Journal of Quality Engineering and Production Optimization

Journal of Quality Engineering and Production Optimization

Balancing Financial Performance and ESG Objectives in Portfolio Optimization: Insights from the S&P 500

Document Type : 20th IIIE Conference Selected Papers

Authors
Department of Industrial Engineering, Faculty of Engineering, Iran University of Science and Technology, Iran
Abstract
Portfolio optimization is a critical component of informed investment decision-making, balancing risk and return to maximize long-term performance. With increasing focus on sustainability, integrating Environmental, Social, and Governance (ESG) factors into portfolio construction has gained significant importance. This study presents a multi-objective portfolio optimization model that minimizes ESG impact while ensuring financial viability through return and risk constraints. Utilizing the augmented epsilon-constraint method (AECM), the model incorporates ESG considerations without compromising profitability. The analysis is based on a dataset of 20 top market-cap stocks from the S&P 500, evaluated on annual return, risk exposure, and ESG scores. Our findings demonstrate that ESG-conscious portfolios can generate competitive returns while effectively managing risk levels. A sensitivity analysis investigates the trade-off between volatility and profitability, while a correlation heatmap illustrates the relationships between ESG scores and financial metrics. This framework offers a structured methodology for constructing portfolios that are both financially optimal and socially responsible, enabling investors to align their financial goals with sustainable, ethical practices. A theoretical contribution is provided by integrating ESG criteria into a multi-objective optimization framework using the AECM approach. Practically, a replicable method is offered for constructing portfolios that remain both financially viable and socially responsible.
Keywords

