The Role of Fundamental Factors in Optimal Stock Portfolio Formation Using the Fama–French Five-Factor Model with an Emphasis on Data Mining: Evidence from Pharmaceutical Companies Listed on the Tehran Stock Exchange

Reza  Khosravi; Jamshid Peikfalak; Hassan  Fattahi Nafchi

Authors

Reza Khosravi Department of Accounting, Shk.C., Islamic Azad University, Shahrekord, Iran Author https://orcid.org/0000-0002-0802-2429
Jamshid Peikfalak Department of Accounting, WT.C., Islamic Azad University, Tehran, Iran https://orcid.org/0009-0008-1883-8086
Hassan Fattahi Nafchi Department of Accounting, University of Isfahan, Isfahan, Iran Author https://orcid.org/0000-0001-6702-4195

Keywords:

Fundamental Factors, Optimal Stock Portfolio, Fama–French Five-Factor Model

Abstract

The purpose of the present study was to determine the role of fundamental factors in optimal stock portfolio formation using the Fama–French five-factor model with an emphasis on data mining. The statistical population consisted of companies listed on the Tehran Stock Exchange during the 2012–2023 period. To achieve the research objectives and construct an optimal stock portfolio, dimensionality-reduction approaches, Data Envelopment Analysis (DEA), Support Vector Machine (SVM), and clustering algorithms were employed. Balance-sheet financial ratios, income-statement financial ratios, cash-flow-statement financial ratios, composite financial ratios, and risk and return measures based on the Fama–French five-factor model were used as model inputs for constructing four portfolios. The findings indicated that the support vector machine method and the fourth approach, which comprised the composite model, demonstrated superior performance in stock portfolio optimization. These findings can assist investors and stock analysts in identifying appropriate financial ratios for optimal portfolio formation while achieving an appropriate balance between return and risk.

References

[1] H. Vakilifard and M. Vakilifard, Financial Management, 10th ed. Jangal Publications, 2015.

[2] S. M. A. Shohadai, Fundamental Analysis in the Capital Market, 2nd ed. Tehran: Chalesh Publishing House, 2008.

[3] A. Pahlavan, E. Ramezanpour, and M. H. Gholizadeh, "Prioritizing Factors Affecting Stock Selection in Tehran Stock Exchange Using Fuzzy Network Analysis Process," presented at the Third Conference on Financial Mathematics and Applications, 2012.

[4] A. R. Keyghabadi, Z. Lashgari, and A. Shabani, "Ranking the Top Listed Companies for Portfolio Selection Using AHP Technique: Case Study of the Top 50 Companies of Tehran Stock Exchange," presented at the Second National Conference on Applied Research in Management and Accounting Sciences, 2014.

[5] S. Mehrani, F. Ghior, and T. Bahramfar, "Examining the Relationship between Traditional Liquidity Ratios and the Ratios Obtained from the Cash Flow Statement in Order to Evaluate the Continuity of Companies' Activity," Accounting and Auditing Reviews, vol. 12, no. 2, pp. 3-17, 2014.

[6] W. D. Cook and L. M. Seiford, "Data Envelopment Analysis (DEA) - Thirty Years On," European Journal of Operational Research, vol. 192, pp. 1-17, 2014, doi: 10.1016/j.ejor.2008.01.032.

[7] S. Khajawi, A. Ghiori Moghadam, and M. Ghafari, "The Data Envelopment Analysis Technique Is a Supplement to the Traditional Analysis of Financial Ratios," Financial Accounting and Audit Reviews, vol. 17, no. 6, pp. 41-56, 2019.

[8] S. Khajawi and A. Ghiori Moqdeh, "Data Envelopment Analysis, a Method for Choosing the Optimal Portfolio According to the Amount of Stock Liquidity," Accounting Developments, vol. 4, no. 2, pp. 27-52, 2013.

[9] E. Pätäri, T. Leivo, and S. Honkapuro, "Enhancement of Equity Portfolio Performance Using Data Envelopment Analysis," European Journal of Operational Research, vol. 220, no. 2, pp. 786-797, 2012, doi: 10.1016/j.ejor.2012.02.006.

[10] A. C. Tarnaud and H. Leleu, "Portfolio Analysis with DEA: Prior to Choosing a Model," Omega, vol. 75, pp. 57-76, 2017, doi: 10.1016/j.omega.2017.02.003.

