Comparative Study of Gray and Random Relationship Analysis Methods to Optimize Stock Investment Portfolio In Tehran Stock Market
Subject Areas : Financial Engineering
Reza Adak
1
,
Mehdi Meshki Miavaghi
2
*
,
Mohammad Hassan Gholizadeh
3
1 - Deparetment of Financial Engineering, Ra.C., Islamic Azad University, Rasht, Iran.
2 - Deparetment of Accounting and Finance, Payame Noor University, Tehran, Iran. (
3 - Department of Business Management, Gilan University, Gilan, Iran.
Keywords: Data Envelopment Analysis (DEA), Strategies of Gray Relationship Algorithm (GRA), Stochastic Approaches,
Abstract :
Purpose: One of the critical components of portfolio management is the comparison and ranking of financial assets when deciding on the structure of the portfolio. Quantitative methods serve as powerful tools in this process, aiming to construct an optimal stock investment portfolio with high precision and ease. This study pursues two main objectives: first, to rank stock portfolios using gray relational analysis (GRA); and second, to compare this approach with classical methods referred to in this research as random-based approaches.
Research Methodology: To address the research objectives, 11 weighting and investment strategies were defined under the gray relational analysis method and 2 strategies under the random-based approach. The research population includes all listed companies on the Tehran Stock Exchange, with the sample comprising five leading industries at the start of the study period: investment, chemical, steel and iron, banking and financial institutions, and petroleum products. Based on specific selection criteria, 160 companies were ultimately chosen for analysis. The Mann–Whitney U test was used to test hypotheses, and Data Envelopment Analysis (DEA) was employed to compare the performance of the GRA and random approaches.
Findings: The results indicate that in the gray relational analysis, strategies 7, 9, 10, and 11 (which assign 100% weight to the third and fourth moments) were the most efficient, achieving performance across 186 weeks. Furthermore, DEA comparisons of the GRA with random approaches type I and II showed that the gray relational method outperforms its random counterparts in portfolio efficiency. This finding was statistically validated using the Mann–Whitney U test.
Originality / Value: This research introduces a novel integration of gray relational algorithms, a postmodern technique in financial modeling, and compares it with traditional random approaches. By enhancing accuracy and reducing processing time in identifying optimal stock portfolios, the study contributes a practical and robust framework for intelligent decision-making in portfolio selection.
Adinehvand, D., Razini Rahmani, E.A., Khoddam, M., Ohadi, F., & Hashemizadeh, E. S. (2024). Examining the efficiency of optimization models of multi objective genetic algorithm and particle swarm algorithm under the risk criteria of conditional value at risk and mean smai variance in determining the optimal stock portfolio. Advances in Finance and Investment, 4(4), 65-92. [In Persian]
Bauer Jr, R. J., & Griffiths, M. D. (1988). Evaluating expert system investment: An introduction to the economics of knowledge. Journal of Business Research, 17(2), 223-233.
Benders, J., & Manders, F. (1993). Expert systems and organizational decision-making. Information & management, 25(4), 207-213.
Deng, J. L. (1982). Control problems of grey systems. Systems & control letters, 1(5), 288-294.
Edirisinghe, N. C. P., & Zhang, X. (2008). Portfolio selection under DEA-based relative financial strength indicators: case of US industries. Journal of the operational research society, 59(6), 842-856.
Huang, Y. P., & Yang, H. P. (2004). Using hybrid grey model to achieve revenue assurance of telecommunication companies. Journal of grey system, 7(1), 38-49.
Jalalian, H., & Tsang, E. (2018). An Investigation into the Noisy Portfolio Optimization Problem abstract. Second National Management Conference and Fuzzy Systems.
Khajavi, S., & Ghayuri Moqaddam, A. (2013). DEA Method of Choosing Optimum Portfolio in Accordance with Stock Liquidity: The Case Study of Listed Companies of Tehran Stock Exchange. Journal of Accounting Advances, 4(2) , 27-52. [In Persian]
Lincy, R. M. G., & John, J. C. (2016). A multiple fuzzy inference systems framework for daily stock trading with application to NASDAQ stock exchange. Expert Systems with Applications, 44, 13-21.
Lu, I. J., Lin, S. J., & Lewis, C. (2008). Grey relation analysis of motor vehicular energy consumption in Taiwan. Energy Policy, 36(7), 2556-2561.
Moradi, L. (2012). Investigating road traffic accidents in Fars province using gray system theory (Master Thesis, Shiraz University). [In Persian]
Nurcan, E., & Deniz Köksal, C. (2021). Determination of financial failure indicators by gray relational analysis and application of data envelopment analysis and logistic regression analysis in BIST 100 Index. Interdisciplinary Journal of Management Studies, 14(1), 163-187.
Ramezani, N., & Mokhatab Rafiei, F. (2022). Using grey relational analysis for dynamic portfolio selection in Tehran Stock Exchange. Journal of Industrial and Systems Engineering, 14(18), 31-39.
Silva, N. F., dos Santos, M., Gomes, C. F. S., & de Andrade, L. P. (2023). An integrated CRITIC and Grey Relational Analysis approach for investment portfolio selection. Decision analytics journal, 8, 100285.
Škrinjarić, T., & Šego, B. (2018). Using grey incidence analysis approach in portfolio selection. International Journal of Financial Studies, 7(1).
Škrinjarić, T. (2020). Dynamic portfolio optimization based on grey relational analysis approach. Expert systems with applications, 147, 113207.
Taghizadeh, K., Mullah Alizadeh Zavardehi, S., Salehi, A. K., & Mahmoudi Rad, A. (2022). Evaluation of the optimal portfolio portfolio using market criteria using multi-criteria decision criteria under conditions of uncertanty in the Iranian capital market. Advances in Finance and Investment, 3(6), 101-128. [In Persian]