Assessing the Determinants of Corporate Risk-Taking Using Machine Learning Algorithms.

Given that risk-taking is an essential channel for companies to obtain high returns and realize value enhancement, the goal of this study is to holistically explore the determinants of corporate risk-taking using various machine learning algorithms. Based on the data from Chinese listed companies between 2010 and 2019, we document that the adaptive boosting (AdaBoost) model makes better predictions of corporate risk-taking. We further visualize the importance and influence of the firm basic characteristics, firm performance, and chief executive officer (CEO) characteristics and discover that in the AdaBoost model, the firm basic characteristics, and performance factors, such as the firm's fixed asset investments, size, and return on equity, are important in predicting corporate risk-taking, while CEO characteristics are less important. Finally, the role of variables in corporate risk-taking varies among large and small enterprises. Overall, our findings deepen the comprehension of what drives corporate risk-taking and provide a potential way for real-world firms seeking to adjust their risk-taking level.