Developing a new equally weighted portfolio strategy using different risk measures: An empirical evidence from S&P 500 index

Document Type : Original Article

Authors

Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.

Abstract
Purpose: Diversification is an essential component of risk management in the investment field. Among the various methods at hand, the establishment of an equal-weight stock portfolio is widely acknowledged as a simple and efficient strategy. This research aims to introduce a new approach based on the equal-weighted stock portfolio strategy to enhance its efficiency in managing risk and making financial decisions.
Methodology: The proposed approach integrates the equal-weight stock portfolio strategy with risk measurement and evaluation tools. By employing common risk measures from the investment management literature, stocks are first evaluated and screened. The remaining stocks are then used to form an investment portfolio using the equal weight portfolio strategy. The risk measures utilized in this research encompass variance, standard deviation, semi standard deviation, value at risk, conditional value at risk, entropic value at risk, drawdown at risk, conditional drawdown at risk, and entropic drawdown at risk.
Findings: To evaluate the performance of the proposed approach, an experimental case study is conducted using monthly historical data of S&P 500 index symbols. The results are compared with those obtained using the traditional equal-weight stock portfolio formation approach. The empirical findings of this study carry practical implications for investors and investment fund managers.
Originality/Value: This research contributes to the field by offering an innovative perspective on stock screening and investment portfolio formation, which can also serve as a valuable measurement criterion.

