Factor Model Complexity Versus Predictive Power

Are more factors better for predicting stock market returns? In their April 2025 paper entitled “The Limited Virtue of Complexity in a Noisy World”, Álvaro Cartea, Qi Jin and Yuantao Shi analyze the interactions between stock factor noisiness (errors/uncertainties) and factor model complexity in portfolio optimization. Specifically, they study how factor noise affects the predictive power of factor-based return models. They postulate that noise arises from issues such as inconsistent data collection, processing errors or insufficient computing power. Their conclusions derive from three examples:

1. Employ market data to simulate ideal noiseless stock factors, and combine them via ridge regression to predict stock returns.
2. Apply a neural network to actual NYSE/AMEX/NASDAQ data to predict monthly stock returns via a varying number of factor inputs during 1991 through 2023.
3. Use simulated stock factors which have either fixed noise levels or noise levels that increase with model complexity to explore how predictive power varies with noise and complexity. 

Using monthly excess returns for the broad value-weighted U.S. stock market and values for 15 factors used in other studies during 1926 through 2023, they find that: (more…)