Herding Behavior and Evolutionary Equilibrium in Cryptocurrency Markets: A Markov Regime-Switching Analysis
DOI:
https://doi.org/10.5281/zenodo.20776736Keywords:
Evolutionary game theory, behavioral economics, herding behaviour, Markov regime-switching modelAbstract
Traditional finance theories rest on the Efficient Market Hypothesis, which assumes that market participants are fully rational and that prices reflect all available information. However, the excessive volatility and speculative bubbles observed in cryptocurrency markets call the validity of these assumptions into question. This study aims to examine the price dynamics of the Bitcoin market from the perspective of Evolutionary Game Theory (EGT) and to empirically test structural shifts in investor behavior. Within this framework, the strategic interaction between “fundamentalists” and “herders” is theoretically modeled and shown to drive the market toward multiple equilibrium points. To analyze this dynamic structure, a Markov regime-switching model is applied to Bitcoin daily return data. The findings reveal that the market exhibits transitions between two distinct regimes: a “low-variance rational pricing” regime and a “high-variance herding” regime. The results indicate that price formation in cryptocurrency markets is not a static equilibrium but rather an evolutionary adaptation process.
References
Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723. https://doi.org/10.1109/TAC.1974.1100705
Akyildirim, E., Aysan, A. F., Cepni, O., & Corbet, S. (2025). News sentiment and DeFi coin returns: An empirical analysis. International Review of Economics & Finance, 103, Article 104112. https://doi.org/10.1016/j.iref.2024.103780
Akyildirim, E., Sensoy, A., Corbet, S., & Yarovaya, L. (2021). The effects of social media sentiment on the volatility and trading volume of cryptocurrencies. Journal of International Financial Markets, Institutions and Money, 76, Article 101481. https://doi.org/10.1016/j.intfin.2021.101481
Ardia, D., & Bluteau, K. (2024). Twitter and cryptocurrency pump-and-dumps. International Review of Financial Analysis, 95 (Part B), Article 103510. https://doi.org/10.1016/j.irfa.2024.103510
Banerjee, A. V. (1992). A simple model of herd behavior. The Quarterly Journal of Economics, 107(3), 797–817. https://doi.org/10.2307/2118364
Barber, B. M., & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and common stock investment. The Quarterly Journal of Economics, 116(1), 261–292. https://doi.org/10.1162/003355301556400
Bariviera, A. F. (2017). The inefficiency of Bitcoin revisited: A dynamic approach. Economics Letters, 161, 1–4. https://doi.org/10.1016/j.econlet.2017.09.013
Baumann, M. H., Baumann, M., Grüne, L., & Herz, B. (2023). Market structure and instability artifacts in heterogeneous agent models: Lessons from implicit discretizations of stiff equations. Computational Economics, 62(3), 855–890. https://doi.org/10.1007/s10614-022-10285-z
Bikhchandani, S., & Sharma, S. (2001). Herd behavior in financial markets. IMF Staff Papers, 47(3), 279–310. https://doi.org/10.2307/3867650
Bikhchandani, S., Hirshleifer, D., & Welch, I. (1992). A theory of fads, fashion, custom, and cultural change as informational cascades. Journal of Political Economy, 100(5), 992–1026. https://doi.org/10.1086/261849
Bouri, E., Gupta, R., & Roubaud, D. (2019). Herding behaviour in cryptocurrencies. Finance Research Letters, 29, 216–221. https://doi.org/10.1016/j.frl.2018.07.008
Brock, W. A., & Hommes, C. H. (1998). Heterogeneous beliefs and routes to chaos in a simple asset pricing model. Journal of Economic Dynamics and Control, 22(8–9), 1235–1274. https://doi.org/10.1016/S0165-1889(98)00011-6
Chang, E. C., Cheng, J. W., & Khorana, A. (2000). An examination of herd behavior in equity markets: An international perspective. Journal of Banking & Finance, 24(10), 1651–1679. https://doi.org/10.1016/S0378-4266(99)00096-5
Choi, H. (2021). Investor attention and Bitcoin liquidity: Evidence from Bitcoin tweets. Finance Research Letters, 39, Article 101555. https://doi.org/10.1016/j.frl.2020.101555
