IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i4p1933-d497548.html
   My bibliography  Save this article

Efficiency Evaluation and Selection Strategies for Green Portfolios under Different Risk Appetites

Author

Listed:
  • Wencheng Yu

    (School of Economics, Qingdao Agricultural University, Qingdao 266109, China)

  • Shaobo Liu

    (School of Economics, Ocean University of China, Qingdao 266100, China)

  • Lili Ding

    (School of Economics, Ocean University of China, Qingdao 266100, China
    Marine Development Studies Institute of OUC, Key Research Institute of Humanities and Social Sciences at Universities, Ministry of Education, Qingdao 266100, China)

Abstract

Since investors have diverse risk motives for green investments, this paper uses data envelopment analysis (DEA) and simulation to accurately evaluate the efficiency of green portfolios from the perspective of investors’ subjective risks and accordingly provide suitable investment selection strategies. On the one hand, the paper integrates investors’ risk preferences with efficiency evaluation models under the framework of behavioral finance, and then constructs a green portfolio efficiency evaluation model based on cumulative prospect theory on the basis of defining green portfolio efficiency. On the other hand, by bringing realistic Chinese stock data into the evaluation model and solving it with the help of large number iteration and DEA, the trends of frontier movements and selection options of green portfolios under the influence of different risk preferences are obtained and analyzed. The empirical simulation reveals that: (1) if investors’ risk aversion at return rises, it will not only reduce the expected prospective value of the green portfolio, but also shift down and flatten the frontier of the green portfolio; indicating that investors will tend to reduce their risk-tolerant attitude and prefer a conservative strategy under the same value condition. (2) If investors increase their risk-seeking in the case of losses, this will raise the expected prospect value of the green portfolio and lead to an inward and steeper green portfolio frontier; suggesting that, given equal value, investors prefer to increase their risk-taking capacity and use aggressive strategies in the hope of turning the profit around. (3) The efficiency results of green portfolios are very sensitive to changes in investors’ risk preferences, suggesting that investors need to select and match green portfolios with their own risk appetite levels. The above findings enrich and expand the risk types and evaluation models in previous green investment studies from the perspective of investors’ subjective risk.

