We Dramatically Improved our Machine Learning Model to Price Kalshi Earnings Contracts. It Went 11/13.

The new model combines historical frequency data with AI narrative analysis to find mispriced Kalshi contracts. Here's how it performed.

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.

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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.

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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.