we’ve found the street-adjusted odds sit around 15–20%—still dramatically below the market’s 87%.

This is based on analyst silence, peer company behavior, management communication patterns, and event sequencing we’ve found through our research.

Below, paid subscribers get the full breakdown why the January announcement is a pricing trap, what Snap's Q3 call and Meta's Q4 call reveal about management's actual language habits, where the analyst community is focused (hint: not here), and our complete risk scenario analysis. On the fence? You can read about our previous success here:

DISCLOSURE:

The author of this article holds the following position:

SNAP MENTION “FAMILY CENTER” — NO

THIS ARTICLE WAS ORIGINALLY PAY WALLED FROM HERE DOWN - SINCE THE EVENT HAS CONCLUDED WE HAVE REMOVED THE PAYWALL.

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Historical Silence

To understand why this keyword is overpriced, it helps to examine what “family center” actually is and why its mention history is so remarkably thin.

Family Center is Snapchat’s in-app parental tools suite, launched in mid-2022. It allows parents to see who their teen is communicating with, set content controls, and monitor usage. Despite existing for over three years and spanning 14+ quarterly earnings calls, management has mentioned it exactly once.

That single mention came on the FY2024 Q4 call (February 4, 2025): “We also announced new location sharing features in Family Center...” — a passing reference nested within a product update, not a standalone talking point.

A 2.9% base rate across 35 calls is not a pattern that supports 87% pricing. It’s a pattern that supports single-digit probability, with room for qualitative adjustment.

The January Catalyst

The Kalshi market’s 87% price almost certainly stems from two events in late January 2026:

January 20, 2026: Snap settled the K.G.M. addiction lawsuit, avoiding trial and CEO Evan Spiegel’s testimony. The settlement terms were minimal—Snap’s public statement said only that they were “pleased to resolve amicably.”

January 22, 2026: Two days later, Snap announced the most significant expansion of Family Center in its history—new time-spent insights for parents, friend trust signals, and AI-powered parental controls. The announcement received major press coverage from The Hill, TechCrunch, Mashable, and IT Brief UK.

For retail Kalshi traders, the logic is obvious: Big product announcement 13 days before earnings = definitely gets mentioned on the call.

But this logic doesn’t survive contact with the data.

The Q3 2025 Precedent

The cleanest comparable is the Q3 2025 earnings call from November 5, 2025—just three months ago.

During that call, Snap faced active regulatory pressure. Australia had just passed its social media age verification law. Snap discussed regulatory impacts and age verification on the call.

“Family center” was mentioned zero times.

Management discussed safety. They discussed regulatory compliance. They discussed parental tools in generic terms. But they did not use the specific phrase “family center”—which is the only thing that matters for contract resolution.

This is the single most important data point in the analysis. When Snap had every reason to mention Family Center by name on the Q3 call—active regulation, safety discussion, product relevance—they chose not to. The contract resolves on the exact phrase, and Snap’s management defaults to discussing safety without naming the specific product.

Peer Confirmation: Meta’s Silence

Meta reported Q4 2025 earnings on January 28, 2026—six days before the Snap call. Meta owns “Teen Accounts,” a directly comparable parental controls feature.

Despite facing the same regulatory environment, the same Congressional pressure on child safety, and the same news cycle around teen social media usage, Meta made zero mentions of parental controls or teen safety in either prepared remarks or Q&A.

This suggests a sector-wide pattern: social media companies are not volunteering safety feature discussions on earnings calls unless directly forced by analyst questions. The earnings call is for financial results—revenue, DAUs, margins, guidance. Product safety features are PR and blog post material.

The Analyst Silence

We reviewed 8+ sell-side analyst previews for the Snap Q4 2025 call across Yahoo Finance, Zacks, MarketBeat, and other sources.

Zero previews mention Family Center, teen safety, parental controls, or child safety regulation as an expected topic for the call.

The analyst community is focused on:

• Snapchat+ subscriber trajectory and monetization

• The Perplexity AI partnership and its revenue contribution

• North America DAU growth and engagement trends

• 2026 guidance and margin trajectory

If the street thought “family center” was an 87% probability topic, at least one analyst preview would flag it. None did.

The Retail Anchoring Problem

The 87¢ YES price exhibits classic retail anchoring bias combined with thin-market inefficiency.

The Kalshi spread on this contract is 25¢ (YES at 87¢, NO at 38¢). A 25-cent spread signals extremely thin liquidity. In thin markets, a handful of uninformed retail traders can push the price far from fair value.

The likely chain of reasoning for YES buyers:

1. See headline: “Snap Expands Family Center with AI Parental Controls”

2. Think: “They just announced this—of course they’ll mention it on earnings”

3. Buy YES at 87¢

4. Never check: historical base rate (2.9%), Q3 precedent (zero mentions despite safety discussion), or whether product announcements typically translate to earnings call mentions (they often don’t)

We searched the Kalshi subreddit, StockTwits, prediction market forums, and fintwit. Zero discussion of this specific contract. The price appears entirely uninformed.

The Lawsuit Settlement Dynamic

The K.G.M. lawsuit settlement on January 20 creates a nuanced risk. On one hand, the settlement + Family Center announcement timing (2 days apart) suggests coordinated PR—Snap wanted to pair “we settled” with “here’s what we’re doing proactively.”

On the other hand, Snap’s settlement statement was deliberately minimal. Management typically does not volunteer lawsuit discussion on earnings calls unless the financial impact is material. Even if asked about safety, Snap can—and historically does—discuss “parental tools,” “age verification,” and “trust and safety investments” without using the specific phrase “family center.”

The Q3 call is proof: safety discussion occurred without the keyword. The contract doesn’t care about intent or topic. It cares about two specific words spoken together.

Forward Probability Assessment

Four factors push the realistic forward probability well below the Kalshi market price:

Historical base rate: 2.9% across 35 calls. The product has existed for 3.5 years and been mentioned once. This is not a fixture of Snap’s earnings script.

Q3 2025 precedent: Safety and regulatory discussion occurred without the phrase. Management defaults to generic safety language on earnings calls.

Analyst silence: Zero sell-side previews flag Family Center as an expected topic. The street is focused on advertising, DAUs, and guidance.

Peer behavior: Meta’s Q4 call omitted teen safety discussion entirely despite owning comparable features and facing identical regulatory pressure.

After adjusting for the January 22 announcement catalyst (which provides a modest probability lift), a reasonable forward probability for “family center” on the Q4 2025 earnings call sits in the 15–20% range.

Analytical Implications

At current NO pricing around 38¢, the implied market probability is approximately 87%. If the adjusted probability sits closer to 18%, this represents potential edge of approximately 67–70 percentage points.

At our model’s 9.2% estimate, the edge is even wider—though we believe the January announcement timing justifies a modest qualitative lift above the pure statistical output.

Expected value calculation at 18% fair value:

• NO costs 38¢, pays $1.00 if “family center” is not said

• EV = (0.82 × $0.62) − (0.18 × $0.38) = +$0.44 per contract (+115% ROI)

Several considerations warrant caution:

Q&A tail risk: Analyst questions are inherently unpredictable. A single inquiry about the lawsuit settlement could prompt management to pivot to Family Center as a defense. We estimate this at 5–8% probability.

Prepared remarks wildcard: If the IR team scripted a safety paragraph citing Family Center by name, the trade loses. This is the biggest unknown, estimated at 10–12% probability. Q3 precedent suggests they don’t script it, but it cannot be ruled out.

Informed flow: If the 87¢ price reflects someone with inside knowledge of prepared remarks (unlikely but possible), the analysis is wrong. Monitor for unusually large YES orders before the call.

Traders evaluating this thesis should size accordingly and recognize that even well-researched positions carry meaningful uncertainty.

Key Monitoring Points Before the Call

• Sell-side notes published February 3–4: If any analyst suddenly flags Family Center, probability jumps to 30–35%

• Unusual Kalshi order flow: Large YES buys ($10K+ single fills) could signal informed trading

• Snap IR communications: Any pre-call press release or investor letter mentioning Family Center would be a warning sign

If none of these appear, our NO trade thesis remains intact.

And—the simplest way to understand CrowdStrike’s competitive vulnerability requires absolutely no technical background. One of their biggest competitors: Microsoft has a cybersecurity product (Defender) for Endpoint built into their enterprise license suite at no additional cost. Approximately 60% of CrowdStrike’s Fortune 500 customer base already holds E5 licenses. So these organizations are, in practical terms, paying CrowdStrike a premium subscription for endpoint protection they already own through their existing Microsoft agreement.

By way of simple analogy this is like purchasing a home that comes with a free professionally monitored security system (Microsoft.) Only to then turn around and pay a different company (CrowdStrike) to install a second system on top of it. This is a redundancy that was once justified only by the market’s perception that Falcon was the categorically superior product.

Our research below will show CrowdStrike's reported 97% retention rate is structurally misleading and most outage-affected customers haven't reached renewal yet. We present evidence of a joint SEC/DOJ investigation, $500M+ in litigation surviving dismissal, and a contract renewal cliff the market has not priced.

In a follow-up article we will be presenting Open Source Intelligence (OSINT) collected by First Strike investigators which reveals CrowdStrike’s arrogant “cybersecurity Jesus” complex headlined by their social prestige and backstopped by a culture of catastrophic QA, pushy sales tactics, and a befuddled customer support network which is often regarded as painstakingly slow.

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CrowdStrike trades at 25.8x revenue with a 95x forward P/E we believe the market hasn't yet seen what happens when the contracts come due. On July 19, 2024, a single faulty content update crashed 8.5 million Windows systems in the largest IT outage in history. Airlines grounded 16,896 flights. Hospitals went dark. Fortune 500 companies absorbed $5.4 billion in losses. But because CrowdStrike locks enterprise customers into 1-3 year prepaid contracts with no termination for convenience, and the churn from that catastrophe has just begun to surface in reported financials. The first major renewal wave starts now. Meanwhile, the SEC and DOJ are jointly investigating the company's revenue recognition practices — a dual-agency probe that, outside of FCPA cases, is rare and typically signals potential criminal exposure.

At the helm is CEO George Kurtz, who is the only cybersecurity executive in history to preside over two global IT outages caused by faulty updates. He declined to testify before Congress about the incident; opting to send Adam Meyers, the company’s Senior Vice President of Counter Adversary Operation instead.

First Strike investigators also uncovered dozens of testimonies from former employees, customers, and adjacent cyber security professionals regarding the arrogant and reckless culture that is plaguingthe company.

• Premium valuation disconnected from decelerating fundamentals. CRWD trades at 25.8x P/S and 95.2x forward P/E — more than double peer averages — while revenue growth has decelerated from 36% to 22%, ARR growth has fallen from 35% to 23%, and net revenue retention has dropped to a record-low 112%. Free cash flow margin compressed from 31% to 27%, moving further from management’s 34-38% target.

• The July 2024 outage was the largest IT failure in history — and the financial impact is structurally delayed. A faulty update, negligently pushed by CrowdStrike caused $5.4 billion in estimated Fortune 500 losses and over $10 billion in total global economic damage. CrowdStrike’s 1-3 year contracts stipulate no termination for convenience which means tranches of customers have not yet reached renewal.

• Competitors are actively converting pipeline from the outage. Palo Alto Networks confirmed active discussions with companies seeking to switch providers. SentinelOne also reported record pipeline and a record number of competitive wins, while growing revenue at 32% way faster than CrowdStrike.

CrowdStrike’s market dominance hides cracks in the foundation

CrowdStrike commands an impressive 21-22% market share in endpoint protection, making it the category leader ahead of McAfee and Microsoft Defender.

The flagship Falcon platform is deployed across 60% of the Fortune 500 and 70% of the Fortune 100, this has afforded CrowdStrike a deep competitive moat through its cloud-native architecture and real-time threat intelligence. The company reached a $118 billion market capitalization and joined the S&P 500 in 2024—the fastest any pure-play cybersecurity firm has achieved that milestone.

However, we believe this growth is decelerating rapidly. Annual recurring revenue growth has fallen from 35% in Q3 FY2024 to 23% in Q4 FY2025, while revenue growth declined from 36% to 22% over the same period.

