Probability Investing
Independent equity research built around expected value. Rather than projecting cash flows or comparing multiples, each thesis identifies the specific decisions that will determine a stock's price, assigns sourced probabilities to each outcome, and calculates a weighted expected value against the current price.
Written by a former Morgan Stanley and Motley Fool analyst with a Wall Street Journal byline. Every probability is sourced and every call is logged in public.
The process
01
Identify the key decisions
The events that will actually move the price.
02
Source a prior
Prediction markets, options, or base rates. No guessing.
03
Build the outcome tree
Each node combination gets a price target. Probabilities sum to 100%.
04
Calculate expected value
Weighted EV above current price is the buy case.
Open theses
Positions pre-registered in public. This table fills out as each call resolves.
| Ticker | Key decision | Direction | Opened | Status |
|---|---|---|---|---|
| MSFT | OpenAI concentration risk reprices the stock | Long | Jun 2026 | Open |
| FIW | $744B infrastructure gap gets priced in | Long | May 2026 | Open |
| SMTC | $8B valuation unsupported by $50M LPO revenue | Bearish | Feb 2026 | Open |
Full reasoning and outcome trees for each thesis are published on Seeking Alpha.
Free
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The full method as a PDF: how I source priors, build outcome trees, and calculate EV. You'll also get a periodic newsletter covering new pamphlets and books, plus market commentary when it's relevant.
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