Pillar
Edge is a compensated exposure discovered by ranking a cross-section, not a forecast of one series. You sort every name by a characteristic, go long the top and short the bottom, and ask whether the spread's return is a real risk premium, a behavioral mispricing, or a ghost that dies out of sample.
Factor investing runs on one equation: expected return equals alpha plus beta times lambda. Beta is exposure, lambda is the premium, alpha is the leftover. The market is factor #1, and CAPM fails on its own.
You never see factor exposure. So you rank stocks on a proxy, go long the top decile and short the bottom, and test the spread. Deciles, a t-stat, and a monotonicity check, from scratch.
Fama-MacBeth gets you the factor premium in two passes, even for untradable factors. But the second pass uses estimated betas, so skipping the Shanken correction inflates every t-stat.
Alpha is the gap between what a portfolio earned and what your model predicts. The GRS test grades all those gaps at once, and the usual verdict is blunt: you are missing a factor.
ML promises to triple your Sharpe. Bryan Kelly, who builds the models, says expect 20%. Headline numbers die under fair tests, 166% turnover, and alpha trapped in tiny illiquid stocks.
Test 316 factors at a t of 2 and about 16 are pure luck. The factor zoo is a multiple-testing failure: raise the bar to t above 3, expect a 36% out-of-sample haircut, but don't prune to five.
Regional models beat global, said 20 years of linear studies. Redone with neural nets across 24 markets, it flips: complex models want global data. The global NN hits 0.74%/mo, t=5.73.
The |t|>3 rule is right for testing one factor, wrong for building a portfolio. A book of 18,000 signals, 80% noise, beats the strict filter because diversification pays where selection does not.
Every factor model, from Fama-French to a neural net, is one equation: the SDF. Same skeleton, but the choice of characteristics and weighting function swings out-of-sample Sharpe from 0.45 to 3.4.
Value's 55% drawdown looked like death. But the structural premium stayed positive; the loss was the value-growth spread hitting the 100th percentile. That is a repricing, and repricings revert.
Value, momentum, volatility, and sentiment timing all lost to a plain equal-weight factor basket in China. Theory says timing is huge; estimation error eats it. Trust the plateau, not the peak.
Alternative data buys a short-dated edge, and Dessaint shows the tax: short-horizon accuracy rises while long-horizon accuracy falls. It is a lease, not a purchase, and the crowd rents it too.
Prospect theory turns broken psychology into two factors: a TK score of how attractive a stock looks and capital-gains overhang. The prettiest stocks pay least, and the spread runs 1.24% a month.
Two stocks end the month flat, one by recovering, one by fading. The shape between the endpoints predicts next month: low-convexity stocks beat high-convexity by 0.84%/mo, and no factor explains it.
Trade an asset off the momentum of everything it's linked to, not its own. A learned cross-asset graph delivers a 1.51 Sharpe, and the alpha lives in the links between asset classes.