In our opinion, one of the most concerning problems with CrowdStrike is their net revenue retention rate. By way of context, NRR is one of the most important KPIs in SaaS as it shows whether a company’s customer base is growing more or less valuable overtime. This important metric demonstrates CrowdStrike’s ability (or lack-there-of) to upsell, cross sell, and avoid churn.

NRR at CrowdStrike—dropped to 112%, the lowest in company history, down from 119% just four quarters earlier. Net new ARR of $153 million in Q3 FY2025 represented a significant decline from the $223 million record set the prior year.

The valuation premium appears increasingly disconnected from these fundamentals. CrowdStrike trades at a forward P/E of 95.2x versus an industry average of 29.5x, and a P/S ratio 2.2 times higher than peer companies including Palo Alto Networks, SentinelOne, and Zscaler. Free cash flow margin compressed from 31% in FY2024 to 27% in FY2025, moving further from management’s stated target of 34-38%. GAAP operating margin swung from near-breakeven to negative 3%, generating a $120.4 million operating loss in FY2025 compared to prior-year profitability.

Competitive threats are intensifying from multiple directions. Microsoft’s Defender offering has captured 12.7% market share through aggressive bundling with E5 enterprise licenses—meaning many CrowdStrike customers already have unused Microsoft security capabilities they could consolidate to at no incremental cost. By way of simple analogy without technical nuance: this is like buying a house which includes a free security system (Microsoft) only to turn around and purchase a completely separate system.

Palo Alto Networks’ $25 billion CyberArk acquisition and “platformization” strategy directly targets CrowdStrike’s enterprise customer base.

SentinelOne is showing impressive growth at 32-33% annually faster than CrowdStrike all while achieving its first-ever profitable quarter while offering similar capabilities at lower valuations.

The July 2024 outage was unprecedented in scale and cause

At 04:09 UTC on July 19, 2024, CrowdStrike released a faulty Falcon sensor content update that crashed 8.5 million Microsoft Windows systems worldwide—the largest IT outage in the history of information technology.

The root cause was simple: CrowdStrike pushed an update with an additional data field that the Falcon sensor was not built to handle. By way of simple analogy CrowdStrike put diesel fuel in a gasoline car. This in turn crashed windows machine into endless boot loops.

To get technical: Channel File 291 provided 21 input fields when the Falcon sensor expected only 20, causing an out-of-bounds memory read that triggered Windows Blue Screen of Death errors and boot loops.

The Falcon sensor operates at the Windows kernel level with the highest system privileges. When it crashed, it took down entire operating systems. BitLocker-encrypted machines required unique 48-digit recovery keys for restoration, and all affected systems needed manual intervention—technicians booting into Safe Mode to delete a single faulty .sys file, one machine at a time.

The damage spread across every sector of the global economy. Airlines cancelled 16,896 flights within 72 hours.

Parametrix Insurance estimated $5.4 billion in Fortune 500 losses alone, with total global economic damage exceeding $10 billion. Delta Air Lines was hit hardest of all, cancelling 7,000+ flights, stranding 1.3 million passengers, and suffering $550 million in total losses ($380 million revenue, $170 million expenses).

CrowdStrike’s crisis response drew sharp criticism. CEO George Kurtz’s initial Twitter statement identified the problem but notably omitted any apology.

Their contract structure creates a delayed churn trap

The most critical element of our thesis may be what current financials cannot yet show. CrowdStrike requires upfront payment for the entire subscription term—typically 1-3 years—and explicitly does not allow termination for convenience. This contractual structure creates a significant lag between customer dissatisfaction and visible churn.

Enterprise customers locked into multi-year agreements signed before July 2024 cannot switch providers until their contracts expire. A three-year deal signed in Q3 2023 won’t reach renewal until Q3 2026—more than two years after the incident. Even one-year contracts signed in early 2024 are only now approaching their first renewal window. The Q1-Q3 2025 period represents the first major wave of post-incident contract renewals, meaning the true test of customer loyalty is only beginning as of February 2025.

SentinelOne’s CEO observed that “companies do not make snap decisions and need to figure out how to make the transition. Sales cycles typically last 9 to 12 months.”

Competitors report active customer interest. Palo Alto Networks’ CEO confirmed “a number of discussions with companies looking to change vendors,” while SentinelOne reported a “record pipeline” and “significant pipeline pickup” following the outage, later citing “record number of wins against closest competitor.” CrowdStrike acknowledged losing accounts in the “managed security services space” and has deployed “Customer Commitment Packages”—discounts, free products, and extended terms—that reduced quarterly revenue by $10-15 million while generating “longer-term deals” designed to lock customers in past the immediate churn risk window.

CrowdStrike reports a 97% gross retention rate, but this metric only measures customers who reached renewal—it cannot capture churn intent among the majority still locked into multi-year contracts. The true retention picture will only emerge over the next 18-24 months as the contract renewal wave progresses.

Legal and regulatory exposure extends beyond Delta

Delta Air Lines filed suit against CrowdStrike on October 25, 2024, in Fulton County Superior Court, Georgia (Case No. 24CV013621), seeking $500 million or more in damages plus punitive damages and legal fees. The complaint alleges gross negligence, breach of contract, computer trespass under Georgia law, and willful misconduct, claiming CrowdStrike “forced” untested updates through an “unauthorized door” within Microsoft’s operating system. Delta’s attorney David Boies argued CrowdStrike’s initial remediation assistance “simply referred Delta to CrowdStrike’s publicly available remediation website.”

CrowdStrike filed a countersuit the same day in federal court (N.D. Georgia, Case No. 24-cv-04904), seeking declaratory judgment that liability is capped at contract limits—which external counsel characterized as “single-digit millions”—and blaming Delta’s “antiquated IT infrastructure” for the extended recovery period. In May 2025, Judge Kelly Lee Ellerbe ruled that Delta’s claims may proceed, allowing gross negligence and computer trespass claims to advance while dismissing most fraud allegations. If Delta proves gross negligence or willful misconduct, standard contractual liability caps may be invalidated, exposing CrowdStrike to the full claimed damages.

The most significant legal development came in June 2025, when CrowdStrike disclosed an ongoing SEC and DOJ joint investigation into revenue recognition practices. The probe focuses on a $32 million Carahsoft-IRS transaction from 2023, structured as four $8 million payments with reportedly no products ever purchased. CrowdStrike later excluded approximately $26 million from ARR citing “distributor-transferability issues.” Investigators are interviewing former staff about potential “pre-booking” or “channel-stuffing” practices and reviewing Sarbanes-Oxley compliance questionnaires. Dual SEC/DOJ involvement outside FCPA cases is unusual and suggests potential criminal exposure. Disclosure Insight research flagged elevated restatement risk.

Additional proceedings include a dismissed passenger class action against CrowdStrike (now on appeal to the 5th Circuit) and an active passenger class action against Delta where some claims survived dismissal in May 2025. Insurers who paid first-party claims to affected customers are expected to pursue subrogation claims. Total insured losses from the incident are estimated at $400 million to $1.5 billion.

George Kurtz’s track record raises governance questions

CEO George Kurtz co-founded CrowdStrike in February 2012 after serving as McAfee’s Executive Vice President and Worldwide Chief Technology Officer. His tenure at McAfee included oversight of a strikingly similar incident: On April 21, 2010, McAfee released a faulty antivirus update that mistakenly flagged the critical Windows XP file “svchost.exe” as malicious, sending systems into endless reboot loops. Internal documents cited by ZDNet blamed “inadequate” quality control. McAfee was acquired by Intel for $7.7 billion shortly after. Kurtz resigned in October 2011.

Kurtz is now the only cybersecurity CEO to have been at the center of two global IT outages caused by faulty security software updates—a pattern that raises fundamental questions about quality control culture under his leadership.

Following the July 2024 incident, the House Homeland Security Committee formally demanded that Kurtz personally testify before Congress. CrowdStrike declined, stating Kurtz was “not the appropriate witness,” and instead sent SVP Adam Meyers to the September 24, 2024 hearing. Industry observers expressed surprise. Josh Aaron, CEO of Aiden Technologies, stated it was “a little shocking to me that CEO George Kurtz didn’t agree to also testify.” Meyers acknowledged during testimony that the faulty update was distributed “to all customers in one session” and that the validator that failed had been “in place for over a decade.”

Kurtz’s compensation totaled approximately $47 million in FY2024 and $35.2 million in FY2025—158 times median employee pay. The FY2024 package represented a 29% increase over the prior year despite the catastrophic July 2024 incident occurring mid-fiscal year.

SEC Form 4 filings document substantial insider sales in the months preceding the outage. Kurtz sold approximately $28.4 million in stock on January 12, 2024, $25.5 million on March 21, 2024, and $17 million on May 3, 2024—totaling over $70 million in the six months before the incident. The company characterizes these as routine administrative sales to cover tax withholdings on vesting RSUs under 10b5-1 plans. CrowdStrike insiders recorded 41 sales and zero purchases over a twelve-month period.

Delayed reality

Our thesis does not solely rest on speculation about whether the July 2024 outage damaged customer relationships—it rests on the structural reality that most of that damage cannot yet appear in reported metrics. The combination of 1-3 year upfront-paid contracts, 9-12 month enterprise sales cycles, and no termination for convenience means the true retention picture will only emerge through the Q1 2025 to Q3 2026 renewal window.

The company faces this reckoning with a 25.8x P/S multiple that assumes continued premium growth, despite clear evidence of deceleration: revenue growth down to 22%, ARR growth down to 23%, net revenue retention at a record-low 112%, and FCF margin compressing from 31% to 27%. Competitors are growing faster (SentinelOne at 32%), executing aggressive platform strategies (Palo Alto’s $25 billion CyberArk deal), or bundling at zero incremental cost (Microsoft Defender).

Legal exposure compounds operational risk. The Delta lawsuit seeks $500 million and survived a motion to dismiss on gross negligence grounds—the specific claim that could void CrowdStrike’s contractual liability caps. The SEC/DOJ joint investigation into revenue recognition represents potential restatement and enforcement risk. And CEO George Kurtz’s refusal to testify before Congress, combined with his unique distinction as the only cybersecurity executive to oversee two global IT outages, raises fundamental governance questions.

The bull case assumes customers will forgive and renew, the SEC investigation will resolve without material consequence, and competitive dynamics will not erode the premium valuation. The bear case observes that none of these assumptions have yet been tested—and the tests begin now.

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Subsequently, all contracts bought at 30 cents moved to a dollar. This represented a nearly 233% return to traders that followed our research.

Our primary confidence in this trade (which was revealed exclusively to paid subscribers, among other intelligence tranches) was news cycle based. We determined the 70 percent chance of them saying it was grossly overpriced and the clear upside was in them NOT saying it.

The “Condiment Bar” news cycle, came and went. And your average Kalshi trader who trades off vibes based their positions on “Well, if they said it three calls in a row they’ll certainly say it again.”

Don’t take our word for it

Over the past 24 hours I have been inundated on my personal line, Discord, and Substack with the overwhelming gratitude of our readers.

This past week, First Strike has seen an over 1,000% increase in free subscribers, and in turn a 75% increase in paid subscribers. To new subscribers, both paid and unpaid: Thank you!

Lately, our focus has been on event contracts. Primarily because mis-pricing is abundant and we have demonstrated that there is real edge here for those that are willing to do the research, and trade with a “data first” approach.

This weekend we will release our write up on Crowd Strike - and another Kalshi Earnings Call Mention Market write-up so stay tuned.

Both Kalshi and Polymarket had versions of this mention market. What happened next is an interesting case of regulated vs. un-regulated markets.

Kalshi, a CFTC-regulated exchange, resolved based on the rules as written.

Polymarket, an unregulated exchange changed the rules after the event was over, then resolved said event to YES.

This of course, has left Polymarket traders absolutely livid—

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But, how?

On Kalshi, the rules were clear from the start. “Bros” meant “Bros.” The speakers on the call said “Brothers.” The textual interpretation was straightforward, even if some traders disagreed with it. The market resolved according to the written criteria that existed when traders entered their positions.

On Polymarket, the outcome was less certain—so they simply made it certain. After the event concluded, the resolution source was amended to treat “Warner Brothers” and “Warner Bros” as equivalent. After almost three days of back-and-forth disputes—the market resolved to YES.

