Application of Artificial Intelligence-Genetic Algorithms to Select Stock Portfolios in the Asian Markets

Abstract

The paper's main goal is to use a genetic algorithm to find the best stock portfolio that meets the criteria of high return and low risk, allowing investors to adjust the appropriate proportion for each share. Using the Python programming language based on the Jupyter Notebook engine, this paper introduces a model of six stock portfolios, each of 30 stocks selected with market capitalization and high liquidity criteria of six markets in the Asian region. The results show that the four portfolios created from the markets of Vietnam, Thailand, Philippines, and Singapore meet both the return and risk objectives. The Malaysian market only meets the risk target, but the portfolio's return is not close to the expected ratio. Meanwhile, the Indonesian market outperformed expectations in terms of profits, but high profits come with high risks, so this market carries a concerning level of risk when compared to the profit and loss of other markets. The suggested stock allocation levels for each portfolio are based on the above results. Finally, the author proposes several policy implications related to the management and operation of the market to limit unnecessary price fluctuations of the stock and affect the business model of companies.