Research Article
Application of Monte Carlo Simulation in Calculating the Maximum Sharpe Ratio Based on American Funds
@INPROCEEDINGS{10.4108/eai.18-11-2022.2327143, author={Yiqian Wang and Nan Yang Yang and Qianwei Zhao}, title={Application of Monte Carlo Simulation in Calculating the Maximum Sharpe Ratio Based on American Funds}, proceedings={Proceedings of the 4th International Conference on Economic Management and Model Engineering, ICEMME 2022, November 18-20, 2022, Nanjing, China}, publisher={EAI}, proceedings_a={ICEMME}, year={2023}, month={2}, keywords={sharpe ratio; monte carlo simulation; markowitz's portfolio theory}, doi={10.4108/eai.18-11-2022.2327143} }- Yiqian Wang
Nan Yang Yang
Qianwei Zhao
Year: 2023
Application of Monte Carlo Simulation in Calculating the Maximum Sharpe Ratio Based on American Funds
ICEMME
EAI
DOI: 10.4108/eai.18-11-2022.2327143
Abstract
Markowitz's portfolio theory has been widely used and confirmed in practice. Most rational models of portfolio choice suggest that investors hold diversified portfolios to reduce or eliminate the non-compensated risk, thus getting a higher Sharpe ratio. Based on the data including the daily return rate on the first day of every month from 1968 to 1982 collected from CRSP, using R, this paper verifies the effectiveness of Markowitz's portfolio theory and creatively introduces the method of “Monte Carlo Simulation” into the process of finding the maximum Sharpe ratio of portfolios and evaluating the corresponding weights. This paper finds that portfolios have a higher yield, lower standard deviation and a higher Sharpe ratio than any single fund. Additionally, the maximum Sharpe ratio of the portfolio with T-Bills is higher than the one of portfolio without T-Bills. Therefore, when picking funds in the data set to make an investment, the risk-free assets should be chosen to construct the portfolio. This paper highlights the use of Sharpe ratio and providing a feasible decision-making method for investors. Moreover, this paper analyzes both risk and return factors, which improves the utilization and comprehension of data relevant to yields.