The CFTC Exists for This Exact Reason

I don’t want to seem like I’m overtly praising CFTC. But let’s be clear about what the CFTC’s oversight of Kalshi actually provides:

1. Immutability: Contract terms are filed with the CFTC and cannot be changed post-issuance on a whim.

2. Transparency: Resolution criteria must be documented and applied in a uniform manner.

3. Recourse: Traders have a regulatory body and the National Futures Assoc. to appeal to if an exchange behaves improperly.

4. Regulatory Accountability: Kalshi can’t just decide who wins after the fact.

Polymarket operates offshore, and outside CFTC jurisdiction. There’s no filing requirement. There’s no regulatory oversight. There’s no appeals process beyond hoping the UMA oracle or Polymarket’s team rules in your favor.

When the rules are whatever Polymarket says they are—and whenever they want to say it—it adds risk to traders.

But isn’t Polymarket Decentralized?

Yes, settlement technically runs through UMA’s oracle system. But when Polymarket itself amends the resolution criteria fed to that oracle, “decentralization” is just theater. The oracle resolves based on inputs. Polymarket controls the inputs.

Decentralized infrastructure executing centralized decisions is just centralization with extra steps.

The Precedent Problem

Polymarket’s has now set the precedence:

The rules are suggestions. We can change them if the outcome feels wrong to us.

How do you price a contract when the strike price can move after expiration?

For small retail traders, this might not matter much. For anyone trying to run a serious strategy, deploy real capital, or build systematic approaches to prediction markets, Polymarket just revealed itself as unacceptably risky not because of market risk, but because of platform risk.

This is a rare case, where regulators ended up being cheered by traders.

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Disclaimer

This article is for informational and educational purposes only and does not constitute investment advice, trading advice, or a recommendation to buy, sell, or hold any position in any event contract or financial instrument. Event contracts involve risk, including the potential loss of your entire investment. Past performance is not indicative of future results.

The author may hold positions in contracts discussed on various prediction market platforms. Contract interpretation and regulatory analysis is opinion only. The author is not providing Commodity Trading Advice (CTA) by publishing this article. Readers should conduct their own research and consult with a qualified financial advisor before making any trading decisions.

You can find the market on Kalshi here.

Disclaimer: This article is for educational and informational purposes only. It does not constitute investment advice, trading recommendations, or solicitation to buy or sell any financial instruments or prediction market contracts. All analysis represents the author’s research and opinions based on publicly available information. Past performance of any trading strategy does not guarantee future results. Readers should conduct their own due diligence and consult with qualified professionals before making any trading decisions. Prediction market trading involves substantial risk of loss.

Kalshi traders currently have the odds of “Condiment Bar” being said on the upcoming Starbucks earning call at a 76% chance. We believe these odds are drastically overstated. We utilized our machine learning system, and social sentiment analysis to provide First Strike readers more accurate odds for this market.

Our analysis examines why keyword frequency models—including systematic quantitative approaches—may be overestimating this particular contract.

The Surface-Level Pattern

First Strike Research maintains API access to every single public facing companies earning calls transcripts, and our machine learning system poured over the data:

• Across 48 Starbucks earnings calls (2014–2025), “condiment bar” received zero mentions for approximately a decade

• The phrase then appeared in four of the last five quarters

• A basic recency-weighted model interprets this as an upward trend

Current Kalshi YES pricing sits around 76¢, implying a high probability that “condiment bar” has become a fixture of Starbucks’ quarterly script. We believe this to be false (at-least going forward.)

Why This Matters Beyond One Trade

Obviously, our model which is trained on historical mention frequencies cannot easily detect when a talking point has transitioned from “forward-looking initiative” to “completed checkbox.” The model notes rising mentions and extrapolates continued discussion.

So in turn we layer qualitative analysis—event timing, social sentiment, and management communication patterns—on top of statistical frameworks.

First Strike’s proprietary machine learning system has the probability for “Condiment Bar” being said at:

31.6% - YES which equates to a roughly 40% over-confidence premium applied to the contract on the numbers alone. When we drill into the social sentiment we’ve found the true odds of them saying Condiment bar even lower. This is based on social head-winds, and business trends we’ve found through our research.

This deep research is provided below to our paid subscribers. If you’re not a paid subscriber become one today!

DISCLOSURE:

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Based on our examination of confirmation mechanics, competitors, and structural factors, our analysis concludes that Waller’s true probability sits closer to 22%

Which is nearly double his current market price. We will also provide a bull case that Christopher Waller may be the next Fed Chair given the current socio-economic-political environment. Paid subscribers will have access to our early warning intelligence signals at the end of the article!

Current State of Play

Treasury Secretary Scott Bessent confirmed on January 21, 2026 that Trump has narrowed the field to three finalists.

These odds reflect public perception of Trump’s statements rather than genuine insider intelligence. The race experienced dramatic volatility after Trump’s January 16 comment about wanting to “keep Hassett where he is”—a 26-point sell-off.

As expected markets are reacting to public signals, not tangible “inside the White House” information.

Waller’s Profile

Christopher Waller brings a unique combination of Trump loyalty, academic credibility, and institutional knowledge.

Credentials:

• Current Fed Governor since December 2020 (term through January 2030)

• Research Director, Federal Reserve Bank of St. Louis (2009-2020)

• Ph.D. Economics, Washington State University

• 100+ peer-reviewed publications on monetary theory and central bank policy

2020 Confirmation: Waller was confirmed 48-47 with five Democratic senators crossing party lines. This is a proven track record of bipartisan ability for confirmation.

December 2025 White House Interview: President Trump conducted several in-person interviews with his short list. Waller was described by two senior officials as having a “strong interview.” It is believed his interview was predominately focused on the labor market and job creation strategies. During this interview President Trump was flanked by Treasury Sec. Bessent, Susie Wiles, and Dan Scavino his Chief of Staff and Deputy Chief of Staff, respectively.

The Case for Waller

1. Senate Math

All roads lead to a 13-11 Republican majority on the Senate Banking Committee.

However —

Senator Thom Tillis (R-NC) publicly declared on January 12 that he will oppose any Fed nominee until the DOJ’s criminal investigation into Powell is resolved. The important keyword to remember here is: ANY.

With Tillis voting no, the committee splits 12-12—blocking any nomination from advancing.

Waller has a unique advantage: Among the four finalists, only Waller has demonstrated bipartisan confirmability. If the DOJ matter involving Powell is not resolved, we expect trump to select the candidate most likely to pass when the window opens.

That candidate is Waller.

2. The Larry Summers Victory

When the Fed began aggressive rate hikes, Larry Summers argued unemployment would need to rise to 6%+ to bring inflation down—implying roughly 4 million job losses. Waller countered with Beveridge Curve analysis suggesting inflation could fall without mass layoffs.

Subsequently—Inflation dropped from 8%+ to near 2%. Unemployment remained below 4%. Waller was correct; Summers was wrong.

Trump overwhelmingly values: economic policy elites (Waller outperformed a Harvard economist) and Trump also wants to be able to take credit for a soft landing and strong job market.

3. The Policy Alignment

Throughout 2025, Waller executed a strategic dovish pivot:

• July 2025: Dissented when FOMC held rates steady, arguing for a 25 basis point cut

• November 2025: Made “The Case for Continuing Rate Cuts” in London, citing labor market concerns

What Waller posits is directly aligned with Trump’s routinely public calls for “lower interest rates”

Competitor Vulnerabilities

Kevin Warsh (51% Odds)

The Estée Lauder Problem: Warsh married Jane Lauder, granddaughter of Estée Lauder’s founder. His father-in-law Ronald Lauder is a Trump ally who planted the Greenland annexation idea. This connection provides access but creates a nepotism narrative that Senate Democrats will exploit.

Policy Inconsistency: Warsh resigned from the Fed in 2011 after opposing QE2, warning about inflation risks from monetary expansion. His current advocacy for aggressive rate cuts represents a complete reversal that confirmation hearings will expose.

The Morgan Stanley Conflict: Warsh worked at Morgan Stanley 1995-2002. During the 2008 crisis, as Fed Governor, he negotiated Morgan Stanley’s conversion to bank holding company status to access Fed emergency lending—a potential conflict that generates negative headlines.

Rick Rieder (28% Odds)

We believe Rieder’s odds are vastly over estimated—Rieder has never held a federal position, testified before Congress, or navigated Senate confirmation.

Rieder was head of Lehman’s Global Principal Strategies team (proprietary trading) until the September 2008 bankruptcy. Democrats will likely grill him on his involvement in this.

The Washington Examiner reported January 20 that Rieder has supported higher corporate tax rates and increased immigration—positions directly contrary to Trump’s agenda.

Kevin Hassett (5% Odds)

Hassett’s odds crated when Trump’s January 16 statement appeared to eliminate Hassett, though his “I want to keep you where you are.”

Five Scenarios Where Waller Wins

Scenario 1: Confirmability Crisis (20-25% probability) Warsh and Rieder hit serious confirmation turbulence. Bessent and Wiles argue Waller is the only candidate with proven bipartisan support.

Scenario 2: Labor Market Narrative (15-20%) Weak January/February jobs data shifts Trump’s framing from “Fed Chair who cuts rates” to “Fed Chair who protects jobs.” Waller’s soft landing prediction provides credibility.

Scenario 3: Powell Stay-On (25-30%) Powell announces he’ll remain as Governor through January 2028. Waller’s working relationship with Powell (both dissented in July 2025) provides operational compatibility other candidates lack.

Scenario 4: The Tillis Trade (15-20%) Tillis negotiates: he drops the blockade if Trump withdraws the Powell investigation and nominates a genuinely independent Chair. Waller’s “absolutely” defense of Fed independence provides Tillis cover.

Scenario 5: Bessent Advocacy (10-15%) Treasury Secretary privately pushes Waller as optimal for confirmability, competence, and dovish policy. Bessent’s market credibility gives him unique influence over Trump’s decision.

The Bear Case

Despite structural strengths, Waller faces headwinds:

• Trump historically prioritizes personal relationships and media presence over technocratic competence

• Waller’s December 17 statement that he would “absolutely” defend Fed independence may have been disqualifying

• Sustained 12% odds despite his December interview suggest insiders know he’s not Trump’s choice

• At 67 in 2026, Waller may be seen as too old for a decade-shaping appointment

The Contrarian Position:

We believe Waller at 12% represents significant value. A $1,000 bet returns $8,300 if he wins.

Please bear in mind— the above statement is governed via our Terms

🔒 Early Warning Indicators (Paid Subscribers Only) 🔒

The following intelligence collection priorities and early warning signals are available to paid subscribers. These indicators may provide 24-72 hours advance notice of the selection before public announcement.

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A recently closed mention market on Kalshi just delivered a masterclass in why you should read the fine print. I’ve said this numerous times, on pretty much all of my Kalshi / Event-Contract articles.

READ.

THE.

RULES.

The setup was simple: bet on whether a speaker would say “Warner Bros” during a public address. The speaker mentioned “Warner Brothers.” The contract is now in limbo—and traders are learning an expensive lesson about the difference between common sense and contractual language.

The Literal vs. The Obvious

On its face, this feels absurd. Warner Bros is Warner Brothers. Same company. Same referent. If you asked anyone on the street whether the speaker “mentioned Warner Bros,” they’d say yes without hesitation.

Again, this is why it is absolutely vital that you READ THE RULES.

The rulebook is explicit about what counts:

• ✅ Plurals and possessives — “Immigrants” counts for “Immigrant”

• ✅ Hyphenated compounds — “pro-Palestine” counts for “Palestine”

• ✅ Homonyms — “ice water” counts for “ICE”

• ❌ Grammatical inflections — “Immigration” doesn’t count for “Immigrant”

• ❌ Homophones — “right” doesn’t count for “write”

Notice what’s missing? Any provision for abbreviations and their expansions.

The NO Case is Stronger

The rules use slashes to indicate alternatives: “Elon / Musk” and “Doge / Dogecoin” appear as examples. If Kalshi intended both “Warner Bros” and “Warner Brothers” to resolve the market, they had a clear mechanism to say so.

They didn’t. And I will play devil’s advocate that Kalshi should’ve included the / in the market and named it Warner Bros / Warner Brothers… but they didn’t.

