Machine Learning of Economically Sensitive Industries without Data-Mining Bias for Domestic Equity Management

Abstract

We proposed a method to reduce the data-mining bias[1] caused by the complexity of machine learning models and to increase their readability and transparency of the models, considering an investment situation in financial business as an example. By investment simulations with real financial data, we could confirm that our proposed method improved investment performance compared with the original random forest[2] even though decision rules were simplified and their number of them was reduced by our proposed method.