Acerbi, C., & Tasche, D. (2002). On the coherence of expected shortfall. Journal of banking & finance26(7), 1487-1503.
Al-Majali, B. H., & Zobaa, A. F. (2025). Analyzing bi-objective optimization Pareto fronts using square shape slope index and NSGA-II: A multi-criteria decision-making approach. Expert Systems with Applications, 272. https://doi.org/10.1016/j.eswa.2025.126765
Artzner, P., Delbaen, F., Eber, J. M., & Heath, D. (1999). Coherent measures of risk. Mathematical Finance9(3), 203-228. https://doi.org/10.1111/1467-9965.00068
Ashrafzadeh, M., Sadrani, M., & Zolfani, S. H. (2025). Deep Learning and Machine Learning Models for Portfolio Optimization: Enhancing Return Prediction with Stock Clustering. Results in Engineering, 106263. https://doi.org/10.1016/j.rineng.2025.106263
Ayadi, A., Gana, M., Goutte, S., & Guesmi, K. (2023). Optimizing portfolios for the BREXIT: An equity-commodity analysis of US, European and BRICS markets. Journal of International Financial Markets, Institutions and Money, 89.    https://doi.org/10.1016/j.intfin.2023.101863
Behera, J., & Kumar, P. (2025). An approach to portfolio optimization with time series forecasting algorithms and machine learning techniques. Applied Soft Computing170, 112741. https://doi.org/10.1016/j.asoc.2025.112741
Benati, S., & Conde, E. (2022). A relative robust approach on expected returns with bounded CVaR for portfolio selection. European Journal of Operational Research, 296(1), 332–352. https://doi.org/10.1016/j.ejor.2021.04.038
Burkhardt, R., & Ulrych, U. (2023). Sparse and stable international portfolio optimization and currency risk management. Journal of International Money and Finance, 139. https://doi.org/10.1016/j.jimonfin.2023.102949
Chen, L., Zhang, L., Huang, J., Xiao, H., & Zhou, Z. (2021). Social responsibility portfolio optimization incorporating ESG criteria. Journal of Management Science and Engineering, 6(1), 75–85. https://doi.org/10.1016/j.jmse.2021.02.005
Chiang, T. C., & Chen, X. (2016). Stock returns and economic fundamentals in an emerging market: An empirical investigation of domestic and global market forces. International Review of Economics & Finance, 43, 107–120. https://doi.org/10.1016/j.iref.2015.10.034
Cui, T., Du, N., Yang, X., & Ding, S. (2024). Multi-period portfolio optimization using a deep reinforcement learning hyper-heuristic approach. Technological Forecasting and Social Change, 198. https://doi.org/10.1016/j.techfore.2023.122944
El Kharrim, M. (2023). Multi-period fuzzy portfolio optimization model subject to real constraints. EURO Journal on Decision Processes, 11. https://doi.org/10.1016/j.ejdp.2023.100041
Emami, I., Kamran Rad, R., & Jokar, E. (2021). Location and allocation in multi-level supply chain network of projects. Journal of Quality Engineering and Production Optimization6(1), 49-70. doi: 10.22070/jqepo.2021.13527.1173
Fernandez, E., Navarro, J., Solares, E., & Coello, C. (2019). A novel approach to select the best portfolio considering the preferences of the decision maker. Swarm and Evolutionary Computation, 46, 140–153. https://doi.org/10.1016/j.swevo.2019.02.002
Fooeik, A., 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.
Ghanbari, H., Mohammadi, E., Fooeik, A. M. L., Kumar, R. R., Stauvermann, P. J., & Shabani, M. (2024). Cryptocurrency portfolio allocation under credibilistic CVaR criterion and practical constraints. Risks12(10), 163.  https://doi.org/10.3390/risks12100163
Kaucic, M., Piccotto, F., Sbaiz, G., & Valentinuz, G. (2023). A hybrid level-based learning swarm algorithm with mutation operator for solving large-scale cardinality-constrained portfolio optimization problems. Information Sciences, 634, 321–339. https://doi.org/10.1016/j.ins.2023.03.115
Larni-Fooeik, A., Sadjadi, S. J., & Mohammadi, E. (2024a). Stochastic portfolio optimization: A regret-based approach on volatility risk measures: An empirical evidence from the New York stock market. Plos one, 19(4). https://doi.org/10.1371/journal.pone.0299699
Larni-Fooeik, A., Ghanbari, H., Shabani, M., & Mohammadi, E. (2024b). Bi-Objective Portfolio Optimization with Mean-CVaR Model: An Ideal and Anti-Ideal Compromise Programming Approach. In: Yazdi, M. (eds) Progressive Decision-Making Tools and Applications in Project and Operation Management. Studies in Systems, Decision and Control, vol 518. Springer, Cham. https://doi.org/10.1007/978-3-031-51719-8_5
Li, Z., Xing, Q., Hu, R., & Qian, B. (2026). Pareto evolutionary algorithm based on Markov chain for portfolio optimization considering profit of assets. Expert Systems with Applications, 296. https://doi.org/10.1016/j.eswa.2025.129144
