Noise-Canceling Finance: How the Information Bottleneck Tames Overfitting in Asset Pricing
TL;DR for operators Most quant teams already know the awkward truth: adding model capacity often makes the research backtest look smarter while making the deployed model less useful. The interesting part of Che Sun’s paper is not that it adds another neural network to asset pricing. We have enough of those. The useful move is more surgical: it asks the factor model to keep the information that helps explain returns and discard the information that merely helps memorise noisy firm characteristics.1 ...