Suggested Citation

  • Wencheng Yu & Shaobo Liu & Lili Ding, 2021. "Efficiency Evaluation and Selection Strategies for Green Portfolios under Different Risk Appetites," Sustainability, MDPI, vol. 13(4), pages 1-15, February.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:4:p:1933-:d:497548
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/4/1933/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/4/1933/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Giorgio Consigli & Asmerilda Hitaj & Elisa Mastrogiacomo, 2019. "Portfolio choice under cumulative prospect theory: sensitivity analysis and an empirical study," Computational Management Science, Springer, vol. 16(1), pages 129-154, February.
    2. Thorsten HENS & János MAYER, 2014. "Cumulative Prospect Theory and Mean Variance Analysis: A Rigorous Comparison," Swiss Finance Institute Research Paper Series 14-23, Swiss Finance Institute.
    3. Liu, Wenbin & Zhou, Zhongbao & Liu, Debin & Xiao, Helu, 2015. "Estimation of portfolio efficiency via DEA," Omega, Elsevier, vol. 52(C), pages 107-118.
    4. Ino, Hiroaki & Matsumura, Toshihiro, 2021. "Promoting green or restricting gray? An analysis of green portfolio standards," Economics Letters, Elsevier, vol. 198(C).
    5. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    6. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    7. Bento, Antonio M. & Garg, Teevrat & Kaffine, Daniel, 2018. "Emissions reductions or green booms? General equilibrium effects of a renewable portfolio standard," Journal of Environmental Economics and Management, Elsevier, vol. 90(C), pages 78-100.
    8. Lin, Ruiyue & Li, Zongxin, 2020. "Directional distance based diversification super-efficiency DEA models for mutual funds," Omega, Elsevier, vol. 97(C).
    9. Lim, Sungmook & Oh, Kwang Wuk & Zhu, Joe, 2014. "Use of DEA cross-efficiency evaluation in portfolio selection: An application to Korean stock market," European Journal of Operational Research, Elsevier, vol. 236(1), pages 361-368.
    10. Chao Gong & Chunhui Xu & Ji Wang, 2018. "An Efficient Adaptive Real Coded Genetic Algorithm to Solve the Portfolio Choice Problem Under Cumulative Prospect Theory," Computational Economics, Springer;Society for Computational Economics, vol. 52(1), pages 227-252, June.
    11. Banihashemi, Shokoofeh & Navidi, Sarah, 2017. "Portfolio performance evaluation in Mean-CVaR framework: A comparison with non-parametric methods value at risk in Mean-VaR analysis," Operations Research Perspectives, Elsevier, vol. 4(C), pages 21-28.
    12. Gagari Chakrabarti & Chitrakalpa Sen, 2020. "Time series momentum trading in green stocks," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 37(2), pages 361-389, March.
    13. Choi, Hyung-Suk & Min, Daiki, 2017. "Efficiency of well-diversified portfolios: Evidence from data envelopment analysis," Omega, Elsevier, vol. 73(C), pages 104-113.
    14. N. Grishina & C. A. Lucas & P. Date, 2017. "Prospect theory–based portfolio optimization: an empirical study and analysis using intelligent algorithms," Quantitative Finance, Taylor & Francis Journals, vol. 17(3), pages 353-367, March.
    15. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    16. Daraio, Cinzia & Simar, Leopold, 2006. "A robust nonparametric approach to evaluate and explain the performance of mutual funds," European Journal of Operational Research, Elsevier, vol. 175(1), pages 516-542, November.
    17. Zhang, Yue-Jun & Chen, Ming-Ying, 2018. "Evaluating the dynamic performance of energy portfolios: Empirical evidence from the DEA directional distance function," European Journal of Operational Research, Elsevier, vol. 269(1), pages 64-78.
    18. Allevi, E. & Basso, A. & Bonenti, F. & Oggioni, G. & Riccardi, R., 2019. "Measuring the environmental performance of green SRI funds: A DEA approach," Energy Economics, Elsevier, vol. 79(C), pages 32-44.
    19. Joe Zhu, 2014. "DEA Cross Efficiency," International Series in Operations Research & Management Science, in: Quantitative Models for Performance Evaluation and Benchmarking, edition 3, chapter 4, pages 61-92, Springer.
    20. Daniel Ellsberg, 1961. "Risk, Ambiguity, and the Savage Axioms," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 75(4), pages 643-669.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Massimiliano Kaucic & Filippo Piccotto & Gabriele Sbaiz & Giorgio Valentinuz, 2023. "Optimal Portfolio with Sustainable Attitudes under Cumulative Prospect Theory," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 13(4), pages 1-4.
