Before there was a Medallion Fund, before Renaissance Technologies existed as a name, there was a piece of mathematics almost nobody outside a narrow academic circle had heard of, and a mathematician who had never set out to solve a market problem. The mathematician's name was Leonard Baum. Leonard Baum spent his career on a problem that sounds abstract to the point of uselessness: how do you infer the hidden state of a system when all you can see is a noisy, partial signal generated by it? Working through the 1960s alongside colleagues like Lloyd Welch, Baum helped develop what's now called the Hidden Markov Model — a mathematical framework for figuring out what's probably happening beneath the surface of data, using only the messy, incomplete observations that surface produces. It was built for speech recognition and codebreaking, not stock charts. And that's precisely why it mattered so much when Jim Simons brought Baum into the fold. The Insight Nobody in F...
Every series has to land somewhere. This is where ours does. Over the past several posts, one man's approach to markets kept circling back to the same handful of ideas — expressed differently each time, but never really changing. Before wrapping up, it’s worth tying those threads together. Data over drama. The market rewards what's tested, not what's compelling. A good story about why a stock moved is entertainment, not information — the discipline was always in trusting what decades of data showed over what felt true in the moment. Patterns over predictions. Nobody forecasted the future. The edge came from finding relationships in data robust enough to repeat, then trusting them to keep repeating — without needing to explain why. Process over conviction. The hardest discipline wasn't building a good model — it was refusing to override it. Instinct is a handful of vivid memories dressed up as wisdom; a tested system has absorbed thousands of scenarios no gut fe...