Markowitz, H. (1952). PORTFOLIO SELECTION. The Journal of Finance, 7(1), 77–91. https://doi.org/10.1111/j.1540-6261.1952.tb01525.x
Pang, X., Zhu, S., Cui, X., & Ma, J. (2023). Systemic risk of optioned portfolio: Controllability and optimization. Journal of Economic Dynamics and Control153. https://doi.org/10.1016/J.JEDC.2023.104701
Pendharkar, P. C., & Cusatis, P. (2018). Trading financial indices with reinforcement learning agents. Expert Systems with Applications, 103, 1–13. https://doi.org/ 10.1016/j.eswa.2018.02.032
Prol, J. L., & Kim, K. (2022). Risk-return performance of optimized ESG equity portfolios in the NYSE. Finance Research Letters50. https://doi.org/10.1016/j.frl.2022.103312
Puerto, J., Ricca, F., Rodríguez-Madrena, M., & Scozzari, A. (2022). A combinatorial optimization approach to scenario filtering in portfolio selection. Computers and Operations Research, 142. https://doi.org/10.1016/j.cor.2022.105701
Ranilla-Cortina, S., & Vigo-Aguiar, J. (2024). Performance enhancement through portfolio optimization of delayed insider information: An analysis and implementation study. Journal of Computational and Applied Mathematics, 446. https://doi.org/10.1016/j.cam.2024.115855
Rockafellar, R. T., & Uryasev, S. (2000). Optimization of conditional value-at-risk. Journal of risk2, 21-42.
Sa’diyah, R. N. R., Nooraeni, R., Sofa, W. A., & Falahuddin, M. I. (2024). Portfolio Optimization Using the Mean-Variance Method with a Prototype-based Segmentation Approach. Procedia Computer Science, 245, 601–616. https://doi.org/10.1016/j.procs.2024.10.287.
Seiti, H., Larni-Fooeik, A., Ghasemi Pitrbalouti, R., Selvik, JT., Safa Erenay, F., Elkamel, A. (2024). Incorporating failure mode and effects analysis into a novel framework for hydrogen production from solid waste gasification. International Journal of Hydrogen Energy. https://doi.org/10.1016/j.ijhydene.2024.04.334
Silva, A., Neves, R., & Horta, N. (2015). A hybrid approach to portfolio composition based on fundamental and technical indicators. Expert Systems with Applications, 42(4), 2036–2048. https://doi.org/10.1016/J.ESWA.2014.09.050
Som, A., & Kayal, P. (2022). A multicountry comparison of cryptocurrency vs gold: Portfolio optimization through generalized simulated annealing. Blockchain: Research and Applications, 3(3). https://doi.org/10.1016/j.bcra.2022.100075
Sulas, A., Maringer, D., & Paterlini, S. (2025). Systemic risk from overlapping portfolios: A multi-objective optimization framework. International Review of Financial Analysis, 97. https://doi.org/10.1016/j.irfa.2024.103794
Taheripour, E., Sadjadi, S. J., & Amiri, B. (2025). A novel approach to portfolio construction: An application of finbert sentiment analysis and credibilistic CVaR criterion. IEEE Access13, 76775-76795. doi: 10.1109/ACCESS.2025.3564615
Takahashi, A., & Takahashi, S. (2021). A new interval type-2 fuzzy logic system under dynamic environment: Application to financial investment. Engineering Applications of Artificial Intelligence, 100.
Tian, Y., Cheng, R., Zhang, X., Li, M., & Jin, Y. (2019). Diversity assessment of multiobjective evolutionary algorithms: Performance metric and benchmark problems. IEEE Computational Intelligence Magazine. https://doi.org/10.1109/ MCI.2019.2919398
Wang, Z., Ding, Q., Ding, D., Zhu, S., Ren, J., Wang, Y., & Tan, C. H. (2026). Reinforcement Learning-Guided NSGA-II Enhanced with Gray Relational Coefficient for Multi-Objective Optimization: Application to NASDAQ Portfolio Optimization. Mathematics, 14(2), 296. https://doi.org/10.3390/math14020296
Xidonas, P., Hassapis, C., Soulis, J., & Samitas, A. (2017). Robust minimum variance portfolio optimization modelling under scenario uncertainty. Economic Modelling, 64, 60–71. https://doi.org/10.1016/j.econmod.2017.03.020
Xue, P., & Ye, Y. (2026). Attention-enhanced reinforcement learning for dynamic portfolio optimization. Intelligent Systems with Applications. https://doi.org/10.1016/j.iswa.2025.200622
Yeo, L. L. X., Cao, Q., & Quek, C. (2023). Dynamic portfolio rebalancing with lag-optimised trading indicators using SeroFAM and genetic algorithms. Expert Systems with Applications, 216. https://doi.org/10.1016/j.eswa.2022.119440
Zabavnik, D., & Verbič, M. (2021). Relationship between the financial and the real economy: A bibliometric analysis. International Review of Economics and Finance, 75, 55–75. https://doi.org/10.1016/j.iref.2021.04.014
Zhou, R., & Palomar, D. P. (2020). Understanding the Quintile portfolio. IEEE Transactions on Signal Processing, 68, 4030–4040. https://doi.org/10.1109/ TSP.2020.3006761