Keywords

Subjects


 
[1] Ghanbari, H., Lerni Foik, A. M., Eskorouchi, A., & Mohammadi, E. (2022). Investigating the effect of US dollar, gold and oil prices on the stock market. Journal of future sustainability, 2(3), 97–104.
[2] Lerni Foik, A. M., Ghanbari, H., Bagheriyan, M., & Mohammadi, E. (2022). Analyzing the effects of global oil, gold and palladium markets: Evidence from the Nasdaq composite index. Journal of future sustainability, 2(3), 105–112.
[3] Lerni Foik, A. M., Ghanbari, H., Sajjadi, S. J., & Mohammadi, E. (2022). Investigating the effectiveness of the nasdaq index on the global oil, gold and palladium markets. The second international conference on optimization of production and service systems, Guilan, Iran. (In Persian). Civilica. https://civilica.com/l/94348/
[4] Barro, R. J. (1991). Economic growth in a cross section of countries. The quarterly journal of economics, 106(2), 407–443.
[5] Mendoza, E. G., & Yue, V. Z. (2012). A general equilibrium model of sovereign default and business cycles. The quarterly journal of economics, 127(2), 889–946.
[6] Leduc, S., & Liu, Z. (2016). Uncertainty shocks are aggregate demand shocks. Journal of monetary economics, 82, 20–35.
[7] Barber, B. M., & Odean, T. (2013). The behavior of individual investors. In Handbook of the economics of finance (Vol. 2, pp. 1533–1570). Elsevier.
[8] Lerni Foik, A. M., Ghanbari, H., Sadjadi, S. J., & Mohammadi, E. (2024). Behavioral finance biases: a comprehensive review on regret approach studies in portfolio optimization. International journal of industrial engineering, 35(1), 1–23.
[9] Haratemeh, M. H. (2021). Portfolio optimization and random matrix theory in stock exchange. Innovation management and operational strategies, 2(3), 257-267. (In Persian). https://www.journal-imos.ir/article_139087_4a8d671abaa173e27cc4109fc85d2b0e.pdf
[10] Ricciardi, V. (2008). The psychology of risk: The behavioral finance perspective. In Behavioral finance: investors, corporations, and markets (pp. 131–145). John Wiley & Sons.
[11] Eskorouchi, A., Ghanbari, H., & Mohammadi, E. (In Press). A scientometric analysis of robust portfolio optimization. Iranian journal of accounting, auditing and finance. (In Persian). https://ijaaf.um.ac.ir/article_44518_a096fd2fe1bb26040b808752b0edb27a.pdf
[12]  Ghanbari, H., Safari, M., Ghousi, R., Mohammadi, E., & Nakharutai, N. (2023). Bibliometric analysis of risk measures for portfolio optimization. Accounting, 9(2), 95–108.
[13]  Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. The journal of finance, 55(4), 1705–1765.
[14]  Fama, E. F., & French, K. R. (2012). Size, value, and momentum in international stock returns. Journal of financial economics, 105(3), 457–472.
[15]  Yan, X., & Zheng, L. (2017). Fundamental analysis and the cross-section of stock returns: A data-mining approach. The review of financial studies, 30(4), 1382–1423.
[16] Markowitz, H. M. (1991). Portfolio selection: efficient diversification of investments. Wiley.
[17] Ghanbari, H., Shabani, M., & Mohammadi, E. (2023). Portfolio optimization with conditional drawdown at risk for the automotive industry. Automotive science and engineering, 13(2), 4236-4242. (In Persian). https://www.sid.ir/paper/1136165/en
[18] Kettering, R. C. (2008). International stock market diversification. Allied academies international conference. academy of accounting and financial studies. proceedings (Vol. 13, p. 35). Jordan Whitney Enterprises, Inc.
[19] Lerni Foik, A. M., Ghanbari, H., Shabani, M., & Mohammadi, E. (2024). Bi-objective portfolio optimization with mean-cvar model: an ideal and anti-ideal compromise programming approach. In Progressive decision-making tools and applications in project and operation management: approaches, case studies, multi-criteria decision-making, multi-objective decision-making, decision under uncertainty (pp. 69–79). Springer.
[20] Heydarzadeh, H., Rahnamay Roodposhti, F., Rashidi Komijan, A., & Najafi, S. E. (In Press). Portfolio formation based on risk-adjusted performance and distribution-based returns using data envelopment analysis. Journal of decisions and operations research. (In Persian). https://www.journal-dmor.ir/article_189301.html
[21] DeMiguel, V., Garlappi, L., & Uppal, R. (2009). Optimal versus naive diversification: How inefficient is the 1/N portfolio strategy? The review of financial studies, 22(5), 1915–1953.
[22] Kirby, C., & Ostdiek, B. (2012). It’s all in the timing: simple active portfolio strategies that outperform naive diversification. Journal of financial and quantitative analysis, 47(2), 437–467.
[23] Bessler, W., Opfer, H., & Wolff, D. (2017). Multi-asset portfolio optimization and out-of-sample performance: an evaluation of Black–Litterman, mean-variance, and naïve diversification approaches. The european journal of finance, 23(1), 1–30.
[24] Black, F., & Litterman, R. (1992). Global portfolio optimization. Financial analysts journal, 48(5), 28–43.
[25] Silva, L. P. da, Alem, D., & Carvalho, F. L. de. (2017). Portfolio optimization using mean absolute deviation (MAD) and conditional value-at-risk (CVaR). Production, 27. https://www.scielo.br/j/prod/a/ZXKqrbw58tzwGGzhNpcnyGB/?lang=en&format=html
[26] Michaud, R. O., & Michaud, R. O. (2008). Efficient asset management: a practical guide to stock portfolio optimization and asset allocation. Oxford University Press.
[27] Yang, D., Yu, M., & Zhang, Q. (2009). Downside and drawdown risk characteristics of optimal portfolios in continuous time. In Handbook of numerical analysis (Vol. 15, pp. 189–226). Elsevier.
[28] Bellini, F., Klar, B., Müller, A., & Gianin, E. R. (2014). Generalized quantiles as risk measures. Insurance: mathematics and economics, 54, 41–48.
[29] Rockafellar, R. T., & Uryasev, S. (2002). Conditional value-at-risk for general loss distributions. Journal of banking & finance, 26(7), 1443–1471.
[30] Hao, B., Wang, J., & Zhu, J. (2019). A fast linearized alternating minimization algorithm for constrained high-order total variation regularized compressive sensing. IEEE access, 7, 143081–143089.