Improving Earnings Predictions and Abnormal Returns with Machine Learning

What is it about?

Using use stepwise logit regression, Ou and Penman (1989) predicts the sign of future earnings changes and uses these predictions to form a profitable hedge portfolio. Dramatic increases in computing power and recent advances in machine learning allow us to extend Ou and Penman (1989) using a larger dataset, more computer intensive forecasting algorithms, and modern prediction models. We find that stepwise logit continues to provide good out-of-sample predictions and can be used to form a trading strategy that generates small abnormal returns, but a nonparametric machine learning technique (random forest) significantly improves out-of-sample forecast accuracy and trading strategy returns. Our results confirm the Ou and Penman (1989) finding that financial statement information can be useful for investment decisions, and suggest that recent nonparametric machine learning techniques could be useful in a variety of accounting contexts where predictions of binary outcomes are needed.

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