With that in mind the YES side will argue intent, common understanding, and the “spirit” of the market. They’ll point to the transliteration rule allowing variations of “Zelensky” and claim this is analogous.

But here’s the problem: “Zelenski” and “Zelensky” are spelling variations of the same word. “Bros” and “Brothers” are different words entirely—one is an abbreviation, the other is the full form. The rules are strict everywhere else. Why would this be the exception?

The Precedent Problem

If “Warner Brothers” counts for “Warner Bros,” where does it stop?

• Does “United States” count for “US”?

• Does “okay” count for “OK”?

• Does “cryptocurrency” count for “crypto”?

Markets need predictable rules. The moment you start adjudicating based on “reasonable interpretation” rather than textual analysis, you’ve created uncertainty that makes these contracts harder to price.

What Happens Next

Expect a support ticket war. Traders on both sides will make their case, and Kalshi will have to rule.

My read: the NO side should prevail on a strict textual interpretation. But this is Kalshi—outcomes aren’t always predictable, and “common sense” arguments have swayed resolutions before.

In very rare cases, Kalshi reserves the right to “wash” the market and void every parties trade. This would result in all shares being “burned” and allocated capital returned to traders—granted they held to expiration.

Either way, this market is a reminder: in prediction markets, the edge isn’t just in forecasting events. It’s in forecasting how the rules will be applied to those events.

What’re your thoughts? Do you think YES or NO holders should be paid?

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This article is for informational and educational purposes only and does not constitute investment advice, trading advice, or a recommendation to buy, sell, or hold any position in any event contract or financial instrument. Event contracts traded on CFTC-regulated exchanges like Kalshi involve risk, including the potential loss of your entire investment. Past performance is not indicative of future results.

Readers should conduct their own research and consult with a qualified financial advisor before making any trading decisions. The views expressed are solely those of the author and do not represent the views of any exchange or regulatory body.

Event contract rules and settlement outcomes are determined solely by the relevant exchange. The author’s interpretation of contract rules is opinion only and should not be relied upon for trading decisions.

Position Disclaimer: The author of this article holds NO “Warner Bros” contract(s.)

First Strike has continued its investigation into Monroe Capital Corporation and we’ve found MRCC and it’s shareholders are significantly exposed to HFZ Capital Group and its affiliated entities. For context, the MRCC portfolio company: HFZ Capital Group has a checkered history of botched real estate projects which include criminal indictments in the New York City Metro.

You can read the first article here.

Based on our investigation into MRCC’s SEC filings, public court records, and public reporting we have ascertained the following:

1. MRCC has approximately $30.3 million in cost basis exposure to HFZ, which they currently mark at $35.5 million fair value which relates to a 17% premium to cost.

2. In turn, we believe HFZ to be functionally insolvent given that HFZ has forfeited all major assets in foreclosure action all while facing $420+ million in outstanding legal judgements. Moreover, Anthony Marrone of HFZ pled guilty to grand larceny.

3. MRCC has received next to nothing from HFZ. All reported income has been “Payment-In-Kind” which are paper gains we believe to be worthless obligations.

4. MRCC’s HFZ exposure is approximately 16% of their NAV.

5. A proper mark-down would reduce MRCC’s NAV by approximately $1.50-$1.64 per share.

   ---

First Strike investigators reviewed MRCC’s Schedule of Investments (as filed with the SEC via Forms 10-Q and 10-K)

We’ve identified five distinct line items attributable to HFZ Capital Group and its restructured affiliates.

For background, MRCC originated a loan to HFZ, then restructured the deal so that part of the loan sits in a new structure within MC Asset entities.

Notwithstanding, MRCC still maintains an effective 18 million dollars of exposure to HFZ, however, it was split into two pieces.

Much of the interest on these loans is Payment-In-Kind (PIK.) We believe this is a clear indicator of financial distress in the underlying asset (HFZ.) When a company (in this case HFZ) pivots to PIK it is a clear sign of a cash vacuum. Instead of repayment in cash, the PIK merely delays default by compounding interest into the existing loans.

Phantom Income and Dividend Risk

MRCC is a Business Development Company— as such they are legally required to pay out 90% of their income to maintain their tax status. Since PIK technically counts as taxable income.

The Feedback Loop

We believe there is tangible risk that MRCC may enter a PIK/Dividend feedback loop—based primarily on the underlying solvency issues with one of their largest portfolio companies on their books (HFZ.)

1. MRCC books PIK interest as income, consequently reported and taxable income rise with no support inflows of actual cash.

2. In turn—MRCC must distribute at least 90% of this phantom income.

3. Given there is no actual cash to back the PIK income, MRCC may have to fund these distributions by borrowing, selling other assets, or issuing new equity.

4. This then creates a feedback loop of significant balance sheet strain.

5. If investors notice dividends are being funded by leverage and asset sales the stock price could start to fall. When PIK is deemed terminally uncollectible income will subsequently drop and the loop will be further reinforced, if not completed.

We believe this event may already be materializing on the balance sheet based on management’s recent slashing of the dividend down to 0.18 from 0.25. Even if management attempts to mitigate these concerns by pointing to yield compression, credit issues, and merger strategy.

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2020 RESTRUCTURING

MRCC’s SEC filings include a footnote explaining a 2020 restructuring of its HFZ position. We offer this quote directly from the Company’s Schedule of Investments:

“In connection with the restructuring of its investment in HFZ Capital Group, LLC (’HFZ’) and HFZ Member RB Portfolio, LLC (’Member RB’), the Company obtained a 15.9% equity interest in MC Asset Management (Corporate), LLC (’Corporate’). In addition, the Company exchanged its investment in the loan to Member RB for a promissory note in MC Asset Management (Industrial), LLC (’Industrial’). Corporate owns 100% of the equity interest in Industrial.”

“The Company also participated $4,758 of the principal amount of its investment in the loan to HFZ as an equity contribution to Industrial. The participation did not meet the definition of a ‘participating interest’ under ASC Topic 860, Transfers and Servicing. As a result, the participation did not qualify for sale accounting and the Company continues to reflect the full amount of its investment in the HFZ loan.”

In simple terms—MRCC restructured a deteriorating HFZ loan into a complex multi-entity structure. Despite multiple line items appearing on the Schedule of Investments, the economic reality is unchanged: this is a single concentrated exposure to HFZ’s real estate portfolio, now filtered through holding company shells.

The failed sale accounting under ASC 860 is particularly notable. MRCC attempted to recharacterize a portion of its loan as an “equity contribution” but was unable to achieve derecognition under GAAP. The Company nonetheless presents the participated portion as a separate investment—a presentation that, in our view, obscures the concentrated nature of this exposure.

Downstream Analysis of HFZ

In reality, MRCC is a junior creditor sitting very low in the recovery bracket. Through the course of our investigation we’ve estimated the overall chances of MRCC accomplishing any degree of asset recovery from HFZ:

On August 5th, 2024 HFZ Capital Group pled guilty to three counts of grand larceny in the first degree and criminal tax fraud. In consequence the company received an unconditional discharge and attributed all criminal wrongdoing to Nir Meir.

Subsequently, Mr. Meir filed personal bankruptcy to the sum of $30 Million in liability and listed a total of $50 in assets. Mr Meir was arrested and spent over thirteen months at Rikers Island before materializing $1 Million dollars in a secured cash bail.

His criminal trial remains pending.

HFZ Foreclosure History

Further complicating MRCC’s odds of asset recovery is HFZ has lost total control of all of their major development projects. Granted, one was foreclosed on by Monroe themselves.

Court Judgements

First Strike has gone on to find the following material judgements against HFZ Capital Group and associated companies—

We believe the most concerning of these judgements is the CCO Condo Portfolio case—this involved the foreclosure on four Manhattan condo projects. This judgement remains unsatisfied and is senior to any claim MRCC may present.

Waterfall Analysis

We conducted a simple recovery waterfall to calculate the chance of any value from HFZ reaching MRCC’s structural position.

1. Senior Secured Mortgages (~$2.0B+ across projects) — Substantially satisfied via foreclosure

2. Senior Mezzanine Debt (~$500M+) — Foreclosed or converted to equity

3. Junior Mezzanine Debt (~$350M+) — Foreclosed; deficiency judgments remain

4. Trade Creditors & Liens (~$100M+) — Omnibuild $100M+ lien; many unpaid

5. Judgment Creditors (~$128M+) — Active collection efforts ongoing

6. Unsecured Claims (various)

7. HFZ Equity / MRCC’s Position — Structurally last

Every major HFZ asset has been liquidated through foreclosure. In each case, sale proceeds were insufficient to satisfy senior claims, resulting in deficiency judgments rather than distributions.

There is no remaining asset base from which MRCC could recover.

NAV Impact

First Strike estimates that MRCC’s HFZ‑related exposure totals $30.3 million at cost and $35.5 million at reported fair value, based on the consolidated Schedule of Investments and related footnote describing the HFZ/MC Asset restructuring.

We calculate the NAV impact of a full write-down of this exposure as follows:

• As of September 30, 2025, MRCC reported net assets of approximately $173.0 million (about $7.98 per share), and disclosed HFZ/MC Asset exposure of roughly $30.3 million at cost and $35.5 million at reported fair value in the consolidated Schedule of Investments and related HFZ restructuring footnote. A full write-down of this complex to zero would reduce total NAV by $35.5 million, to roughly $137.5 million, implying a pro forma NAV of about $6.35 per share, or a decline of approximately $1.63 per share

Bear in mind the numbers presented above represent a zero recovery scenario (which we consider most likely)

It does not calculate or account for the following:

• Reversal of previously recognized PIK income.

• Potential dividend reduction if NII coverage erodes.

Disclosure Concerns

MRCC marks HFZ as one of their top portfolio investments—we believe investors relying solely on these filings and absent this investigative report would be unaware of the significant red flags we’ve obtained here-in and here-to.

Opaque SEC Disclosures

First Strike conducted an investigation into MRCC’s SEC filings over the past several years and uncovered a consistent pattern: HFZ Capital Group is provided to investors as a large portfolio position, not as a borrower embroiled in civil and criminal turmoil.

In annual reports, and quarterly reports HFZ Capital Group, LLC (and related entities) are repeatedly listed as MRCC’s largest positions with fair value listed in the tens of millions of dollars.

First Strike considers what the filings DO NOT contain as considerably important to MRCC investors.

In sum, our research indicates that MRCC’s SEC filings omitted the following material events:

• HFZ Capital Group, its executives, or affiliates being criminally charged, arrested, and/or indicted.

• HFZ or its affiliates pleading guilty in New York court.

• Any fraud prosecution, criminal case, or investigation involving HFZ.

Continuously MRCC presents HFZ merely as investment - a line item in the portfolio - even after a public report of HFZ’s guilty pleas.1

Conclusion

First Strike Investigators has determined that MRCC’s $35.5 million fair value exposure to HFZ Capital Group represents a material overstatement of Net Asset Value. Based on the evidence assembled herein—including HFZ’s guilty plea to criminal fraud, loss of substantially all assets through foreclosure, outstanding judgments exceeding $128 million, and zero history of cash payments to MRCC—we conclude that realistic recovery on this exposure is negligible.

We estimate MRCC’s NAV is overstated by approximately $1.50–$1.64 per share, or 17–19% of reported book value, solely as a result of the HFZ exposure.

Management’s failure to disclose HFZ’s criminal conviction, explain the status of matured obligations, or justify fair value marks on a functionally insolvent counterparty raises questions regarding the adequacy of MRCC’s financial reporting and the rigor of its investment valuation process.

