In the existing literature on corporate governance，most of the research related to managerial characteristics has two main limitations. First，most of the papers focus on the relationship between one managerial individual characteristic and corporate performance but lack a comprehensive understanding of the potential non-linear relationship and interactions among some of the important independent variables. Second，existing research tests casual inference but ignores the predictive performance of the model. In this paper，we first examine if managerial individual characteristics can predict corporate performance by using a machine learning approach: Boosting regression trees. Using a sample of listed firms in the Chinese A-share market from 2008 to 2016，we study whether these individual characteristics could predict corporate performance. The evidence shows that: 1) The individual characteristics of Chinese executives including CEOs and chairmen could predict corporate performance only to a limited degree. 2) Among multiple individual characteristics，managerial ownership and executive age are the two most important predictors of corporate performance. 3) The relations between predictors and corporate performance are non-linear，consistent with the prior literature. This paper initiates a new，more thorough perspective in Chinese executive research using machine learning methods and has important implications for selecting executives and designing incentive mechanisms.