Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
Series Vol. 50 , 01 December 2023
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Portfolio Optimization by Monte Carlo Simulation
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Abstract
In this paper, Monte Carlo simulation is used for constructing Efficient Frontier and optimizing the portfolio. Then the performance of the optimized portfolio had been evaluated and compared to the performance of the whole market, Firstly, this study collected the closing prices of five stocks in different industries that was listed in New York stock exchange between 2023/01/01 and 2023/04/12. Secondly, to testify if the construction of the portfolio can possibly mitigate the volatility, the correlation coefficient between these chosen stocks has been calculated. Then, Monte Carlo simulation has been used to construct the Efficient Frontier and find the weights of Maximum Sharpe Ratio portfolio and Minimum Variance portfolio. Lastly, this study put the market price data between 2023/03/12 and 2023/04/12 into the portfolios which had been built in the last step. The returns were compared to the S&P 500 subsequently. As the results shows, the Maximum Sharpe Ratio portfolio is performed better than S&P 500, Minimum Variance portfolio is performed worse than S&P 500. The results of this paper show the performance of these two portfolios compare to the market, which may help investors to decide which strategy to use when it comes to constructing a portfolio.
Keywords
portfolio optimization, monte carlo simulation, S&P500
References
1. Ivanova, M., and Dospatliev, L. (2017) Application of Markowitz portfolio optimization on Bulgarian stock market from 2013 to 2016. International Journal of Pure and Applied Mathematics, 117(2), 291-307.
2. Kresta, A., and Slova, K. (2011) Solving cardinality constrained portfolio optimization problem by binary particle swarm optimization algorithm. Department of Mathematical Methods in Economics, Faculty of Economics, VŠB-Technical University of Ostrava, Sokolská třída, 33(701), 21.
3. Zanjirdar, M. (2020) Overview of portfolio optimization models. Advances in mathematical finance and applications, 5(4), 419-435.
4. Elbannan, M. A. (2015) The capital asset pricing model: an overview of the theory. International Journal of Economics and Finance, 7(1), 216-228.
5. Shah, C. A. (2015) Construction of optimal portfolio using sharpe index model & camp for bse top 15 securities. International Journal of Research and Analytical Reviews, 2(2), 168-178.
6. Marling, H., & Emanuelsson, S. (2012) The Markowitz portfolio theory. Journal of Financial Risk Management, 8(2), 1-6.
7. Wang, J. (2000) Mean-variance-VaR based portfolio optimization. Valdosta State University.
8. Harrison, R. L. (2010) Introduction to monte carlo simulation. In AIP conference proceedings, 1204(1), 17-21
9. Chaweewanchon, A., and Chaysiri, R. (2022) Markowitz Mean-Variance Portfolio Optimization with Predictive Stock Selection Using Machine Learning. International Journal of Financial Studies, 10(3), 64.
10. Kuo, R.J., and Hong, C.W. (2013) Integration of Genetic Algorithm and Particle Swarm Optimization for Investment Portfolio Optimization. Applied Mathematics & Information Sciences, 7, 2397-2408.
Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
- ISBN (Print)
- 978-1-83558-147-6
- ISBN (Online)
- 978-1-83558-148-3
- Published Date
- 01 December 2023
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/50/20230568
- Copyright
- 01 December 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited