Improving Earnings Predictions and Abnormal Returns with Machine Learning
用随机森林等机器学习方法扩展Ou和Penman（1989）的研究，发现随机森林显著提升盈利预测准确率和交易策略收益，证实财务报表信息对投资决策有用。
SYNOPSIS Using stepwise logit regression, Ou and Penman (1989) predicts the sign of future earnings changes and uses these predictions to form a profitable hedge portfolio. Increases in computing power and advances in machine learning allow us to extend Ou and Penman (1989) using more data, computer intensive forecasting algorithms, and modern prediction models. Stepwise logit still provides good predictions and can be used to form a trading strategy that generates small abnormal returns, but random forest significantly improves forecast accuracy and returns. The models identify different variables as being important for prediction in high tech and manufacturing, but this does not lead to better predictions or higher returns. Results confirm Ou and Penman's (1989) finding that financial statement information is useful for investment decisions, and suggest that machine learning techniques can be useful in a variety of accounting contexts.