Do agency and stewardship behaviors coexist at firms, or does one dominate the other? We use data from listed companies in China over the period 2007–2016 to show that powerful chief executive officers (CEOs) simultaneously incur self‐interested agency costs while acting as stewards to benefit the firm. In balancing the push‐and‐pull forces of stewardship and agency behaviors, powerful CEOs in Chinese firms ultimately improve short‐term and long‐term firm performance. Our results have important implications for understanding how CEOs affect firms and how cultural factors can motivate CEOs to work in the interest of the firm.
The economic interaction between the countries of the world is gradually strengthening. Among them, the US stock market is a “barometer” of the global economy, which has a huge impact on the global economy. Therefore, it is of great significance to study the data in the US stock market, especially the data mining algorithm of abnormal data. At present, although data mining technology has achieved many research results in the financial field, it has not formed a good research system for time series data in stock market anomalies. According to the actual performance and data characteristics of the stock market anomaly, this paper uses data mining techniques to find the abnormal data in the stock market data, and uses the isolated point detection method based on density and distance to analyze the obtained abnormal data to obtain its implicit useful information. However, due to the defects of traditional data mining algorithms in dealing with stock market anomalies containing uncertain factors, that is, the errors caused by other human factors, this paper introduces the roughening entropy of the uncertainty data and applies its theory to the field of data mining, a data mining algorithm based on rough entropy in the US stock market anomaly is designed. Finally, the empirical analysis of the algorithm is carried out. The experimental results show that the data mining algorithm based on rough entropy proposed in this paper can effectively detect the abnormal fluctuation of time series in the stock market.
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