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IMPORTANT DISCLOSURES & DISCLAIMER (READ CAREFULLY)

This publication is provided for general informational and educational purposes only and reflects the author’s opinions as of the date of publication, which are subject to change without notice. Nothing herein is, or should be construed as, investment advice, legal advice, accounting advice, tax advice, or a recommendation or solicitation to buy, sell, or hold any security or other financial instrument; readers should consult their own licensed professionals and conduct independent due diligence.​

All statements of fact are intended to be drawn from publicly available materials believed to be reliable at the time of writing (including, where cited, SEC filings, court pleadings, motions, bankruptcy dockets, press releases, news reporting, and municipal/public records). To the extent this publication quotes, summarizes, or characterizes allegations from lawsuits, administrative proceedings, regulatory actions, or other disputes, such allegations are unproven unless and until established by a final adjudication, and the defendants deny wrongdoing as reflected in the cited record.​

Any references to “fraud,” “scheme,” “predatory,” “coercive,” “usurious,” “improper,” “misconduct,” “incompetence,” or similar terms are used, if at all, only as shorthand for allegations described in cited proceedings or as non-literal rhetorical or analytical commentary, and not as a statement of proven criminal or civil liability. Readers should not interpret any discussion of incentives, motive, intent, knowledge, control, or causation as a factual assertion of any person’s state of mind; such discussion reflects the author’s opinion and inference based on the public record cited.​

Estimates, projections, scenarios, and forward-looking statements (including those regarding valuation, rent/lease economics, NAV implications, merger outcomes, portfolio performance, credit metrics, or “risk”) are inherently uncertain and may differ materially from actual results; such statements are provided for illustrative purposes only. Numerical figures may be rounded, derived, or modeled and could contain errors; readers are encouraged to verify all figures independently.​

The author and publisher disclaim any duty to update this publication. By reading this publication, you agree that the author and publisher shall not be liable for any losses, damages, or claims arising from the use of, reliance on, or inability to use the information hereto.

Positions / conflicts: The author’s positions (if any) may change at any time, including before or after publication, without notice. Unless expressly stated otherwise in this publication, the author does not hold any fiduciary relationship with readers.

As part of routine investigative due diligence, First Strike Investigators may contact companies and other market participants to evaluate publicly observable business practices, customer-facing representations, and operational risk indicators. Any such outreach is intended solely to understand general processes, stated underwriting criteria, and downstream risk profiles, and is not intended to obtain non-public information, to interfere with any business relationship, or to induce reliance by the contacted party.

Contact: outreach@firststrike.trading

Monroe Capital Corporation (NASDAQ: ) is a publicly traded business development company (”BDC”) headquartered in Chicago, Illinois. The company operates as an externally managed, non-diversified closed-end investment fund that has elected to be regulated as a BDC under the Investment Company Act of 1940.

Over the last several years, Monroe’s share price has retreated significantly from a COVID era high of $11.67 (Nov 2021) a share, and Monroe’s cash return to shareholders has subsequently been halved —

• Historical dividend: $0.35 per share quarterly (annualized $1.40)

• Current dividend: $0.18 per share quarterly (annualized $0.72)

• Total reduction: 48.6% decline in the annualized dividend rate

In our research, MRCC has exhibited a recurring pattern of deal structuring and pre‑closing diligence that, in our view, has at times left certain MRCC portfolio companies more highly levered and operationally sensitive than is prudent, which increases the risk of companies not being able to service their debt. We further believe these risks may be compounded by forecasting and expense decisions that appear inconsistent with a period of tightening credit conditions and reduced shareholder distributions, including the company’s premium office footprint and related build‑out spending

Based on a review of public filings and court documents, First Strike Investigators identified four principal concerns:

1. Nearly $9 million in premium office construction during sustained dividend cuts

2. A $40 million credit facility to MV Realty, now subject to a federal class action alleging Monroe financed a predatory scheme affecting 38,000 homeowners;

3. A $22.4 million loan to The Worth Collection that a bankruptcy trustee seeks to avoid as an alleged fraudulent transfer.

4. Exposure to Lifted Trucks, a specialty vehicle lender facing consumer protection scrutiny.

And—as a bonus, First Strike Investigators called a MRCC loan portfolio company, Lifted Trucks, while posing as a subprime borrower to understand downstream risk profiles and to assess the ability of MRCC’s portfolio companies to service their own obligations as MRCC’s debtors.

Not withstanding—we’ve continuously uncovered a pattern at Monroe Capital Corporation that turns middle‑market credit into what we believe to be a risk transfer machine with an inordinate burden placed on investors.

While giving due consideration to the pending NAV‑for‑NAV merger with Horizon Technology Finance Corporation, in which MRCC shareholders are expected to receive HRZN ( ) shares based on relative net asset values (incld. Monroe’s) and to own roughly 37% of the combined company, we believe MRCC’s management may face incentives that are not fully aligned with either current MRCC shareholders or existing Horizon shareholders.

Because the merger consideration is tied to MRCC’s reported NAV, any upward pressure on reported asset values or understatement of indirect corporate costs could, in theory, support a higher effective claim on the combined company’s equity for MRCC holders, and thus increase the dilution experienced by existing Horizon holders.

Bear in mind — we are not alleging that MRCC has misstated its NAV or violated accounting rules; rather, we highlight these structural incentives as a potential governance risk that investors in both companies should evaluate independently.

REQUIRED DISCLOSURE: This report presents a short-biased investment thesis. The author DOES NOT hold short positions in MRCC securities and DOES NOT stand to benefit from a decline in share price. All information herein is derived from publicly available sources including SEC filings, federal court documents, bankruptcy proceedings, and municipal records. Allegations from pending litigation are unproven; defendants have denied liability. This is not investment advice. Conduct independent due diligence.

THESIS

Based on a review of public filings and court documents, Monroe Capital Corporation presents material risks that, in our assessment, are not fully reflected in current market pricing:

• New Office Build-Out During Dividend Cuts $8.9M in permit-documented construction costs

• MV Realty Class Action $40M credit facility to alleged predatory scheme 38,000 potential class members

• Worth Collection Bankruptcy $23M loan subject to fraudulent transfer claims

• Dividend Deterioration 48.6% reduction from historical levels (company disclosures)

CAPITAL MISALLOCATION: THE 155 N. WACKER BUILD-OUT

Monroe Capital relocated its Chicago headquarters from 311 South Wacker Drive to 155 North Wacker Drive in early 2025, expanding its footprint by approximately 38% to 40,000 square feet across more than one and a half floors. The move is notable because their original office at 311 South Wacker is itself a Class A, LEED Gold-certified trophy tower—a 65-story landmark that was once the world's tallest reinforced concrete building, featuring a 12,000 square-foot conference center, 7,600 square-foot fitness center, and the iconic illuminated crown visible across Chicago's skyline.

First Strike Investigators found no evidence of building deficiencies or lease disputes that would necessitate departure from 311 South Wacker (above) rather, Monroe's broker characterized the move as seeking "a trophy asset that offered improved access to in-building and neighborhood amenities."

The decision to expand premium office space by 38% while simultaneously cutting shareholder dividends by 48.6% raises questions about management's capital allocation priorities.

Additionally, First Strike contacted MRCC Investor Relations to posit several relocation questions—MRCC did not respond for comment. The following questions were presented to MRCC Management via Electronic Mail

As such, we further direct the following questions to MRCC management:

1. In light of the Company’s contracting asset base and declining net asset value, what rationale supported the Board’s decision to relocate to a premium “Trophy Class” facility specifically located at 155 N Wacker Dr 35th Floor, Chicago, IL 60606 while simultaneously expanding the corporate footprint by approximately 60%?

2. What specific cost mitigation initiatives, if any, has management undertaken at the 155 N. Wacker location to offset the estimated $1.45 million annual rent increase and protect shareholder interests?

Findings

Monroe Capital occupied approximately 25,000 square feet at 311 S Wacker. Based on comparable market data for Class B/C office space in Chicago's West Loop submarket, estimated lease rates ranged from $30 to $38 per square foot.

Estimated Annual Occupancy Cost (Prior Location):

25,000 sq. ft. × $34/sq. ft. (midpoint) = $850,000 

Monroe Capital executed a lease for approximately 40,000 square feet at 155 N. Wacker Drive, a 48-story commercial tower developed by the John Buck Company in 2009. The property is classified as "Trophy Class"

Based on publicly available commercial real estate data for Trophy Class properties in Chicago's central business district, estimated lease rates range from $50 to $65 per square foot.

Estimated Annual Occupancy Cost (Current Location):

40,000 sq. ft. × $57.50/sq. ft. (midpoint) = $2,300,000 

Permit Evidence

Moreover, First Strike Investigators obtained and reviewed City of Chicago building permit records which document Monroe Capital Investment Holdings, L.P. expended $8,910,942 constructing premium office space at 155 North Wacker Drive during a period of sustained dividend reductions and NAV contraction.

The following permits were filed with the City of Chicago Department of Buildings and are independently verifiable:

These expenditures occurred while:

• Quarterly dividend fell from $0.35 to $0.18 per share (48.6% reduction)

• NAV per share declined materially (company-reported)

• The company faced mounting litigation exposure and regulatory scrutiny

The build-out features a custom connecting staircase between floors and operable partition systems—amenities inconsistent with cost discipline expected during periods of financial stress.

The above image is the custom staircase constructed at Monroe Capital. Generally speaking, this is a very costly project in commercial real estate as it requires significant labor, and permitting. Moreover, Monroe Capital made this renovation on a leased property.

Implication

Standard asset management practice dictates that overhead costs should decline proportionally—or at accelerated rates—during periods of revenue contraction to preserve operating margins. The evidence we presented above indicates MRCC's management pursued the inverse approach: increasing fixed costs while revenue declined.

Management elected to allocate nearly $9 million to premium office construction while simultaneously reducing shareholder distributions by approximately $14.6 million annually, based on the dividend cut across the share base. This capital allocation decision warrants scrutiny with respect to management alignment and governance.

MONROE ALLEGEDLY FINANCED A PREDATORY LENDING SCHEME

Case Reference

Keller v. Monroe Capital Corporation, No. 1:25-cv-02474-SAG (D. Md.)

• Status: Motion to Dismiss pending

• Potential Class Size: ~38,000 homeowners

• Monroe Credit Facility Amount: $40 million

Legal sections are drawn from publicly available court filings and other public records. These allegations are unproven, and all defendants, including Monroe Capital Corporation and its affiliates, have denied liability. Any characterizations of conduct or incentives reflect the author’s opinions based on those materials and are not statements of fact about anyone’s actual intent or legal liability. Nothing herein should be interpreted as legal advice or as a conclusion that any contract or practice is unlawful; such determinations rest with courts and regulators.

The Alleged Scheme: Overview

According to the complaint, the alleged action arises from:

“a fraudulent and predatory scheme initially designed by MV Realty PBC, LLC and financed, strategically directed, and monetized by Monroe Capital.“

The complaint alleges Monroe Capital was not a passive lender but an active strategic partner that enabled MV Realty to scale from operating in a handful of states to 33 states nationwide.

The Homeowner Benefit Agreement

We examined the complaint’s detailed description of the HBA structure, which plaintiffs characterize as a predatory financial instrument disguised as a real estate listing agreement.

What Consumers Were Told

According to the complaint, MV Realty’s marketing materials promised “risk-free cash” with “no obligation to return the payment” and “no obligation to sell.” Telemarketers positioned the HBA as “better than a loan“ because it required no credit check and, they claimed, no repayment. What consumers received appeared modest: a cash payment typically ranging from $300 to $1,000, averaging approximately 0.03% of their home’s value.

For a family struggling with bills, facing an unexpected expense, or simply seeking a financial cushion, this upfront cash appeared harmless.

What Consumers Actually Signed

The complaint describes a dramatically different reality:

A 40-Year Exclusive Listing Agreement: The HBA bound not only the homeowner but their heirs for four decades—a contractual term that would follow families across generations.

A Recorded Encumbrance on Their Property: The complaint states MV Realty would, “within 48 hours of signing the HBA, record the agreement in the state land records.” This recorded memorandum functioned, as plaintiffs allege, as a lien—clouding the property’s title and preventing homeowners from refinancing, accessing equity, or selling without MV Realty’s involvement.

A 3% Termination Penalty: To escape the agreement, homeowners faced a termination fee of 3% of their home’s value—a payment that typically exceeded the original cash advance by a factor of ten or more.

