Equity Factor Timing from Deep Neural Networks

Can enhanced machine learning models accurately time popular equity factors? In their January 2024 paper entitled “Multi-Factor Timing with Deep Learning”, Paul Cotturo, Fred Liu and Robert Proner explore equity factor timing via a multi-task neural network model (MT) to capture the commonalities across factors and a dynamic multi-task neural network model (DMT) to extract financial and macroeconomic states. They attempt to time six well-known factors: (1) excess market return, size, value, profitability, investment and momentum. They employ 272 model inputs (123 macroeconomic and 149 financial) to predict each month:

1. The sign of next-month return for each factor.
2. The return for an equal-weighted portfolio that holds the factors (the risk-free asset) for factors with positive (negative) predicted returns.

The compare performances of MT and DMT with those of seven simpler off-the-shelf machine learning models: logistic regression (LR), penalized logistic regression (EN), random forest (RF), extremely randomized trees (XRF), gradient boosted trees (GBT), support vector machine (SVM) and feed-forward neural network (NN). For all models, they use the first 20 years of their sample period for training, the next five years for validation and the remaining years for out-of-sample testing. Their benchmark is an equal-weighted portfolio of all six factors. Using monthly data for the 272 model inputs and monthly returns for the six factors during January 1965 through December 2021, with out-of-sample testing starting January 1990, they find that: (more…)