Presenting the combined algorithm of machine learning and the combination of risk metrics and fuzzy theory in choosing an investment portfolio

The current research was conducted to find the optimal portfolio for investing in stock exchange stocks, and one of the methods that is currently very popular among analysts and researchers in this field is methods based on artificial intelligence, followed by methods aimed at reducing risk metrics. The aim of the current research is to form a portfolio using machine learning methods, risk measurement and its combination with fuzzy theory, which has a better return than the average return of the market. The output of each method is entered into the random forest algorithm and prediction is made by this algorithm, and in the last step, the prediction output is entered into the value-at-risk and value-at-risk optimization model with the fuzzy theory approach to form the capital portfolio. Shares information is daily and its time period is from the beginning of 2014 to the middle of 2018. At the end of each of these methods and steps, it was compared with the real return of the market. the CVAR risk measure has a better ability than the VAR risk measure, and the random forest algorithm among the used machine learning algorithms has achieved better results in choosing the investment portfolio.