The Alleged Economic Reality

The complaint provides this calculation:

“If a homeowner receives $500 for signing the HBA and sells the home five years later for $250,000, they owe MV Realty $7,500—a 1,400% return on the original payment.“

The complaint articulates the true purpose of this structure:

“The true purpose of the HBA was to so completely frustrate the homeowner that they capitulated and paid an onerous penalty to terminate the HBA. This ‘termination fee’ was typically 3% of the home’s value, making it at least ten times the amount of MV Realty’s upfront cash payment.“

Despite repeated assurances that the HBA was “not a loan,” the complaint alleges that “for all functional purposes, the HBA Scheme functioned as a predatory high interest loan.“

Monroe Capital’s Alleged Knowledge

The complaint cites a May 28, 2021 due diligence document titled “MV Receivables II, LLC New Business Presentation” allegedly authored by Monroe Capital BDC Advisors, LLC. First Strike Investigators consider this 39-page document central to understanding Monroe’s alleged awareness of the scheme’s mechanics.

On the extraordinary returns:

“MV Realty earns an internal rate of return of 150% on an HBA.”

On the lien mechanism:

“[I]mportantly, the FLC (referring to an HBA), is recorded on the home title... so the homeowners are unable to sell the homes with clean title without paying MV[R] its commissions.“

On industry classification:

The presentation lists MV Receivables II as operating in the “banking and finance“ industry—not real estate.

On exit strategies:

Exit strategies discussed include “Portfolio Securitization“ and “Sale of the Company“—treating homeowner obligations not as real estate service contracts but as financial instruments to be packaged and monetized.

The complaint alleges this presentation demonstrates Monroe Capital’s awareness of the coercive and deceptive aspects of the HBA scheme and that it acted in furtherance of that scheme.

Revenue Structure: The 77% Termination Fee Finding

First Strike Investigators consider this statistic among the most significant in the complaint. The complaint cites an investigation by the Georgia Attorney General:

“Between January 2021 and December 2022, early termination fees accounted for 77% of all ‘harvests’“ in that period.

The complaint states this percentage was representative nationwide, implying MV Realty’s business model depended on penalty extraction rather than brokerage services.

When more than three-quarters of revenue derives from consumers paying to escape contracts rather than from the stated service, questions arise regarding the legitimacy of the underlying business model—and the due diligence conducted by Monroe.

Monroe’s Alleged Profit Share

According to the complaint, Monroe Capital’s financial stake in the “harvests” from homeowners escalated over time:

• Year 1: MV retained harvest revenues but had to substitute new HBAs as collateral.

• Year 2: Monroe Capital allegedly received 50% of all harvest proceeds.

• After Year 2: Monroe Capital allegedly received 100% of harvest proceeds, including from earlier portfolios.

The complaint alleges Monroe’s financial gains extended to MV Realty’s existing portfolio, including agreements secured through prior marketing practices.

Employee Testimony: Todd Schneider

The complaint references testimony from Todd Schneider, a former MV Realty agent and team leader, in the Indiana Attorney General enforcement action.

On Monroe’s control over valuation:

“Schneider states valuation rules were dictated or directed by Monroe’s investor requirements.”

On Monroe’s influence over consumer-facing terms:

“Schneider also testified that MV Realty reduced homeowner payments when interest rates rose in order to satisfy Monroe Capital’s investment return expectations, demonstrating Monroe Capital’s influence and control over consumer-facing contract terms.”

On securitization:

“According to Mr. Schneider, Monroe Capital not only funded the program but also participated in securitizing the HBA contracts and selling them to third parties, transforming future termination fee obligations into financial products.”

First Strike Investigators consider this testimony significant: if credited and sustained, it suggests Monroe’s influence extended to the very terms offered to consumers at the point of sale.

Monroe’s Alleged Operational Control

The complaint cites contractual arrangements that plaintiffs argue demonstrate operational involvement beyond passive lending:

Board Observation Rights (Section 6.07 of the Equity Purchase Agreement):

“...observation rights at all Board of Directors meetings of the Company (or any committee thereof)... the Agent [Monroe], at its option, shall be permitted to have a designee attend in-person or via telephone for sessions covering matters relating to the Company’s business including... operational details (including financial performance, operations, credit, and collection and servicing); [and] legal and regulatory updates...”

The Backup Servicing Agreement:

• “Provides that ‘the lender,’ defined as Monroe, must approve the assignment of backup brokers“

• “Grants Monroe Capital (as lender) the power to act directly in place of MV Realty, including the right to assign or remove backup brokers”

• “Gives Monroe Capital access to transactional records, listing agreements, and broker communications for properties secured by HBAs”

Expansion Approval:

“On information and belief, Monroe Capital’s financing agreements required MV Realty to obtain approval before entering new markets, particularly because Monroe’s valuation and risk models were tied to enforceability of the liens and harvest rate projections in each state.”

Defendant’s Position (Motion to Dismiss)

Monroe Capital’s motion to dismiss characterizes its role as that of a passive lender not alleged to have directly engaged in fraud:

“Plaintiffs allege MV Realty defrauded them into entering illegal contracts. Rather than sue MV Realty, they sue Monroe, a lender to MV Realty. But Monroe itself is not alleged to have actually engaged in any fraud, and, as a lender, Monroe is not responsible for MV Realty’s actions.“

Monroe frames its contractual protections as “garden-variety creditor protections” and “normal incidents of a borrower-lender relationship.”

First Strike Investigators note the tension between this characterization and the allegations regarding Monroe’s due diligence documentation acknowledging 150% IRRs, board observation rights, backup servicing authority, expansion approval requirements, and profit-sharing arrangements with escalating percentages of “harvest” proceeds. Please bear in mind, again, all information provided above is alleged.

WORTH COLLECTION

Case Reference

Tabachnik v. Monroe Capital Management Advisors, LLC, Adv. Proc. No. 23-50321-BLS (Bankr. D. Del.)

• Status: Litigation ongoing

Monroe’s financing of The Worth Collection is a clear example of aggressive deal structuring that appears to prioritize shareholder cash-outs over the company’s ability to survive. The Chapter 7 trustee alleges that Monroe helped fund a leveraged buyout that put more than $25 million of debt on Worth while leaving the company with only about $1 million of operating cash, effectively making it insolvent at closing.

According to the complaint, most of the money went to selling shareholders rather than into the business, while Worth’s interest expense exploded and the company swung from profit before the deal to losses afterward, defaulting on covenants within a year and liquidating by 2020. Worth is asking the court to unwind Monroe’s $22.4 million loan, strip its liens, and even recharacterize its supposed “debt” as equity—remedies normally reserved for conduct viewed as abusive or inconsistent with arms‑length lending.

Viewed alongside other regulatory and litigation matters involving Monroe’s risk and disclosure practices, this transaction can be presented as part of a broader pattern: deals that maximize leverage, rely on optimistic projections, and expose both borrowers and Monroe’s own investors to undue downside, raising serious questions about Monroe’s underwriting discipline and risk culture.

DOWNSTREAM CREDIT ANALYSIS

First Strike Investigators utilized open source intelligence collection methods to surmise Monroe’s exposure to downstream credit risk. First Strike chose one of Monroe’s largest portfolio companies for analysis:

Lifted Trucks Holdings, LLC, is an Arizona‑based used truck dealership group specializing in customized lifted pickups and SUVs.

Monroe entered the Lifted Trucks facility in August 2021, committing a senior secured term loan and revolver maturing in 2027. In 2021, used‑vehicle pricing and dealer profitability were still inflated by stimulus, supply constraints, and ultra‑low rates, but the tightening cycle and affordability reversal were already visible in the macro backdrop.

Rather than building in a margin of safety, Monroe chose to lend long‑dated capital at double‑digit yields to a business model whose demand and credit quality are acutely sensitive to rising rates and normalization in used‑vehicle values.

• As of the March 31, 2024 10‑Q, MRCC’s exposure consisted of:

  • Senior secured term loan:

    • Principal: approximately $6.843 million

    • All‑in interest: about 11.18% (SOFR + 5.85%)

    • Maturity: August 2, 2027

    • Fair value: $6.702 million (very close to par)

  • Revolving credit facility:

    • Funded balance: about $0.889 million

    • Fair value: $0.871 million

• Combined, Monroe had roughly $7.6 million of funded exposure to Lifted Trucks, representing around 3.8–4.0% of MRCC’s net assets at that time.

Put simply, this is one of MRCC’s larger single‑name positions, and the loan is still carried near 100 cents on the dollar despite a weakening used‑vehicle and subprime credit backdrop.

A First Strike Investigator contacted a Lifted Trucks sales manager by phone in January 2026, and posed as a prospective buyer with a stressed credit profile to test how aggressively the dealership is using leverage to drive sales.

• First Strike imitated the following buyer persona:

  • FICO score “around 600“ after the past year.

  • Recent charged‑off credit cards, with only “repayment plans” in place.

  • Self‑reported income of $100,000 per year.

  • Looking for a truck in the $80,000 price range.

• Manager’s response on approval odds:

  • The manager said he could get the buyer approved “without a worry” given the $100k income, despite the charged‑off cards.

  • He volunteered that he has seen customers with credit scores as low as ~450 obtain financing.

  • He went on to volunteer that their finance department is one of the best in the country at closing deals and getting buyers behind the wheel.

  • He indicated the only profiles he routinely sees declined are borrowers with prior foreclosures or repossessions, implying that almost anything short of that can be pushed through a lender.

  • Subjectively speaking, the sales manager sounded very “buyer friendly” which could lead conjecture towards muted sales.

This conversation suggests Lifted Trucks is actively steering even heavily distressed credit profiles into auto loans, likely via a mix of local credit unions and specialized lenders that “work with them,” enabling high loan‑to‑value structures for subprime low‑FICO borrowers. Such behavior is consistent with late‑cycle dealerships that rely on increasingly risky customers to keep unit volume and gross profit high as affordability deteriorates and used‑vehicle pricing normalizes.

The above is consistent with a dealership increasingly dependent on near‑ and deep‑subprime financing to sustain unit volume into a deteriorating auto credit cycle, not with the kind of resilient, through‑the‑cycle cash flow profile implied by Monroe’s near‑par valuation of the loan.

For MRCC, this creates a tension between the 11%+ yield it collects on the senior secured facility and the rising embedded default risk at the dealership level. If delinquencies and charge‑offs spike among Lifted Trucks’ customer base, the borrower’s ability to service Monroe’s loan could erode far faster than the near‑par fair value marks currently imply.

We believe Monroe does not have the ability to accurately forecast macro/micro economic trends before exposing investors to downstream credit risk as evidenced by Monroe’s Lifted Trucks underwriting which can be best understood as a late‑cycle, pro‑cyclical bet that has aged poorly under even basic stress testing and forward economic modeling.

Read our next article on MRCC’s holdings here:

​

IMPORTANT DISCLOSURES & DISCLAIMER (READ CAREFULLY)

This publication is provided for general informational and educational purposes only and reflects the author’s opinions as of the date of publication, which are subject to change without notice. Nothing herein is, or should be construed as, investment advice, legal advice, accounting advice, tax advice, or a recommendation or solicitation to buy, sell, or hold any security or other financial instrument; readers should consult their own licensed professionals and conduct independent due diligence.​

All statements of fact are intended to be drawn from publicly available materials believed to be reliable at the time of writing (including, where cited, SEC filings, court pleadings, motions, bankruptcy dockets, press releases, news reporting, and municipal/public records). To the extent this publication quotes, summarizes, or characterizes allegations from lawsuits, administrative proceedings, regulatory actions, or other disputes, such allegations are unproven unless and until established by a final adjudication, and the defendants deny wrongdoing as reflected in the cited record.​

Any references to “fraud,” “scheme,” “predatory,” “coercive,” “usurious,” “improper,” “misconduct,” “incompetence,” or similar terms are used, if at all, only as shorthand for allegations described in cited proceedings or as non-literal rhetorical or analytical commentary, and not as a statement of proven criminal or civil liability. Readers should not interpret any discussion of incentives, motive, intent, knowledge, control, or causation as a factual assertion of any person’s state of mind; such discussion reflects the author’s opinion and inference based on the public record cited.​

Estimates, projections, scenarios, and forward-looking statements (including those regarding valuation, rent/lease economics, NAV implications, merger outcomes, portfolio performance, credit metrics, or “risk”) are inherently uncertain and may differ materially from actual results; such statements are provided for illustrative purposes only. Numerical figures may be rounded, derived, or modeled and could contain errors; readers are encouraged to verify all figures independently.​

The author and publisher disclaim any duty to update this publication. By reading this publication, you agree that the author and publisher shall not be liable for any losses, damages, or claims arising from the use of, reliance on, or inability to use the information hereto.

