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

Author

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. Liu, Wenbin & Zhou, Zhongbao & Liu, Debin & Xiao, Helu, 2015. "Estimation of portfolio efficiency via DEA," Omega, Elsevier, vol. 52(C), pages 107-118.
    3. 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.
    4. 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.
    5. Lin, Ruiyue & Li, Zongxin, 2020. "Directional distance based diversification super-efficiency DEA models for mutual funds," Omega, Elsevier, vol. 97(C).
    6. 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.
    7. 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.
    8. 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.
    9. 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..
    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. 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.
    13. 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.
    14. Ino, Hiroaki & Matsumura, Toshihiro, 2021. "Promoting green or restricting gray? An analysis of green portfolio standards," Economics Letters, Elsevier, vol. 198(C).
    15. 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.
    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. 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.
    18. Choi, Hyung-Suk & Min, Daiki, 2017. "Efficiency of well-diversified portfolios: Evidence from data envelopment analysis," Omega, Elsevier, vol. 73(C), pages 104-113.
    19. 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.
    20. 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.
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    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.

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