Establishing a Dynamic Capital Structure Model for Company Sustainability Performance Using Data Mining Techniques

In order to reconsider the changes of adjustment speed caused by the recapitalization cost, this research adopted dynamic capital structure theory with adjustment speed as one of the independent variables to analyze the relationship between capital structure and company performance. Instead of applying the commonly used regression models, this research used the decision tree C4.5 algorithm and association rules of priori algorithm. Taking the predictive models created by the decision tree as the main result and supporting it with association rules which help to explain the relationships between capital structure and company performance, this research shows how capital structure influences company performance. As the result presents, a company tends to have better performance when its debt ratio is low, and Tobin’s Q and ROA will turn worse as the ratio gets higher. However, maybe because of the financial leverage, ROE will not decrease when the ratio is high but will increase instead. In addition, this research found out that adjustment speed is negatively related to company performance, meaning that even though a company is more flexible in adjusting itself, it might still perform badly since it is deviating from its optimum leverage. This research found that not only capital structure, but other variables such as price-earnings ratio, research and development expense ratio, and dividend payout ratio also determine a company’s performance.