Portfolio Optimization Based on Markowitz Investment Theory and Monte Carlo Simulation

Abstract

In the current global economic recovery, the market still has a certain degree of volatility, in the case of volatility or bad market how to go to the portfolio and optimize it is a very critical task. In this paper, based on Markowitz’s investment theory, Monte Carlo algorithm is applied to achieve portfolio optimization, which is verified by the dataset of five A-share data selected as a representative dataset in the period of global epidemic. For the model and the algorithm, good results are achieved for the objective market, and the market conditions are successfully represented; for the portfolio optimization, the high-risk and highreturn portfolio with the highest Sharpe ratio is finally found, and compared with the portfolio with minimized risk, and the portfolio with the highest Sharpe ratio is concluded as the optimal portfolio for this experiment, which is the optimal portfolio for this experiment, and should not be pursued for low risk but will be the best portfolio for this experiment in the turbulent and downturn market. In a volatile and depressed market, it should not be pursued for low risk but will result in a loss of return, and it should be maximized with a certain degree of volatility.