ESG portfolio for TDFs with time-varying higher moments and cardinality constraint. (English) Zbl 07871011
Summary: Pension funds are crucial in supporting environmentally sustainable and socially responsible investments. This paper focuses on an essential product of pension funds, target date funds (TDFs), constructing its portfolio model that considers environmental, social, and governance (ESG) investment. Our model utilizes the mean-variance-skewness-kurtosis optimization framework and accounts for the time-varying relationship between realized higher moments and subsequent TDFs performance. A cardinality constraint is included to prevent over-diversification and reduce costs, controlling the number of constituent funds in the TDFs. Since the model is multiobjective, we transform it into a single-objective optimization through the investment manager’s preferences. The numerical experiments represent the most suitable time-varying higher moment strategy for the Chinese market and demonstrate the model’s applicability to managers prioritizing risk over returns, which resonates well with the conservative investment of pension funds. We prove that controlling the number of constituent funds with cardinality constraint is advantageous for improving TDFs’ performance. By selecting ESG funds as underlying assets, we highlight that green TDFs have better long-term performance in terms of higher moment risk.
© 2023 International Federation of Operational Research Societies.
© 2023 International Federation of Operational Research Societies.
MSC:
| 90-XX | Operations research, mathematical programming |
Keywords:
target date funds; environmental; social; governance investment; portfolio selection; cardinality constraint; higher momentsReferences:
| [1] | Ahmad, M., Jinglai, S., 2023. A penalty decomposition algorithm with greedy improvement for mean‐reverting portfolios with sparsity and volatility constraints. International Transactions in Operational Research30, 5, 2415-2435. · Zbl 07744755 |
| [2] | Aifan, L., Junxue, L., Limin, W., Yi, Z., 2023. When trackers are aware of ESG: Do ESG ratings matter to tracking error portfolio performance?Economic Modelling125, 106346. |
| [3] | Alda, M., 2020. ESG fund scores in UK SRI and conventional pension funds: Are the ESG Concerns of the SRI niche affecting the conventional mainstream?Finance Research Letters36, 10, 101313. |
| [4] | Alessandrini, F., Jondeau, E., 2021. Optimal strategies for ESG portfolios. Journal of Portfolio Management47, 6, 114-138. |
| [5] | Amelia, B.T., Mar, A.P., Celia, B.T., 2023. Measuring the overall efficiency of SRI and conventional mutual funds by a diversification‐consistent DEA model. International Transactions in Operational Research30, 5, 2224-2256. · Zbl 07744747 |
| [6] | Ballestero, E., Bravo, M., Perez‐Gladish, B., Arenas‐Parra, M., Pla‐Santamaria, D., 2012. Socially responsible investment: a multicriteria approach to portfolio selection combining ethical and financial objectives. European Journal of Operational Research216, 2, 487-494. |
| [7] | Baum, L.E., Petrie, T., Soules, G., Weiss, N., 1970. A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. Annals of Mathematical Statistics41, 1, 164-171. · Zbl 0188.49603 |
| [8] | Berg, M., 2021. Value judgments at the heart of green transformation: the leverage of pension fund investors. Global Environmental Politics21, 3, 77-96. |
| [9] | Brown, L., 2019. Towards “green” trusteeship: new statutory amendments for occupational pension trustees. Trusts and Trustees25, 10, 978-985. |
| [10] | Campello, B.S.C., Ghidini, C.T.L.S., Ayres, A.O.C., Oliveir, W.A., 2020. A multiobjective integrated model for lot sizing and cutting stock problems. Journal of the Operational Research Society71, 9, 1466-1478. |
