Portfolio Optimization Using Machine Learning Method and Monte Carlo Simulation
DOI:
https://doi.org/10.54097/farx3k44Keywords:
Portfolio Optimization; Monte Carlo Simulation; Machine Learning; Linear Regression; Risk Management; Sharpe Ratio.Abstract
Investment portfolio optimization is a crucial aspect of quantitative finance, aiming to maximize returns and minimize risks. This study focuses on optimizing investment allocation among five selected stocks (BAIC Blue Valley, BYD, Chang’ an Automobile, Kweichow Moutai, Sunwoda) over 100 days using Monte Carlo simulation and machine learning methods. This study uses the sliding window approach with the linear regression model to predict future returns and calculate the variance-covariance matrix to determine the optimal portfolio weights, leading to two key strategies: maximizing the Sharpe ratio and minimizing the risk. The results reveal that the Max Sharpe Ratio portfolio achieved a cumulative return of 0.0909, significantly outperforming the CSI 300 index’s return of -0.0348. Additionally, the Min Risk portfolio exceeded the market index with a cumulative return of 0.0062. These findings demonstrate that both the Max Sharpe Ratio and Min Risk strategies are effective in achieving a balance between risk and return and surpassing the market, offering valuable insights for investors seeking optimized portfolio allocations.
Downloads
References
[1] Portfolio optimization-based stock prediction using long-short term memory network in quantitative trading. Applied Sciences, 2020, 10(2): 437. DOI: https://doi.org/10.3390/app10020437.
[2] Mittal S, Bhattacharya S, Mandal S. Characteristics analysis of behavioural port-folio theory in the Markowitz portfolio theory framework[J]. Managerial Finance, 2022, 48(2): 277-288. DOI: https://doi.org/10.1108/MF-05-2021-0208.
[3] James G, Witten D, Hastie T, et al. Linear regression. In: An Introduction to Sta-tistical Learning: With Applications in Python. Cham: Springer International Publishing, 2023: 69-134.
[4] Khan I am, Akber A, Xu Y. Sliding Window Regression Based Short-Term Load Forecasting of a Multi-Area Power System[J]. Ithaca: Cornell University Library, arXiv.org, 2019. DOI:10.48550/arxiv.1905.08111
[5] Norwawi N M. Sliding window time series forecasting with multilayer perceptron and multiregression of COVID-19 outbreak in Malaysia[J]. Data Science for COVID-19, 2021: 547-564. DOI: 10.1016/B978-0-12-824536-1.00025-3.
[6] Poornima B G, Reddy Y V. An Analysis of Portfolio VaR: Variance-Covariance Ap-proach[J]. IUP Journal of Applied Finance, 2017, 23(3).
[7] Albuquerque P H M, de Moraes Souza J G, Kimura H. Artificial intelligence in portfo-lio formation and forecast: Using different variance-covariance matrices[J]. Commu-nications in Statistics - Theory and Methods, 2021, 52(12): 4229–4246. DOI:10.1080/03610926.2021.1987472.
[8] Pav S E. The Sharpe Ratio: Statistics and Applications[M]. Chapman and Hall/CRC, 2021.
[9] Vinzelberg A, Auer B R. A comparison of minimum variance and maximum Sharpe ra-tio portfolios for mainstream investors[J]. The Journal of Risk Finance, 2022, 23(1): 55-84.
[10] Boubaker S, Le T D Q, Manita R, et al. The trade-off frontier for ESG and Sharpe ratio: a bootstrapped double-frontier data envelopment analysis[J]. Ann Oper Res, 2023. https://doi.org/10.1007/s10479-023-05506-z.
[11] Siswanah E. Comparative analysis of mean variance efficient frontier and resampled efficient frontier for optimal stock portfolio formation[C]//IOP Conference Series: Materials Science and Engineering. IOP Publishing, 2020, 846(1): 012065.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