Positions / conflicts: The author’s positions (if any) may change at any time, including before or after publication, without notice. Unless expressly stated otherwise in this publication, the author does not hold any fiduciary relationship with readers.

As part of routine investigative due diligence, First Strike Investigators may contact companies and other market participants to evaluate publicly observable business practices, customer-facing representations, and operational risk indicators. Any such outreach is intended solely to understand general processes, stated underwriting criteria, and downstream risk profiles, and is not intended to obtain non-public information, to interfere with any business relationship, or to induce reliance by the contacted party.

Contact: outreach@firststrike.trading

This one of my longer articles, and if you’re serious about taking money from the uninformed suckers on Kalshi then equipping yourself with knowledge, strategy, and capital is imperative to dominating this new frontier. As such, I highly recommend you read this entire article.

This article will explore how these contracts actually work under the hood: how contracts are born or ‘minted’, how liquidity flows through the system, how markets settle (Kalshi literally flips a switch), what happens when outcomes are disputed, and a bonus section on the struggling U.S. Government regulator assigned to this sector!

Let’s start with the fundamental unit: the contract.

Unlike traditional stock markets where you’re buying a slice of ownership in a company, Kalshi deals in binary outcomes. Every market poses a simple yes-or-no question: “Will the Fed raise rates in Q1?” or “Will Bitcoin close above $100,000 on December 31st?”

Each contract is worth exactly $1 if the answer is “Yes” and $0 if the answer is “No.” And that’s it. No dividends, no earnings calls, no quarterly reports. Just a 1 or a 0.

The price of these contracts fluctuates between the 1 and the 0 by one cent and ninety-nine cents. A “Yes” contract trading at $0.65 doesn’t just represent a price—it represents the market’s collective probability estimate that the event will occur. In short, traders are saying “there’s a 65% chance this happens.”

This creates a remarkably intuitive trading environment. If you think the market is underpricing an outcome (say, it’s trading at $0.30 but you believe there’s a 50% chance), you buy contracts and profit if you’re right. Your maximum profit per contract is $0.70 (you paid $0.30, you receive $1.00), and your maximum loss is your initial stake of $0.30. Using my machine learning, and data sampling I have routinely found mispriced contracts to capitalize on. I plan on having two tiers of data access for both paid, and un-paid subscribers. So either way, subscribe now so you don’t miss this important market intelligence. Paid subscribers will also get automatic access to the First Strike Discord where you can talk to me, and other members of the team directly.

Subscribe now

Yes? Or, No?

What confuses new traders is Kalshi doesn’t actually have two independent contracts—a “Yes” contract and a “No” contract—trading against each other. Instead, there’s a single contract for each market, and you can either go “long” (betting Yes) or go “short” (betting No). It is literally two sides of the same coin and when it settles only one can be right.

It is important to note you can not short contracts in the sense you can short-sell a stock. Shorting a stock has a maximum possible return of 100%, while buying a “No” contract at 5 cents has a potential return of roughly 1,900%.

This is mechanically similar to a futures market, but with a crucial difference. Futures markets require margin and collateral, which gets complicated when prices move against you. Kalshi has absolutely zero counter-party risk. No margin calls, and no naked (non-collateralized) positions that can lead you to financial ruin.

Traders pay money to open a position, and traders pay money again to close it by taking the opposing side. The price you pay to open a Yes position is always (1 - price of No), and vice versa. This ensures every position is fully funded at all times, eliminating counterparty risk.

Market Making

On Kalshi there are literal market makers who make money on the bid-ask spread of contracts while maintaining outcome neutral positions. Kalshi refers to them as liquidity providers (LPs.) To avoid confusion with the actual making of the contract markets (which Kalshi does.)

Markets like: What will be the most played Spotify Christmas Song? Or What X Company say on their earnings call? Has to literally be made by Kalshi.

The lifecycle from idea to market creation begins in one of two places: a suggestion from a Kalshi member through their market suggestion form, or an idea from Kalshi’s internal team.

But having an idea is only the first step. Before any market can go live, it must pass through a rigorous approval process involving Kalshi’s internal team where they are allowed to “self-certify” and submit the certification to regulators. It is important to note, markets can not be created to trade on terrorism, war, or other criminal adjacent events. Every new market must comply with the Commodity Exchange Act and associated regulations.

First, there must be a clear, unambiguous resolution source. When the market asks “Will inflation exceed 3%?”, exactly which inflation measure are we talking about? CPI? Core CPI? Which month’s reading? These details must be spelled out in the contract specifications before trading begins.

Second, the outcome must be verifiable through publicly available data. Kalshi can’t create markets that resolve based on private information or subjective judgments.

Once approved, markets are literally made and “listed” and trading can begin. The market receives a unique ticker symbol, an expiration date, a settlement source, and detailed rules that govern exactly how the outcome will be determined.

IT IS VERY IMPORTANT YOU READ THE RULES OF ALL THE CONTRACTS YOU TRADE.

The Liquidity Question

Now for the perpetual challenge in prediction markets: liquidity.

Say you want to buy 100 contracts betting that the Democrats will win the House. You submit your order at $0.55. But there’s no one on the other side willing to sell at that price. Your order sits on the books, unfilled, until someone comes along who disagrees with you enough to take the opposite position.

A quick reminder on contract minting:

You need a counter party believe the Democrats have a 45% percent chance of NOT winning the house. Your counter party would literally put their money where their mouth is by placing a buy-to-open NO contract at .45 cents—and thus, a contract is born! You are long that the democrats win the house at .55 cents, and your counterparty is short at .45 cents. Both of your cents together, mints a complete $1.00 contract.

Confused? Leave a comment and I’d be happy to help.

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Then comes the liquidity gap that traditional stock exchanges have spent decades solving through market makers—professional firms that continuously quote both buy and sell prices, earning small profits from the bid-ask spread while ensuring that regular traders can always find a counterparty. Kalshi has attacked this problem from multiple angles.

Kalshi operates an affiliate called Kalshi Trading LLC that actively trades on the exchange and provides liquidity across many contracts. This is explicitly disclosed—and Kalshi Trading is subject to the same eligibility requirements and rules as any other liquidity program participant.

This approach has drawn some criticism (isn’t the exchange competing with its own customers? Including, a lawsuit in New York state.), but it addresses the problem: new markets need initial liquidity to attract traders, but traders won’t come without liquidity. Kalshi Trading can seed markets with quotes, getting the flywheel spinning.

Institutional Market Makers

In April 2024, Susquehanna International Group (SIG) became Kalshi’s first dedicated institutional market maker. This was a significant milestone—SIG is one of the largest options market makers in the world, with sophisticated quantitative infrastructure.

The mechanics work similarly to traditional exchanges. Market makers commit to maintaining quotes within certain spread parameters for certain percentages of the trading day. In exchange, they receive benefits like reduced fees, different position limits, and enhanced access to the platform.

The Liquidity Incentive Program

Perhaps the most interesting development is Kalshi’s recent Liquidity Incentive Program, which pays regular traders for providing liquidity. This isn’t market making in the traditional sense—it’s an incentive structure that rewards anyone who maintains “resting orders” that improve market quality.

The program takes snapshots of the order book every second and calculates a “Liquidity Provider Score” based on how much qualifying size you’re maintaining at competitive prices. Your share of the reward pool is proportional to your contribution.

The math gets complicated (the CFTC filing includes formulas that could make a graduate student cry), but the essence is straightforward: if you’re willing to post bids and offers near market prices, Kalshi will pay you for it. Some traders have started treating this as a standalone income stream, forming small teams to quote systematically across markets.

Settlement: Kalshi Literally Flips a Switch

When a market reaches its expiration date, trading halts and the resolution process begins. This is where the rubber meets the road—where abstract probability becomes cold hard cash.

The process unfolds in distinct phases.

Market Closure

First, trading simply stops at the determined expiration time. No more orders can be placed. Whatever positions you’ve got, you got.

Resolution (Determination)

Next comes the determination phase, where Kalshi’s team checks the outcome against the predefined settlement source. If the market asked “Will Bitcoin close above $100,000 on December 31st?”, someone at Kalshi is checking the designated data source to see what Bitcoin’s closing price was.

This step can take anywhere from one hour to more than twelve hours after market closure. The timeline depends entirely on when the source data becomes available. A market based on a government inflation report won’t resolve until that report is published.

Settlement

A few hours after determination, settlement occurs. This is the “switch flip” moment—the system automatically processes all positions and transfers funds accordingly.

From Kalshi’s API documentation, the mechanics are clean: “Yes outcome: Yes contract holders receive $1 per contract. No outcome: No contract holders receive $1 per contract. Net position: Only net positions are settled (after netting).”

The settlement message includes the market result, the quantities settled, and the collateral changes. If you were holding 100 Yes contracts and the market resolved Yes, you receive $100. If it resolved No, you receive $0.

Request to Settle

Interestingly, Kalshi allows members to submit a “Request to Settle” for markets where the outcome is obvious but settlement hasn’t occurred yet. If a basketball game ended hours ago and the score is clear, you can flag the market for expedited resolution.

Disputed Markets

Most markets resolve cleanly—the Fed raised rates or it didn’t, the movie won the Oscar or it didn’t. But what happens when the outcome is genuinely ambiguous?

This is where Kalshi’s rulebook and the dispute resolution procedures come into play.

First, every market has a detailed “Rules Summary” section that specifies exactly how edge cases will be handled. What if the scheduled event is postponed? What if the data source publishes a preliminary number that later gets revised? These contingencies are (in theory) addressed before trading begins.

Remember that Kalshi is regulated by the CFTC as a Designated Contract Market (DCM.) Traders have recourse through regulatory channels if they believe the exchange has violated its rules or acted improperly.

In practice, the most contentious situations involve interpretation of contract language. Did “significant policy announcement” include that particular press release? Did “closes above $X” mean at 11:59:59 PM or at the first millisecond of midnight? These debates can get heated in community discussions, and Kalshi has sometimes faced criticism for resolution calls that traders found counterintuitive.

The key protection is transparency—the settlement source and resolution criteria are public before you trade.

A Regulator in Turmoil

And now we arrive at perhaps the most important and under-appreciated factor shaping Kalshi’s future: the current state of its regulator.

The Commodity Futures Trading Commission is in disarray. Following Supreme Court decisions that opened the door to broader federal workforce cuts, the CFTC began laying off staff in July 2025. Over two dozen employees have been affected across enforcement, market oversight, administration, and data divisions.

This comes on top of earlier reductions. In February 2025, about a dozen probationary employees were terminated, including attorneys in enforcement and market oversight. Voluntary resignation programs have further shrunk the workforce.

As of fiscal year 2025, the CFTC had 636 full-time equivalent staff positions—already a relatively small agency overseeing markets worth over $500 trillion. That number is now meaningfully smaller.

More critically, the leadership vacuum is severe. Only two commissioners remain: Acting Chair Caroline Pham and Commissioner Kristin Johnson. The agency is supposed to have five commissioners. Former Chair Rostin Behnam stepped down at the beginning of the year, and Commissioners Summer Mersinger and Christy Goldsmith Romero resigned in the summer. Recent guidance couldn’t even go through normal Federal Register publication procedures because “currently, there is no majority vote of the Commission.”

What does this mean for Kalshi? Several things.

Reduced Enforcement Capacity

With fewer enforcement attorneys and a reorganized enforcement division, the CFTC’s ability to scrutinize prediction market activities is diminished. The CFTC has the power to block contracts it deems “contrary to the public interest,” but it has never actually used this power. With reduced staff, the bar for intervention rises even higher.

A More Permissive Regulatory Environment

Acting Chair Pham, a Republican, has rolled out changes that signal a lighter-touch approach. The agency’s Division of Enforcement issued an advisory introducing a “mitigation credit matrix” for self-reporting and cooperation—essentially, a more formalized path to reduced penalties for companies that work with regulators.

The CFTC’s Voluntary Retreat

Perhaps most tellingly, in May 2025 the CFTC voluntarily dismissed its appeal in the landmark Kalshi litigation over congressional control contracts. This appeal had been pending before the D.C. Circuit Court of Appeals, challenging a district court ruling that favored Kalshi.

