Ensemble methods

Financial decision-making

Ensemble methods have also been applied to corporate failure and bankruptcy prediction. Studies comparing different ensemble constructions (such as bagging, boosting and heterogeneous classifier pools) report that well-tuned ensembles tend to achieve higher classification accuracy and more robust performance across industries than individual models. The accuracy of prediction of business failure is a very crucial issue in financial decision-making. Therefore, different ensemble classifiers are proposed to predict financial crises and financial distress. Also, in the trade-based manipulation problem, where traders attempt to manipulate stock prices by buying and selling activities, ensemble classifiers are required to analyze the changes in the stock market data and detect suspicious symptom of stock pricemanipulation.