| [11] | Chen, L., Zhang, L., Huang, J., Xiao, H., Zhou, Z., 2021a. Social responsibility portfolio optimization incorporating ESG criteria. Journal of Management Science and Engineering6, 1, 75-85. |
| [12] | Chen, X., Li, B., Worthington, A.C., 2021b. Higher moments and US industry returns: realized skewness and kurtosis. Review of Accounting and Finance20, 1, 1-22. |
| [13] | Davies, R.J., Kat, H.M., Lu, S., 2009. Fund of hedge funds portfolio selection: a multiple‐objective approach. Journal of Derivatives & Hedge Funds15, 2, 91-115. |
| [14] | Dziecichowicz, M., Aurlie, C.T., 2017. Robust stock and bond allocation with end‐of‐horizon effects. Rairo Operations Research53, 1, 1-28. · Zbl 1414.90195 |
| [15] | Ehrgott, M., Gandibleux, X., 2002. Multiobjective Combinatorial Optimization Theory, Methodology, and Applications. Springer, Boston, MA. · Zbl 1095.90593 |
| [16] | Elliott, R.J., Siu, T.K., Badescu, A., 2010. On mean‐variance portfolio selection under a hidden Markovian regime‐switching model. Economic Modelling27, 3, 678-686. |
| [17] | Forsyth, P.A., Vetzal, K.R., Westmacott, G., 2019. Management of portfolio depletion risk through optimal life cycle asset allocation. North American Actuarial Journal23, 3, 447-468. · Zbl 1426.91218 |
| [18] | Giamouridis, D., Vrontos, I.D., 2007. Hedge fund portfolio construction: A comparison of static and dynamic approaches. Journal of Banking & Finance31, 1, 199-217. |
| [19] | Hyoung‐Goo, K., Kyoung‐Hun, B., Sung‐Taek, Y., Chang‐Hee, C., 2019. An investment strategy based on life and business cycles. Korean Journal of Financial Studies48, 6, 721-754. |
| [20] | Israelsen, C., 2005. A refinement to the Sharpe ratio and information ratio. Journal of Asset Management volume5, 6, 423-427. |
| [21] | Kat, H.M., 2004. In search of the optimal fund of hedge funds. The Journal of Wealth Management6, 4, 43-51. |
| [22] | Kinateder, H., Papavassiliou, V.G., 2019. Sovereign bond return prediction with realized higher moments. Journal of International Financial Markets, Institutions and Money62, 1, 53-73. |
| [23] | Kolbert, F., Wormald, L., 2010. Robust portfolio optimization using second‐order cone programming. In Satchell, S. (ed.) (ed.), Optimizing Optimization. Elsevier, Boston, MA, pp. 1-22. |
| [24] | Lai, Y., Hwang, C., 1994. Fuzzy multiple objective decision making. In Fuzzy Multiple Objective Decision Making. Lecture Notes in Economics and Mathematical Systems, Vol. 404. Springer, Berlin, pp. 139-262. · Zbl 0810.90138 |
| [25] | Lanski, I., Paramaguru, R., Phoa, W., Wang, Y., Hammond, P.B., 2022. Using a life cycle model to design a target date glidepath. The Journal of Portfolio Management48, 4, 228-240. |
| [26] | Lejeune, M.A., 2011. A VaR Black‐Litterman model for the construction of absolute return fund‐of‐funds. Quantitative Finance11, 10, 1489-1501. · Zbl 1258.91200 |
| [27] | Liagkouras, K., K., M., Tsihrintzis, G., 2020. Incorporating environmental and social considerations into the portfolio optimization process. Annals of Operations Research1, 1, 1-26. |
| [28] | Liechty, M.W., Salam, M., 2017. Revealed preferences for portfolio selection: Does skewness matter?Applied Economics Letters24, 14, 968-971. |
| [29] | Maillet, B., Jurczenko, E., Merlin, P., 2006. Hedge funds portfolio selection with higher‐order moments: a non‐parametric mean‐variance‐skewness‐kurtosis efficient frontier. Social Science Electronic Publishing1, 1, 51-66. |
| [30] | Markowitz, H., 1952. Portfolio selection. Journal of Finance7, 1, 77-91. |