Christie, W. G., & Huang, R. D. (1995). Following the pied piper: Do individual returns herd around the market? Financial Analysts Journal, 51(4), 31–37. https://doi.org/10.2469/faj.v51.n4.1918
Cruz-Hernández, A., & Mora-Valencia, A. (2024). The Adaptive Market Hypothesis and predictability in Latin American stock indices. Journal of Economics, Finance and Administrative Science, 29(57), 86–104. https://doi.org/10.1108/JEFAS-09-2023-0274
Da Gama, P. V. J., Klotzle, M. C., Pinto, A. C. F., & Gomes, L. L. (2023). Herding behavior and contagion in the cryptocurrency market: A multifractal analysis. Physica A: Statistical Mechanics and its Applications, 615, 128589. https://doi.org/10.1016/j.physa.2023.128589
Davies, R. B. (1987). Hypothesis testing when a nuisance parameter is present only under the alternative. Biometrika, 74(1), 33–43. https://doi.org/10.1093/biomet/74.1.33
Devenow, A., & Welch, I. (1996). Rational herding in financial economics. European Economic Review, 40(3–5), 603–615. https://doi.org/10.1016/0014-2921(95)00073-9
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74(366a), 427–431. https://doi.org/10.1080/01621459.1979.10482531
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987–1007. https://doi.org/10.2307/1912773
Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383–417. https://doi.org/10.2307/2325486
Figà-Talamanca, G., & Patacca, M. (2021). An explorative analysis of Bitcoin returns: Markov-switching and structural breaks. Journal of Economic Interaction and Coordination, 16(1), 121–148. https://doi.org/10.1007/s11403-020-00295-1
Garcia, R. (1998). Asymptotic null distribution of the likelihood ratio test in Markov switching models. International Economic Review, 39(3), 763–788. https://doi.org/10.2307/2527399
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57(2), 357–384. https://doi.org/10.2307/1912559
Hamilton, J. D. (1990). Analysis of time series subject to changes in regime. Journal of Econometrics, 45(1–2), 39–70. https://doi.org/10.1016/0304-4076(90)90093-9
Hamilton, J. D. (1994). Time series analysis. Princeton University Press.
Hommes, C. H. (2006). Heterogeneous agent models in economics and finance. In L. Tesfatsion & K. L. Judd (Eds.), Handbook of computational economics: Vol. 2. Agent-based computational economics (pp. 1109–1186). Elsevier. https://doi.org/10.1016/S1574-0021(05)02023-X
Hwang, S., & Salmon, M. (2004). Market stress and herding. Journal of Empirical Finance, 11(4), 585–616. https://doi.org/10.1016/j.jempfin.2004.04.003
Jarque, C. M., & Bera, A. K. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters, 6(3), 255–259. https://doi.org/10.1016/0165-1765(80)90024-5
Jeon, D., Kim, C., Park, J., & Jang, B.-G. (2024). Cryptocurrency market herding and U.S. monetary policy shifts [Working Paper]. SSRN. https://doi.org/10.2139/ssrn.4759813
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291. https://doi.org/10.2307/1914185
Khan, M. A., & Rehman, M. U. (2023). Geopolitical risk and herding behavior in the cryptocurrency market. Finance Research Letters, 58 (Part C), Article 104482. https://doi.org/10.1016/j.frl.2023.104482
Kim, C.-J. (1994). Dynamic linear models with Markov-switching. Journal of Econometrics, 60(1–2), 1–22. https://doi.org/10.1016/0304-4076(94)90036-1
Krolzig, H.-M. (1997). Markov-switching vector autoregressions: Modelling, statistical inference, and application to business cycle analysis. Springer. https://doi.org/10.1007/978-3-642-51684-9
Kyriazis, N. A. (2020). Herding behaviour in digital currency markets: An integrated survey and empirical estimation. Heliyon, 6(8), Article e04752. https://doi.org/10.1016/j.heliyon.2020.e04752
Lo, A. W. (2004). The adaptive markets hypothesis: Market efficiency from an evolutionary perspective. Journal of Portfolio Management, 30(5), 15–29. https://doi.org/10.3905/jpm.2004.442611
Lo, A. W., & Zhang, R. (2024). The Adaptive Markets Hypothesis: An evolutionary approach to understanding financial system dynamics. Oxford University Press. https://doi.org/10.1093/oso/9780199681143.001.0001