By dropping the appeal, the CFTC left the favorable district court decision intact. As one legal analysis noted: “The CFTC’s dismissal leaves the district court’s pro-Kalshi decision intact and unchallenged, meaning unless and until reversed by another court or through regulatory action, Kalshi’s political event contracts are lawful under the Commodity Exchange Act.”

The agency also canceled a planned public roundtable on event contracts that was scheduled for April 2025. The cancellation raised questions about the CFTC’s direction and openness to stakeholder input on prediction markets.

State vs. Federal Friction

Meanwhile, states have stepped into the regulatory void with their own views on whether Kalshi’s products constitute illegal gambling. Nevada’s Gaming Control Board issued a cease-and-desist order (Kalshi won a preliminary injunction). Maryland ordered Kalshi to stop offering sports contracts (the court denied Kalshi’s injunction). Massachusetts’ Attorney General sued Kalshi for operating an illegal sports wagering operation.

The central legal question remains unresolved: Are these event contracts fundamentally financial hedging products, or are they a new form of gambling? Federal preemption arguments have prevailed in some courts but not others, creating a patchwork of conflicting rulings.

The Bottom Line

Kalshi represents something genuinely new in American financial markets—a regulated venue where anyone can trade on the probability of real-world events. The mechanics are elegant: binary contracts, automatic settlement, and a clearing infrastructure that eliminates counterparty risk.

But the system’s integrity ultimately rests on two pillars: clear rules that traders can trust, and a regulator with the capacity to enforce them.

Right now, the first pillar is holding. Kalshi’s contract specifications, settlement procedures, and dispute mechanisms are public and reasonably well-defined. Market makers are providing liquidity. The infrastructure works.

The second pillar is wobbling. A CFTC with two commissioners, reduced staff, and uncertain leadership priorities is not a regulator positioned to police aggressive expansion or address novel concerns. The voluntary dismissal of the congressional control appeal suggests an agency that may be stepping back from prediction market oversight entirely.

For traders, this creates both opportunity and risk. The opportunity is in a platform that’s expanding rapidly into new markets with reduced regulatory friction. The risk is that you’re trading in an environment where the rules might matter less than they should—where disputes might not receive serious regulatory attention, where market integrity concerns might go unaddressed, and where the line between financial product and sports bet remains deliberately blurred.

As always in markets, the edge goes to those who understand not just the mechanics, but the incentives of everyone involved. Now you know both.

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THIS IS NOT INVESTMENT ADVICE

THE CONTENTS OF THIS ARTICLE DO NOT CONSTITUTE INVESTMENT ADVICE, AN OFFER, SOLICITATION, OR RECOMMENDATION TO BUY OR SELL ANY SECURITIES OR FINANCIAL INSTRUMENTS WHATSOEVER.

All information provided hereto is for informational and educational purposes only and reflects solely the author’s personal views as of the stated publication date. The information has not been tailored to the individual circumstances, investment objectives, or financial condition of any reader, and should not be relied upon as the basis for any investment, trading, or other financial decision.

Readers are expressly advised to undertake their own independent research and consult with appropriate licensed financial, legal, tax, and/or investment professionals before engaging in any securities or financial transactions. Neither the author nor this publication, nor any of their affiliates, agents, or representatives, accept any liability whatsoever for any direct, indirect, or consequential losses arising from the use of this content or reliance on any opinions stated herein. Neither the author nor this publication act in an advisory, fiduciary, or professional capacity to any party by virtue of distributing this material.

PAST PERFORMANCE IS NOT INDICATIVE OF FUTURE RESULTS. THE INFORMATION CONTAINED HEREIN IS PROVIDED “AS IS” WITHOUT WARRANTY OF ANY KIND.

THIS IS NOT INVESTMENT ADVICE

These prediction markets are so novel that you can easily find mispriced contracts if you’re willing to do the legwork. Professional quant shop algorithms haven’t fully arrived yet. The NYSE has evolved into one of the world’s most efficient markets where billions of dollars and thousands of Harvard PhDs compete over a modicum of alpha, all while prediction markets are still in that infancy phase where a motivated trader doing solid research can easily beat the crowd. Finding the edge in these markets is still time consuming—on my profitable trades I’m spending at least 60 to 90 minutes on research atop running it through my machine learning algo.

I plan on sharing future research where I have spotted criminally underpriced contracts after back-testing them in my own proprietary algorithm and machine learning system. I plan on releasing this intelligence to both free and paid subscribers, with paid subscribers getting significantly more insight, and on more contracts.

My recommendations will be complete with the Kelly Criterion so you don’t bet the farm. If you’re interested in receiving this level of market intelligence subscribe, so you don’t miss it!

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What Are Event Contracts?

Event contracts are financial instruments that settle based on the outcome of a future event. Unlike traditional securities that derive value from company performance, event contracts are tied to direct, verifiable occurrences: Will x candidate win an election? What will the inflation print be? Will a movie make over $100 million in its premier weekend?

Typically, event contracts trade between $0.01 and $0.99 (or $0 and $100, depending on the platform), with the price representing the market’s implied probability of the event occurring. A contract trading at $0.65 suggests the market believes there’s a 65% chance the event will happen. If the event occurs, the contract pays $1; if it doesn’t, it pays $0.

How it works —

Event contracts function through continuous double-sided-auction markets, similar to stock exchanges. There’s an order book where buyers and sellers submit bid/ask orders, and trades execute when prices match. Market makers often provide liquidity, ensuring you can enter or exit positions without moving the market significantly. They make money off the bid-ask spread such as they do in traditional equity markets.

What makes event contracts particularly interesting is their self-correcting nature. When a contract’s price diverges from the true probability of an event, skilled traders can profit by buying the mispricing. In theory this should push prices toward accuracy over time, making prediction markets more often than not effective forecasting tools.

However, this efficiency varies dramatically across markets. Well-established markets with high liquidity(pictured above) and numerous informed participants tend to be quite efficient. Illiquid, emerging markets, by contrast, often contain significant inefficiencies—and that’s where the opportunities lie.

Even highly liquid markets are often times mispriced — take for example a team with a three touch-down lead in the fourth quarter. I found a 6% edge in this scenario just the other weekend. My machine learning stated the team had a 96% chance of winning, while the WIN-YES contracted was trading at a mere 90c (or 90% chance.)

Why Emerging Markets Matter —

Emerging markets in the prediction space are those with limited liquidity, fewer participants, or novel event types that haven’t been extensively traded before. These markets are gold mines for skilled traders because they haven’t yet been picked over by high frequency algorithms.

Consider the difference between betting on a presidential election versus a municipal ballot measure in a mid-sized city. The presidential race will attract thousands of informed traders, political analysts, and substantial capital. The municipal measure might only interest a handful of local residents and political junkies. The presidential market will likely be quite efficient; the municipal market could be wildly mispriced.

Strategies

Information Asymmetry Exploitation

The most straightforward strategy in these emerging event markets is leveraging information advantages. This doesn’t mean insider trading or accessing non-public information it means doing research that other market participants haven’t done.

For a contract on whether a specific legislation will pass, you might read the actual bill, understand the committee composition, track procedural requirements, and follow relevant legislators on social media. Most casual traders will rely on news headlines or superficial analysis. Deeper understanding creates an edge. I have seen this first hand in the comment sections of some contracts where “traders” are only doing one to two weeks of back research.

Early Market Entry

Emerging markets are often most inefficient when they first open. Initial prices may be set somewhat arbitrarily, and early trading volumes are low. Savvy traders who spot these markets quickly can establish positions before efficiency sets in.

This requires monitoring new market listings across platforms and being prepared to act decisively. When you spot a newly listed contract where you have insight or can quickly develop an informed view, early entry can capture value before the crowd arrives. I am presently working on a notification bot, which will notify you when new markets post. Again, this will be a perk for paid subscribers.

Contrarian Positioning in Narrative-Driven Markets

Emerging markets are particularly susceptible to narrative-driven mispricing. When a compelling story takes hold, prices can overshoot rational probability estimates. This creates opportunities for contrarian traders willing to bet against the crowd.

Imagine a contract on whether a hyped startup will reach a billion-dollar valuation within a year. Media coverage might create euphoria, pushing the contract price to $0.85. But a sober analysis of venture funding trends, the company’s actual metrics, and comparable cases might suggest the true probability is closer to 40%. The contrarian takes the “no” side of that bet.

This strategy requires emotional discipline. You’re betting against what “everyone knows,” which feels uncomfortable. But markets often confuse compelling narratives with probable outcomes, especially in emerging spaces where participants lack historical reference points.

Correlation and Portfolio Approaches

Sophisticated traders in emerging event markets don’t just bet on individual contracts—they construct portfolios that exploit correlations between events. If Contract A and Contract B both pay out in similar scenarios but are priced as if they’re independent, you can structure positions to profit from this mispricing. I plan on also using my machine learning system to create ‘baskets’ of contracts with significant edges.

For example, contracts on “Company X will launch Product Y in Q1” and “Company X will report strong Q1 earnings” are clearly correlated. If the first contract is trading high but the second is trading low, there may be an arbitrage opportunity. This is admittedly hard to find as Kalshi as market makers aggressively buy these contracts up.

Building these portfolios requires thinking probabilistically and understanding how different events relate to each other. It’s more complex than simple binary bets, but the rewards can be substantial in markets where these relationships aren’t yet priced in.

Managing Risk in Emerging Markets

The flip side of opportunity is risk, and emerging event markets carry several unique hazards. Liquidity risk is real and you may not be able to get a fill on your positions. Resolution risk exists when there’s ambiguity about how a contract should settle. You must CAREFULLY read the resolution rules of the market you’re trading in. And of course, there’s fundamental risk that your analysis is simply wrong.

Position sizing is your primary defense. Never bet so much on an emerging market that you can’t afford to lose the entire position. Diversification across multiple uncorrelated events helps manage overall portfolio risk. And always understand the resolution criteria before entering a position—nothing’s worse than being “right” about an outcome but losing because the contract settles on a technicality you didn’t anticipate. If you read the comments of contracts on Kalshi you’ll notice most traders aren’t even reading the rules.

Looking forward —

As prediction markets mature and regulatory frameworks evolve, we’re likely to see an explosion of new event types and markets. Climate metrics, AI milestones, space exploration achievements, and countless other domains will spawn tradable contracts. Each new category represents an emerging market with potential inefficiencies to exploit.

The traders who develop systematic approaches to identifying and analyzing these opportunities—who build knowledge in specific domains and learn to spot mispricing patterns—will have sustained advantages as the ecosystem grows.

Again, I plan on starting a series where I will start identifying these mispriced markets based on my own proprietary algorithm and machine learning system.

The best (legal) broker in the U.S. is Kalshi. Use my referral code and we’ll both get 10 dollars! If you can’t afford a subscription, but still find this research possible please sign up with my referral link:

https://kalshi.com/sign-up/?referral=cad6157a-053e-4ad0-8051-caf6ed0f06f9

THIS IS NOT INVESTMENT ADVICE

THE CONTENTS OF THIS ARTICLE DO NOT CONSTITUTE INVESTMENT ADVICE, AN OFFER, SOLICITATION, OR RECOMMENDATION TO BUY OR SELL ANY SECURITIES OR FINANCIAL INSTRUMENTS WHATSOEVER.

All information provided hereto is for informational and educational purposes only and reflects solely the author’s personal views as of the stated publication date. The information has not been tailored to the individual circumstances, investment objectives, or financial condition of any reader, and should not be relied upon as the basis for any investment, trading, or other financial decision.

Readers are expressly advised to undertake their own independent research and consult with appropriate licensed financial, legal, tax, and/or investment professionals before engaging in any securities or financial transactions. Neither the author nor this publication, nor any of their affiliates, agents, or representatives, accept any liability whatsoever for any direct, indirect, or consequential losses arising from the use of this content or reliance on any opinions stated herein. Neither the author nor this publication act in an advisory, fiduciary, or professional capacity to any party by virtue of distributing this material.

PAST PERFORMANCE IS NOT INDICATIVE OF FUTURE RESULTS. THE INFORMATION CONTAINED HEREIN IS PROVIDED “AS IS” WITHOUT WARRANTY OF ANY KIND.

THIS IS NOT INVESTMENT ADVICE