Is It Possible to Earn Abnormal Return in an Inefficient Market? An Approach Based on Machine Learning in Stock Trading

Abstract

Risk management and stock investment decision-making is an essential topic for investors and fund managers, especially in the context of the COVID-19 pandemic. The problem becomes easier if the market is efficient, where stock prices fully reflect potential risk. Nevertheless, if the market is not efficient, investors may have an opportunity to find an effective investment method. Vietnam is one of the emerging markets; the efficiency is still weak. Thus, there will be an opportunity for astute investors. This study aims to test the weak-form efficient market and provide a modern approach to investors' decision-making. To achieve that aim, this study uses historical data of stocks in the VN-Index and VN30 portfolio to buy and sell within a one-day period under the rolling window approach to test the Ho Chi Minh City Stock Exchange (HoSE) through a runs test and to perform stock trading using the support vector machine (SVM) and logistic regression. The buying/selling of stocks is guided by the forecasted outcomes (increase/decrease) of logistic regression and SVM. This study adjusted the return rate in proportion to the risks and compared it with index investments of VN-Index and VN30 to evaluate investment efficiency. The test results dismissed the weak-form efficient-market hypothesis, which opens up many opportunities for short-term traders. This study's primary contribution is to provide a stock trading strategy for short-term investors to maximize trading profits. Because logistic regression and SVM have proven effective trading methods, investors can use them to achieve abnormal returns.