[11] A. Aggarwal, A. Gupta, R. Verma, and R. Kumari, "DEA Based Fuzzy Portfolio Evaluation Models Integrated with TOPSIS Techniques to Rank the Efficient Portfolios under Different Risk Indicators," 2023.

[12] H. A. Heydarzadeh, F. Rahnamee Roudposhti, A. Rashidi, and S. E. Najafi, "Portfolio Formation Based on Efficiency Derived from Risk and Distribution-Based Returns with Data Envelopment Analysis Approach," Decision Making and Operations Research, vol. 9, no. 2, pp. 289-304, 2024.

[13] M. Guidolin, G. Panzeri, and M. Pedio, "Machine Learning in Portfolio Decisions," in "BAFFI CAREFIN Centre Research Paper," 2024. [Online]. Available: https://ssrn.com/abstract=4988124

[14] M. Munawir and U. S. Sulistyawati, "Stock Portfolio Analysis With Machine Learning Algorithmic Approach for Smart Investment Decisions," International Journal Software Engineering and Computer Science (Ijsecs), vol. 4, no. 3, pp. 860-870, 2024, doi: 10.35870/ijsecs.v4i3.2606.

[15] S. Heratizadeh and F. Rezaei, "Presenting a New Method for Utilizing Machine Learning in the Process of Portfolio Optimization," Decision Making and Operations Research, vol. 9, no. 4, pp. 968-984, 2023.

[16] A. Chaweewanchon and R. Chaysiri, "Markowitz Mean-Variance Portfolio Optimization with Predictive Stock Selection Using Machine Learning," International Journal of Financial Studies, vol. 10, no. 3, p. 64, 2022, doi: 10.3390/ijfs10030064.

[17] A. Chaher, "Optimizing portfolio selection through stock ranking and matching: A reinforcement learning approach," Expert Systems with Applications, vol. 269, p. 126430, 2025, doi: 10.1016/j.eswa.2025.126430.

[18] P. Gupta, M. K. Mehlawat, and A. Saxena, "Hybrid Optimization Models of Portfolio Selection Involving Financial and Ethical Considerations," Knowledge-Based Systems, vol. 37, pp. 318-333, 2013, doi: 10.1016/j.knosys.2012.08.014.

[19] Z. Wang, "Active vs. Passive Investment in the Post-Pandemic U.S. Stock Market: A Sharpe Ratio-Based Portfolio Optimization Compared to the S&P 500," Highlights in Business Economics and Management, vol. 61, pp. 41-45, 2025, doi: 10.54097/j6kd1r14.

[20] A. Mousizadeh, J. Ramazani, M. Ali Akbari, M. Safarigrayeli, and R. Rezaiyan, "Portfolio optimization with adjusted risk degree of stocks based on performance measurement model," Investment Knowledge, vol. 15, no. 58, pp. 23-42, 2024, doi: 10.30495/jik.2025.23818.

[21] R. J. Oben, M. Seraj, and Ş. Z. Eyüpoğlu, "Volatility and Return Spillovers Among US Traditional Technology Stocks, Decentralized Finance Instruments And conventional Cryptocurrencies: Implications for Portfolio Optimization," Review of Behavioral Finance, vol. 17, no. 5, pp. 807-834, 2025, doi: 10.1108/rbf-12-2024-0377.

[22] N. Shahbazi and S. Barkhordari, "Optimizing a portfolio comprising selected stocks and cryptocurrencies," Budget and Finance Strategic Research, vol. 6, no. 2, pp. 11-35, 2025.

[23] M. Eskandari, A. Mohseni, and M. Ghasemi, "The Effect of Behavioral Biases on Portfolio Optimization of Investors in the Tehran Stock Exchange: An Approach Based on Modern Behavioral Finance," Accounting, Finance, and Computational Intelligence, pp. 1-17, 2026.

[24] S. Nourollahi, A. Jafari, and A. Pakmaram, "The Impact of Investor Sentiment on Portfolio Optimization in the Tehran Stock Exchange and Cryptocurrency Markets," Business, Marketing, and Finance Open, pp. 1-21, 2025.

[25] V. Mousavi Kakhki and S. Khatabi, "Presenting a stock portfolio optimization model based on behavioral preferences and investor memory," 2024. [Online]. Available: https://civilica.com/doc/1961759.