Lux, T. (1995). Herd behaviour, bubbles and crashes. The Economic Journal, 105(431), 881–896. https://doi.org/10.2307/2235156
Maynard Smith, J. (1982). Evolution and the theory of games. Cambridge University Press. https://doi.org/10.1017/CBO9780511806292
Mignot, S., & Westerhoff, F. (2025). Revisiting the De Grauwe–Grimaldi exchange-rate model: Heterogeneous agents, oscillatory mispricing, and analytical conditions. Journal of Economic Behavior & Organization, 230, Article 106872. https://doi.org/10.1016/j.jebo.2024.106872
Mnif, E., Jarboui, A., & Mouakhar, K. (2020). How the cryptocurrency market has performed during COVID-19? A multifractal analysis. Finance Research Letters, 36, 101647. https://doi.org/10.1016/j.frl.2020.101647
Philippas, D., Philippas, N., Tziogkidis, P., & Rjiba, H. (2020). Signal-herding in cryptocurrencies. Journal of International Financial Markets, Institutions and Money, 65, 101191. https://doi.org/10.1016/j.intfin.2020.101191
Phillips, P. C. B., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335–346. https://doi.org/10.1093/biomet/75.2.335
Raju, R., & Jagannathan, S. (2024). Empirical analysis of the Adaptive Market Hypothesis using moving-average trading strategies across global indices. International Journal of Finance & Economics, 29(4), 4218–4241. https://doi.org/10.1002/ijfe.2845
Ren, B., & Lucey, B. (2022). A clean, green haven? Examining the relationship between clean energy, clean and dirty cryptocurrencies. Energy Economics, 109, 105951. https://doi.org/10.1016/j.eneco.2022.105951
Sathya, N., & Gayathiri, R. (2024). Behavioral biases in investment decisions: An extensive literature review and pathways for future research. Journal of Information and Organizational Sciences, 48(1), 159–186.
Schmitt, N., & Westerhoff, F. (2019). Trend followers, contrarians and fundamentalists: Explaining the dynamics of financial markets. Journal of Economic Behavior & Organization, 192, 117–136. https://doi.org/10.1016/j.jebo.2021.10.005
Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6(2), 461–464. https://doi.org/10.1214/aos/1176344136
Shiller, R. J. (2003). From efficient markets theory to behavioral finance. Journal of Economic Perspectives, 17(1), 83–104. https://doi.org/10.1257/089533003321164967
Susana, D., Kavisanmathi, J. K., & Sreejith, S. (2024). Herding behavior in the cryptocurrency market: Evidence from COVID-19, Russia–Ukraine war, and Palestine–Israel conflict. Cogent Economics & Finance, 12(1), Article 2437022. https://doi.org/10.1080/23322039.2024.2437022
Taylor, P. D., & Jonker, L. B. (1978). Evolutionary stable strategies and game dynamics. Mathematical Biosciences, 40(1–2), 145–156. https://doi.org/10.1016/0025-5564(78)90077-9
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131. https://doi.org/10.1126/science.185.4157.1124
Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters, 148, 80–82. https://doi.org/10.1016/j.econlet.2016.09.019
Vidal-Tomás, D., Ibáñez, A. M., & Farinós, J. E. (2019). Herding in the cryptocurrency market: CSSD and CSAD approaches. Finance Research Letters, 30, 181–186. https://doi.org/10.1016/j.frl.2018.09.008
Yarovaya, L., Matkovskyy, R., & Jalan, A. (2021). The effects of a “black swan” event (COVID-19) on herding behavior in cryptocurrency markets. Journal of International Financial Markets, Institutions and Money, 75, 101321. https://doi.org/10.1016/j.intfin.2021.101321
Youssef, M., & Waked, S. S. (2022). Herding behavior in the cryptocurrency market during COVID-19 pandemic: The role of media coverage. The North American Journal of Economics and Finance, 62, Article 101752. https://doi.org/10.1016/j.najef.2022.101752
Zhu, F., Zhang, Z., & Sun, Q. (2025). A quantile spillover-driven Markov switching model for volatility forecasting: Evidence from the cryptocurrency market. Mathematics, 13(15), Article 2382. https://doi.org/10.3390/math13152382
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Aras Yolusever
This work is licensed under a Creative Commons Attribution 4.0 International License.
