Fundamental Analysis via Machine Learning

We examine the efficacy of machine learning in a central task of fundamental analysis, forecasting corporate earnings. We find that machine learning models not only generate significantly more accurate and informative out-of-sample forecasts than the state-of-the-art models in the literature, but also compete well against analysts’ consensus forecasts. The superior performance appears attributable to the ability of machine learning models to uncover new information through identifying economically important predictors and capturing nonlinear relationships. The new information uncovered by machine learning models is of considerable economic value to investors. It has significant predictive power with respect to future stock returns, with stocks in the most favorable new information quintile outperforming those in the least favorable quintile by over 70 bps per month. The overall results suggest that machine learning technology provides an efficient way of extracting value-relevant information from financial statements.