“Prediction markets strip away the noise that makes backtested Sharpe ratios a dangerous illusion”
Traces the quantitative finance toolkit from backtesting (Deflated Sharpe Ratio, combinatorial purged cross-validation) through factor models, Black-Litterman portfolio optimization, Bayesian regime detection, and machine learning, then argues each technique transfers directly to prediction markets. The core claim is that prediction markets are the purest testing environment for investment theory because binary resolution eliminates the unobservable noise that obscures strategy quality in traditional finance. Uses LMSR's mathematical identity with the softmax function to bridge quant finance and prediction market pricing.
Extensive technical background assumed
Platforms mentioned: Polymarket