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Kinetic model for asset allocation with strategy switching

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  • Hu, Chunhua
  • Feng, Huarong

Abstract

The kinetic theory is employed to study the changes in the amount of wealth invested in two types of risky assets when investors switch their trading strategies. Investors in the market are assumed to possess nonnegative wealth and opt to allocate a portion of their wealth into two types of risky assets. A Boltzmann model, which includes strategy switching and interaction probability, is utilized to investigate the evolution of the amount of wealth invested in risky assets and the number of investors using fundamentalist and chartist strategies. We employ the asymptotic procedure method to derive the Fokker-Planck equations from the Boltzmann model. The resulting stationary solution of these equations sheds light on the effects of strategy switching on the evolution of the amount of wealth invested in risky assets, as well as how investor interactions influence the number of investors employing different strategies.

Suggested Citation

  • Hu, Chunhua & Feng, Huarong, 2024. "Kinetic model for asset allocation with strategy switching," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 636(C).
    • Handle: RePEc:eee:phsmap:v:636:y:2024:i:c:s0378437124000256
    DOI: 10.1016/j.physa.2024.129517
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    1. Philip A. Stork & Milan Vidojevic & Remco C. J. Zwinkels, 2021. "Behavioral heterogeneity in return expectations across equity style portfolios," International Review of Finance, International Review of Finance Ltd., vol. 21(4), pages 1225-1250, December.
    2. Farias Nazário, Rodolfo Toríbio & e Silva, Jéssica Lima & Sobreiro, Vinicius Amorim & Kimura, Herbert, 2017. "A literature review of technical analysis on stock markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 66(C), pages 115-126.
    3. Yamamoto, Ryuichi, 2012. "Intraday technical analysis of individual stocks on the Tokyo Stock Exchange," Journal of Banking & Finance, Elsevier, vol. 36(11), pages 3033-3047.
    4. Novy-Marx, Robert, 2013. "The other side of value: The gross profitability premium," Journal of Financial Economics, Elsevier, vol. 108(1), pages 1-28.
    5. Thomas Lux & Michele Marchesi, 2000. "Volatility Clustering In Financial Markets: A Microsimulation Of Interacting Agents," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 3(04), pages 675-702.
    6. Ling Zhang & Wenlong Bian & Hao Zhang, 2019. "Dissecting the myth of the house price in Chinese metropolises: allowing for behavioral heterogeneity among investors," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 14(4), pages 721-740, December.
    7. Nishiwaki, Takashi, 2022. "Impact of different investment horizons in heterogeneous agent models: Do long-term traders bring market stability?," Journal of Economic Behavior & Organization, Elsevier, vol. 196(C), pages 393-401.
    8. Massoud Metghalchi & Linda A. Hayes & Farhang Niroomand, 2019. "A technical approach to equity investing in emerging markets," Review of Financial Economics, John Wiley & Sons, vol. 37(3), pages 389-403, July.
    9. Huang, Jing-Zhi & Huang, Zhijian (James), 2020. "Testing moving average trading strategies on ETFs," Journal of Empirical Finance, Elsevier, vol. 57(C), pages 16-32.
    10. Thomas Lux & Michele Marchesi, 1999. "Scaling and criticality in a stochastic multi-agent model of a financial market," Nature, Nature, vol. 397(6719), pages 498-500, February.
    11. Zhong, Yue & Lai, Shaoyong & Hu, Chunhua, 2021. "Investigations to the dynamics of wealth distribution in a kinetic exchange model," Applied Mathematics and Computation, Elsevier, vol. 404(C).
    12. Düring, Bertram & Toscani, Giuseppe, 2008. "International and domestic trading and wealth distribution," CoFE Discussion Papers 08/02, University of Konstanz, Center of Finance and Econometrics (CoFE).
    13. Jeon, Junkee & Park, Kyunghyun, 2023. "Optimal job switching and retirement decision," Applied Mathematics and Computation, Elsevier, vol. 443(C).
    14. U. Garibaldi & E. Scalas & P. Viarengo, 2007. "Statistical equilibrium in simple exchange games II. The redistribution game," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 60(2), pages 241-246, November.
    15. Adrian Fernandez-Perez & Bart Frijns & Alireza Tourani-Rad & Jean-Philippe Weisskopf, 2019. "Behavioural heterogeneity in wine investments," Applied Economics, Taylor & Francis Journals, vol. 51(30), pages 3236-3255, June.
    16. E. Scalas & U. Garibaldi & S. Donadio, 2007. "Statistical equilibrium in simple exchange games I," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 60(2), pages 271-272, November.
    17. Bartram, Söhnke M. & Grinblatt, Mark, 2018. "Agnostic fundamental analysis works," Journal of Financial Economics, Elsevier, vol. 128(1), pages 125-147.
    18. William A. Brock & Cars H. Hommes, 2001. "A Rational Route to Randomness," Chapters, in: W. D. Dechert (ed.), Growth Theory, Nonlinear Dynamics and Economic Modelling, chapter 16, pages 402-438, Edward Elgar Publishing.
    19. Maldarella, Dario & Pareschi, Lorenzo, 2012. "Kinetic models for socio-economic dynamics of speculative markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(3), pages 715-730.
    20. Torsten Trimborn & Lorenzo Pareschi & Martin Frank, 2017. "Portfolio Optimization and Model Predictive Control: A Kinetic Approach," Papers 1711.03291, arXiv.org, revised Feb 2019.
    21. Meine, David C.A. & Vvedensky, Dimitri D., 2023. "Emergence of speculation in a hierarchical agent-based model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 620(C).
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