Stock picking is the field of financial analysis that is of particular interest for many professional investors and researchers. In this study stock picking is implemented via binary classification trees. Optimal tree size is believed to be the crucial factor in forecasting performance of the trees. While there exists a standard method of tree pruning, which is based on the cost-complexity tradeoff and used in the majority of studies employing binary decision trees, this paper introduces a novel methodology of nonsymmetric tree pruning called Best Node Strategy (BNS). An important property of BNS is proven that provides an easy way to implement the search of the optimal tree size in practice. BNS is compared with the traditional pruning approach by composing two recursive portfolios out of XETRA DAX stocks. Performance forecasts for each of the stocks are provided by constructed decision trees. It is shown that BNS clearly outperforms the traditional approach according to the backtesting results and the Diebold-Mariano test for statistical significance of the performance difference between two forecasting methods.
Decision tree stock picking pruning earnings forecasting data mining