Out‐of‐sample predictability of firm‐specific stock price crashes: A machine learning approach
用机器学习方法预测公司特定股价崩盘，发现随机梯度提升模型优于逻辑回归和随机森林，结合财务与文本数据能显著提升预测性能。
Abstract We use machine learning methods to predict firm‐specific stock price crashes and evaluate the out‐of‐sample prediction performance of various methods, compared to traditional regression approaches. Using financial and textual data from 10‐K filings, our results show that a logistic regression with financial data inputs performs reasonably well and sometimes outperforms newer classifiers such as random forests and neural networks. However, we find that a stochastic gradient boosting model systematically outperforms the logistic regression, and forecasts using suitable combinations of financial and textual data inputs yield significantly higher prediction performance. Overall, the evidence suggests that machine learning methods can help predict stock price crashes.