    2. Zheng Liu & Wenzhuo Sun, 2023. "Study on Low-Carbon Technology Investment Strategies for High Energy-Consuming Enterprises under the Health Co-Benefits of Carbon Emission Reduction," Sustainability, MDPI, vol. 15(11), pages 1-22, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yangxue Ning & Yan Zhang & Guoqiang Wang, 2023. "An Improved DEA Prospect Cross-Efficiency Evaluation Method and Its Application in Fund Performance Analysis," Mathematics, MDPI, vol. 11(3), pages 1-15, January.
    2. Massimiliano Kaucic & Filippo Piccotto & Gabriele Sbaiz & Giorgio Valentinuz, 2023. "Optimal Portfolio with Sustainable Attitudes under Cumulative Prospect Theory," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 13(4), pages 1-4.
    3. Zhou, Zhongbao & Gao, Meng & Xiao, Helu & Wang, Rui & Liu, Wenbin, 2021. "Big data and portfolio optimization: A novel approach integrating DEA with multiple data sources," Omega, Elsevier, vol. 104(C).
    4. Liu, Hui-hui & Song, Yao-yao & Yang, Guo-liang, 2019. "Cross-efficiency evaluation in data envelopment analysis based on prospect theory," European Journal of Operational Research, Elsevier, vol. 273(1), pages 364-375.
    5. Sweksha Srivastava & Abha Aggarwal & Pooja Bansal, 2024. "Efficiency Evaluation of Assets and Optimal Portfolio Generation by Cross Efficiency and Cumulative Prospect Theory," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 129-158, January.
    6. Liu, Hui-hui & Song, Yao-yao & Liu, Xiao-xiao & Yang, Guo-liang, 2020. "Aggregating the DEA prospect cross-efficiency with an application to state key laboratories in China," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    7. Lin, Ruiyue & Liu, Qian, 2021. "Multiplier dynamic data envelopment analysis based on directional distance function: An application to mutual funds," European Journal of Operational Research, Elsevier, vol. 293(3), pages 1043-1057.
    8. Zhiying Zhang & Huchang Liao, 2024. "A stochastic cross-efficiency DEA approach based on the prospect theory and its application in winner determination in public procurement tenders," Annals of Operations Research, Springer, vol. 341(1), pages 509-537, October.
    9. Fortin, Ines & Hlouskova, Jaroslava, 2024. "Prospect theory and asset allocation," The Quarterly Review of Economics and Finance, Elsevier, vol. 94(C), pages 214-240.
    10. Carla Oliveira Henriques & Maria Elisabete Neves & Licínio Castelão & Duc Khuong Nguyen, 2022. "Assessing the performance of exchange traded funds in the energy sector: a hybrid DEA multiobjective linear programming approach," Annals of Operations Research, Springer, vol. 313(1), pages 341-366, June.
    11. Mohammad Mehdi Hosseinzadeh & Sergio Ortobelli Lozza & Farhad Hosseinzadeh Lotfi & Vittorio Moriggia, 2023. "Portfolio optimization with asset preselection using data envelopment analysis," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(1), pages 287-310, March.
    12. Francesco Cesarone & Massimiliano Corradini & Lorenzo Lampariello & Jessica Riccioni, 2023. "A new behavioral model for portfolio selection using the Half-Full/Half-Empty approach," Papers 2312.10749, arXiv.org.
    13. Zeng, Ximei & Zhou, Zhongbao & Gong, Yeming & Liu, Wenbin, 2022. "A data envelopment analysis model integrated with portfolio theory for energy mix adjustment: Evidence in the power industry," Socio-Economic Planning Sciences, Elsevier, vol. 83(C).
    14. Itzhak Gilboa & Andrew Postlewaite & Larry Samuelson & David Schmeidler, 2019. "What are axiomatizations good for?," Theory and Decision, Springer, vol. 86(3), pages 339-359, May.
    15. Stefano DellaVigna, 2009. "Psychology and Economics: Evidence from the Field," Journal of Economic Literature, American Economic Association, vol. 47(2), pages 315-372, June.
    16. Iñigo Iturbe-Ormaetxe & Giovanni Ponti & Josefa Tomás, 2016. "Myopic Loss Aversion under Ambiguity and Gender Effects," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-11, December.
    17. Mercè Roca & Robin Hogarth & A. Maule, 2006. "Ambiguity seeking as a result of the status quo bias," Journal of Risk and Uncertainty, Springer, vol. 32(3), pages 175-194, May.
    18. Mohammed Abdellaoui & Horst Zank, 2023. "Source and rank-dependent utility," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 75(4), pages 949-981, May.
    19. Tarnaud, Albane Christine & Leleu, Hervé, 2018. "Portfolio analysis with DEA: Prior to choosing a model," Omega, Elsevier, vol. 75(C), pages 57-76.
    20. Basieva, Irina & Khrennikova, Polina & Pothos, Emmanuel M. & Asano, Masanari & Khrennikov, Andrei, 2018. "Quantum-like model of subjective expected utility," Journal of Mathematical Economics, Elsevier, vol. 78(C), pages 150-162.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:4:p:1933-:d:497548. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.