An intelligent stock trading decision support system based on rough cognitive reasoning

Abstract

From the perspective of Momentum Investing (MI), more profitable trading opportunities for bullish investors would exist in the stocks occurring with limit-up. Motivated by this, we propose an intelligent stock trading decision support system by using rough cognitive reasoning, based on which stocks with the higher probabilities of rising in the short term after the occurrences of limit-up can be distinguished. Considering financial markets are full of uncertainty and high noise, an extended rough cognitive network (RCN) is established, which is a granular reasoning model based on rough set theory and fuzzy cognitive maps. As a kind of reasoning mechanism, the extended RCN can effectively analyze both the continuous and discrete features of financial data to deal with the uncertainty and inconsistency. Moreover, entropy-based method is involved into the extended RCN model such that the knowledge representation of model can be further improved, and harmony search algorithm is applied for optimization. The proposed model is further applied in Chinese stock market to carry out empirical studies, where the discussion on parameters are implemented and experiment results show the effectiveness and validity of the proposed model.