Evolutionary Stable Portfolio Rule: Requirements and Obstacles

Öz

The present study aims to investigate the investment strategies used in stock markets from an evolutionary game theory perspective. Our primary objective is to identify the necessary conditions for achieving an evolutionarily stable equilibrium and to highlight the importance of non-return effects. To achieve a yield-dominant stable investment strategy, investors must focus on profits while remaining cautious of yield disparities. However, to achieve this equilibrium, people must act completely rationally, but as social beings, humans can make mistakes. Therefore, evolutionary theory is ideal for modeling emotional states and non-rational behaviors, such as reciprocity, altruism, and selfishness. We used evolutionary game theory to model how people interact with each other when making investment decisions. Our focus was on how individuals adapt and change their strategies. Our analysis also concentrated on non-interactive strategy changes using signaling mechanisms. We conclude that low-return strategies can persist for extended periods due to non-return effects, conformism, and human-specific emotions.

Anahtar Kelimeler

• Evolutionary Game Theory
• Evolutionary Stable Equilibrium
• Replicator Dynamics
• Conformism
• Investment Strategies

Evrimsel Kararlı Portföy Kuralı: Gereksinimler ve Engeller

Öz

Bu çalışma, hisse senedi piyasalarında kullanılan yatırım stratejilerini evrimsel oyun teorisi perspektifinden incelemeyi amaçlamaktadır. Öncelikli amaç, evrimsel istikrarlı bir dengeye ulaşmak için gerekli koşulları araştırmak ve getiri dışı etkilerin önemini ortaya koymaktır. Getiri ağırlıklı istikrarlı bir yatırım stratejisine ulaşmak için, yatırımcıların getiri eşitsizliklerine karşı temkinli davranırken kâra odaklanmaları gerekmektedir. Bununla birlikte, bu dengeye ulaşmak için insanların tamamen rasyonel davranması gerekir, ancak sosyal varlıklar olarak insanlar hata yapabilir. Bu nedenle evrim teorisi, duygusal durumları ve karşılıklılık, fedakarlık ve bencillik gibi rasyonel olmayan davranışları modellemek için idealdir. İnsanların yatırım kararları verirken birbirleriyle nasıl etkileşime girdiklerini modellemek için evrimsel oyun teorisini kullanılmıştır. İnsanların stratejilerini nasıl değiştirdiklerine odaklanılmıştır. Analizimiz ayrıca sinyal mekanizmalarını kullanarak etkileşimli olmayan strateji değişikliklerine de odaklandı. Düşük getirili stratejilerin getiri dışı etkiler, konformizm ve insana özgü duygular nedeniyle uzun süre devam edebileceği sonucuna varılmıştır.

Anahtar Kelimeler

• Evrimsel Oyun Teorisi
• Evrimsel Kararlı Denge
• Replikatör Dinamiği
• Konformizm
• Yatırım Stratejileri

Kaynakça

1. Andrei, D. And Hasler, M. (2015). Investor Attention and Stock Market Volatility. The Review of Financial Studies, 28(1),33-72.
2. Ari, D. and Alagoz, B.B. (2023). DEHypGpOls: a genetic programming with evolutionary hyperparameter optimization and its application for stock market trend prediction. Soft Computing, 27, 2553–2574.
3. Binswanger, H. P. (1980). Attitudes toward Risk: Experimental Measurement in Rural India. American Journal of Agricultural Economics, 62(3), 395–407. https://doi.org/10.2307/1240194
4. Blume, L., and Easley, D. (1992). Evolution and Market Behavior. J. Econ. Theory, 58, 9–40.
5. Blume, L. and Easley, D. (2006). If You're So Smart, Why Aren't You Rich? Belief Selection in Complete and Incomplete Markets. Econometrica, 74, 929 - 966.
6. Boz, E. and Mendoza, E. G. (2014). Financial innovation, the discovery of risk, and the U.S. credit crisis. Journal of Monetary Economics, 62, 1-22.
7. Brock, W.A., Hommes, C. H. and Wagener, F. O. O. (2005). Evolutionary Dynamics in markets with many trader types, Journal of Mathematical Economics, 41(1-2), 7-42.
8. Cheng, L., Columba, F., Costa, A., Kongsamut, P., Otani, A., Saiyid, M., Wezel, T. and Wu, X. (2011). Macroprudential Policy: What Instruments and How to Use Them? Lessons From Country Experiences, IMF Working Paper WP/11/238.