| [31] | Martí‐Ballester, C., 2020. Examining the financial performance of pension funds focused on sectors related to sustainable development goals. International Journal of Sustainable Development & World Ecology27, 2, 179-191. |
| [32] | Morton, D.P., Elmira, P., Ivilina, P., 2006. Efficient fund of hedge funds construction under downside risk measures. Journal of Banking and Finance30, 2, 503-518. |
| [33] | Moshe, L., Haim, L., 2022. Exponential glide paths. Journal of Investment Management20, 1, 25-36. |
| [34] | Nagy, Z., Cogan, D., Sinnreich, D., 2013. Optimizing environmental, social and governance factors in portfolio construction: analysis of three ESG‐tilted strategies. SSRN. Available at https://doi.org/10.2139/ssrn.2221524, (accessed 20 February 2013). · doi:10.2139/ssrn.2221524 |
| [35] | Ni, J., 2006. Mean‐variance‐skewness‐kurtosis‐based portfolio optimization. In First International Multi‐Symposiums on Computer and Computational Sciences, June 20 Jun 24, Hangzhou, China. IEEE, Piscataway, NJ, pp. 292-297. |
| [36] | Nystrup, P., Henrik, M., Erik, L., 2018. Dynamic portfolio optimization across hidden market regimes. Quantitative Finance18, 1, 83-95. · Zbl 1471.91509 |
| [37] | Olszewski, Y., 2005. Building a better fund of hedge funds: a fractal and alpha‐stable distribution approach. SSRN. Available at https://doi.org/10.2139/ssrn.776064, (accessed December 2005). · doi:10.2139/ssrn.776064 |
| [38] | Przemyslaw, J., Ignacy, K., Janusz, M., Dmitry, P., 2023. Expected mean return‐standard deviation efficient frontier approximation with low‐cardinality portfolios in the presence of the risk‐free asset. International Transactions in Operational Research30, 5, 2395-2414. · Zbl 07744754 |
| [39] | Rabiner, L., Juang, B., 1986. An introduction to hidden Markov models. IEEE ASSP Magazine3, 1, 4-16. |
| [40] | Rempel, A., Gupta, J., 2020. Conflicting commitments? Examining pension funds, fossil fuel assets and climate policy in the Organisation for Economic Co‐operation and Development (OECD). Energy Research & Social Science69, 11, 101736. |
| [41] | Rui, B. Pedro, \(H \acute{e}\) lder, S., Pedro, G., 2019. Portfolio management with higher moments: the cardinality impact. International Transactions in Operational Research26, 6, 2531-2560. · Zbl 07766407 |
| [42] | Saaty, T.L., Kearns, K.P., 1985. Systems characteristics and the analytic hierarchy process. In Analytical Planning. Pergamon, Oxford, pp. 63-86. |
| [43] | Gasser, S.M., Rammerstorfer, M., Weinmayer, K., 2017. Markowitz revisited: social portfolio engineering. European Journal of Operational Research258, 3, 1181-1190. · Zbl 1395.91404 |
| [44] | Sun, J., Zhu, D., Platen, E., 2021. Dynamic asset allocation for target date funds under the benchmark approach. Astin Bulletin51, 2, 449-474. · Zbl 1471.91515 |
| [45] | Syouching, L., Hungchih,2008. The performance evaluation for fund of funds by comparing asset allocation of mean‐variance model or genetic algorithms to that of fund managers. Applied Financial Economics18, 6, 485-501. |
| [46] | Wang, Q.Y., Huang, W.L., Wu, X., Zhang, C., 2019. How effective is the tail mean‐variance model in the fund of fund selection? An empirical study using various risk measures. Finance Research Letters29, 6, 239-244. |
| [47] | Xu, F.M., Dai, Y.H., Zhao, Z.H., Xu, Z.B., 2018. Efficient projected gradient methods for cardinality constrained optimization. Science China Mathematics2, 4, 1-24. |
| [48] | Zhihua, Z., Fengmin, X., Meihua, W., Chengyi, Z., 2019. A sparse enhanced indexation model with norm and its alternating quadratic penalty method. Journal of the Operational Research Society70, 3, 433-445. |
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