8.9 Regime-Switching That Works, Factors That Don't (MS-GARCH)
A two-regime MS-GARCH turned 7% buy-and-hold lumber into 158%. The edge was all in the asymmetric variance model. Adding market and behavioral factors made the good versions worse.
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A two-regime MS-GARCH turned 7% buy-and-hold lumber into 158%. The edge was all in the asymmetric variance model. Adding market and behavioral factors made the good versions worse.
The random walk still wins for developed FX. But a stock-return signal beats it for emerging currencies, netting about 7% a year, when it works. The edge is real, and it comes and goes.
The exact P&L of a European trend-follower is a weighted sum of return autocorrelations plus drift squared. Positive long-horizon autocorrelation pays; the story is optional, the sign is not.
A year of Polymarket data, $40M in arbitrage. But almost all of it is plain single-market rebalancing harvested by a few bots during volatility, not the exotic cross-market kind.
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.
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.
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.
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.
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.
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.
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.
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.