9. Craven, M., and Graham, D. (2020, Jan 24). A Matrix-Based Evolutionary Algorithm for Cardinality-Constrained Portfolio Optimisation. Available at https://researchportal.plymouth.ac.uk/en/publications/a-matrix-based-evolutionary-algorithm-for-cardinality-constrained
10. Damodoran, A. (2018). Investment Valuation: Tools and Techniques for Determining the Value of Any Asset., Third Edition, New Jersey: John Wiley & Sons, Inc.
11. De Long, J., Shleifer, A., Summers, L. and Waldman, R. (1989). The Size and Incidence of Losses From Noise Trading. Journal of Finance, 44, 681-696.
12. Di Tollo, G., Fattoruso, G. and Filograsso, G. (2024). An adaptive evolutionary strategy for long–short portfolio construction. Decisions in Economics and Finance https://doi.org/10.1007/s10203-024-00468-8
13. Dong, Y., Zhang Y., Pan, J. and Chen T. (2020). Evolutionary Game Model of Stock Price Synchronicity from Investor Behavior. Hindawi Discrete Dynamics in Nature and Society, Article ID 7957282 https://doi.org/10.1155/2020/7957282
14. Evstigneev, I.V., Hens, T. and Schenk- Hoppé, K.R. (2002). Market selection of financial trading strategies: global stability. Mathematical Finance, 12, 329–339.
15. Evstigneev, I.V., Hens, T. and Schenk- Hoppé, K.R. (2006). Evolutionary stable stock markets.Economic Theory, 27, 449-468.
16. Evstigneev, I.V., Hens, T. and Vanaei, M.J. (2023). Evolutionary finance: a model with endogenous asset payoffs. Journal of Bioeconomics, 25, 117–143.
17. Fama, E. (1965). The behavior of stock market prices. Journal of Business, 38, 34–105.
18. Friedman, M. (1953). Essays in positive economics. Chicago: University of Chicago Press.
19. Guarino, A., Santoro, D., Grilli, L., Zaccagnino, R. and Balbi, M. (2024). EvoFolio: a portfolio optimization method based on multi-objective evolutionary algorithms. Neural Computing and Applications, 36, 7221–7243.
20. Gulmez, B. (2023). Stock price prediction with optimized deep LSTM network with artificial rabbits optimization algorithm. Expert System with Applications, 227, https://doi.org/10.1016/j.eswa.2023.120346.
21. Hens, T. and Naebi, F. (2022). Behavioral heterogeneity in the CAPM with evolutionary dynamics. J Evol Econ, 32, 1499–1521.
22. Hens, T. and Schenk-Hoppé, K.R. (2005). Evolutionary finance: Introduction to the special issue. Journal of Mathematical Economics, 41(1), 1-5.
23. Rubio, M. and Gallego, J.C. (2016). The new financial regulation in Basel III and monetary policy: A macroprudential approach. Journal of Financial Stability, 26, 294-305.
24. Sandroni, A. (2000). Do markets favor agents able to make accurate predictions?. Econometrica 68, 1303–1341.
25. Shleifer, A. (2000). Inefficient markets: an introduction to behavioral finance. UK : Oxford University Press.
26. Song, Y., Zhao, G., Zhang, B., Chen, H., Deng, W. and Deng, W. (2023). An enhanced distributed differential evolution algorithm for portfolio optimization problems. Engineering Applications of Artificial Intelligence, 121.
27. Williams, J. B. (1997). The Theory of Investment Value. Fraser Publishing Company.
28. Kogan, L., Ross, S. A., Wang, J., & Westerfield, M. M. (2006). The Price Impact and Survival of Irrational Traders. The Journal of Finance, 61(1), 195–229.