The detrimental effects of insider trading on the financial markets and the economy are well documented. However, resource-constrained regulators face a great challenge in detecting insider trading and enforcing insider trading laws. We develop a text analytics framework that uses machine learning to predict ex-ante potentially opportunistic insider trading, using actual insider trading allegation by shareholders as the proxy, from corporate textual disclosures. Distinct from typical black-box neural network models, which have difficulty tracing a prediction back to key features, our approach combines the predictive power of deep learning with attention mechanisms to provide interpretability to the model. Further, our model utilizes representations from a business proximity network and incorporates the temporal variations of a firm’s financial disclosures. The empirical results offer new insights into insider trading and provide practical implications. Overall, we contribute to the literature by reconciling performance and interpretability in predictive analytics. Our study also informs the practice by proposing a new method for regulators to examine a large amount of text in order to monitor and predict financial misconduct.

充分记录了内幕交易对金融市场和经济的不利影响。但是，资源受限的监管机构在检测内幕交易和执行内幕交易法律方面面临巨大挑战。我们开发了一个文本分析框架，该框架使用机器学习来预测事前潜在机会主义的内幕交易，以股东实际的内幕交易指控为代表，来自公司文本披露。与典型的黑匣子神经网络模型（难于将预测追溯到关键特征）不同，我们的方法将深度学习的预测能力与注意力机制结合在一起，为模型提供了可解释性。此外，我们的模型利用了业务邻近网络的表示，并结合了公司财务披露的时间变化。实证结果为内幕交易提供了新的见识，并提供了实际的启示。总体而言，我们通过协调预测分析中的性能和可解释性为文献做出了贡献。