The Application of Monte Carlo Simulation and Mean-variance in Portfolio Selection
DOI:
https://doi.org/10.54691/bcpbm.v26i.2089Keywords:
Portfolio; Stock; Monte Carlo simulation; Mean-variance; Efficient frontierAbstract
This paper manages to construct an efficient portfolio that has a better return than the market index. This portfolio is unique because its five assets have been carefully picked from different industries to diversify risk, which is rarely seen in other research. First, this article uses Monte Carlo simulation to generate a possible portfolio and calculate the weight of each asset in different scenarios. Second, it uses the mean-variance model to measure the risk and return of the portfolio. Based on the accessed data, this study finds that the daily return of the minimum volatility portfolio outruns the market index, and it also determines the weight of each stock in the minimum volatility portfolio. The paper provides feasible ways of constructing an efficient portfolio, such as how to select stocks and allocate assets. The results in this paper may benefit the related investors in financial markets and help them find efficient portfolio that outruns the market index.
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References
Fama, E. F., & Macbeth, J. D. (1973). Risk, return, and equilibrium: empirical tests. Journal of Political Economy, 81(3), 607-636. DOI: 10.1086/260061
French, K. R., Schwert, G. W., & Stambaugh, R. F. (1987). Expected stock returns and volatility. Journal of Financial Economics, 19(1), 3-29. DOI: 10.1016/0304-405X(87)90026-2
Markowitz, H. M. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91. DOI: 10.1017/S0020268100019831
Brinson, G. P., Hood, L. R., & Beebower, G. L. (1986). Determinants of portfolio performance. Financial Analysts Journal, 42(4), 39-44. DOI: 10.2469/faj.v42.n4.39
Dijkstra, T. (2001). Where is the Efficient Frontier?. DOI: 10.13140/RG.2.2.14386.40640.
Sen, R. , Gupta, P. and Dey, D. (2016) High Dimensionality Effects on the Efficient Frontier: A Tri-Nation Study. Journal of Data Analysis and Information Processing, 4, 13-20. DOI: 10.4236/jdaip.2016.41002.
Merritt, D & San, D. (2000). Portfolio Optimization using Efficient Frontier Theory. DOI: 10.2118/59457-MS.
Mehrjoo, S & Jasemi, M & Mahmoudi, A. (2013). A new methodology for deriving the efficient frontier of stocks portfolios: An advanced risk-return model. Journal of Artificial Intelligence and Data Mining. 2. 113-123. DOI: 10.22044/jadm.2014.305.
Incorporating time into efficient frontier. Available online: https://www.financialplanningassociation.org/article/journal/MAY11-incorporating-time-efficient-frontier (Accessed on July 13, 2021)
Luo, C & Wu, D. (2016). Environment and economic risk: An analysis of carbon emission market and portfolio management. Environmental research. DOI: 149. 10.1016/j.envres.2016.02.007
Reboredo, J. (2013). Modeling EU allowances and oil market interdependence. Implications for portfolio management. Energy Economics, 36, 471–480. DOI:10.1016/j.eneco.2012.10.004
Bilenko, D., Lavrov, R., Onyshchuk, N., Poliakov, B., & Kabenok, Y. (2019). The normal distribution formalization for investment economic project evaluation using the monte carlo method. Montenegrin Journal of Economics, 15. DOI: 10.14254/1800-5845/2019.15-4.12
J Korytárová, & Barbora Pospíilová. (2015). Evaluation of investment risks in cba with monte carlo method. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 63(1), 245-251. DOI: 10.11118/actaun201563010245
Bednarek, Z, & Patel, P. (2017). Understanding the outperformance of the minimum variance portfolio. Finance Research Letters. DOI: 24. 10.1016/j.frl.2017.09.005.






