Yapay Finansal Market Modelleme: Ajan Temelli Yaklaşım

Yıl 2021, Sayı: Özel Sayı 2, 71 - 96, 02.02.2021

Hidayet Beyhan , Burç Ülengin

https://doi.org/10.33203/mfy.849275

Öz

Finans ve iktisatta rasyonellik ve homojenlik konusundaki geleneksel görüş, teorik sınırlamalar ve artan ampirik bulgular ile sorgulanmaya başlanmıştır. Bu, rasyonel beklentileri olan ajanları temsil etmekten heterojen beklentileri olan rasyonel ajanlara doğru bir paradigma değişikliğine yol açmaktadır. Bu bağlamda, bu çalışma, ajanlar üzerindeki heterojenliği daha iyi ele almak ve aralarındaki etkileşimi yakalamak için ajan temelli bir finansal piyasayı modellemeyi amaçlamaktadır.

Raberto ve diğerleri (2001) tarafından sunulan Genoa piyasa modeline dayanarak, ilkel bir ajan tabanlı yapay finansal piyasa yaratılmıştır. Modelin geçerliliğini sağlamak için finansal varlık getirilerinin “stylized fact” olgusunu tekrarlamayı hedefliyoruz. Ajanlara önceden belirlenmiş nakit ve varlık tutarı atanır. Ajan tabanlı simülasyon farklı senaryolar altında yapılarak sonuçlar incelendi. Ajanlar, alım satım yaparken gürültücü yatırımcı veya teknik indikatörleri kullanan bir ajan olarak farklılık gösterir. Model olasılık yoğunluk fonksiyonunun leptokurtik şekli, getirilerin otokorelasyon yokluğu ve uçuculuk kümelenmesi özelliklerini tekrarlamıştır.

Anahtar Kelimeler

Yapay Finansal Piyasa, Ajan Temelli Simulasyon, Heterojen Ajanlar

Modelling an Artificial Financial Market: Agent Based Approach

Öz

The traditional view of perfect rationality and homogeneity in finance and economics has been challenged by growing evidence on the theoretical limitations and empirical findings. That leads a paradigm shift from representing agents with rational expectations to boundedly rational agent having heterogenous expectations. In this regard, this study aims to model a financial market with agent-based approach to deal better with heterogeneity over agents and to capture the interaction among them.

A primitive agent-based artificial financial market is created based on the Genoa market model introduced by Raberto et al., (2001). We aim to replicate the stylized fact of financial asset returns to assure validity of model. Agents are endowed with prespecified cash and assets amount. Agents based simulation is run under different scenarios and results are examined. Agents differ when trading as being noise trader or an agent using technical trading. The model was able to replicate leptokurtic shape of probability density function, absence of autocorrelation and volatility clustering.

Anahtar Kelimeler

Artificial Financial Market, Agent Based Simulation, Heterogenous Agents

Kaynakça

• Alfi, V., M. Cristelli, L. Pietronero, and A. Zaccaria (2009), Minimal agent based model for financial markets I: origin and self-organization of stylized fatcs, The European Physical Journal B, 67(3), 385– 397.
• Aloud, M., E. Tsang, and R. Olsen (2012), Modelling the FX market traders’ behaviour: an agentbased approach, Chapter 15, in Simulation in Computational Finance and Economics: Tools and Emerging Applications, edited by B. Alexandrova-Kabadjova, S. Martinez-Jaramillo, A. Garcia-Almanza, and E. Tsang, IGI Global, Hershey, Pennsylvania, 202-228.
• Arifovic, J. (1994), Genetic algorithm learning and the cobweb model, Journal of Economic Dynamics & Control, 18, 3–28
• Arifovic, J. (2001), Evolutionary dynamics of currency substitution, Journal of Economic Dynamics & Control, 25, 395–417.
• Arthur, W. (1991), Designing economic agents that act like human agents: a behavioral approach to bounded rationality, American Economic Review, 81, 353–359.
• Arthur, W. B., J. H. Holland, B. LeBaron, R. Palmer, and P. Tayler (1997), Asset pricing under endogenous expectations in an artificial stock market, in The economy as an evolving, complex system II, edited by W. Arthur, D. Lane, and S. Durlauf, pp. 15–44, Addison Wesley, Redwood City, CA
• Bachlier, L. (1964), Theory of speculation in the random character of stock market prices, MIT Press,Cambridge.
• Bouchaud, J. P., & Cont, R. (2000). Herd behaviour and aggregate fluctuations in financial market. Macroeconomic Dynamics, 2, 170-196.
• Brooks, C. (1996), Testing for non-linearity in daily sterling exchange rates, Applied Financial Economics, 6(4), 307–317
• Cincotti, S., Focardi, S. M., Marchesi, M., & Raberto, M. (2003). Who wins? Study of long-run trader survival in an artificial stock market. Physica A: Statistical Mechanics and its Applications, 324(1-2), 227-233.
• Cincotti, S., L. Ponta, and M. Raberto (2005), A multi-assets artifcial stock market with zerointelligence traders, In WEHIA , Essex, United Kingdom
• Cowles, A. (1933), Can stock market forecasters forecast?, Econometrica, 1 (3), 309–324.
• Dacorogna, M., R. Gençay, U. Müller, R. Olsen, and O. Pictet (2001), An introduction to highfrequency finance, Academic Press, San Diego.
• Ehrentreich, N. (2008). Agent-Based Modeling: The Santa Fe Institute Artificial Stock Market Model Revisited. Berlin/Heidelberg: Springer
• Fama, E. (1965), The behavior of stock prices, Journal of Business, 38, 34–105.
• Farmer, J., and S. Joshi (2002), The price dynamics of common trading strategies, Journal of Economic Behavior & Organization, 49(2), 149–171
• Grossman, S., and J. Stiglitz (1980), On the impossibility of informationally efficient markets, The American Economic Review, 70(3), 393–408
• He, X., Li, Y., (2017), The adaptiveness in stock markets: testing the stylized facts in the DAX 30. Journal of Evolutionary Economics 27 (5), 1071–1094.
• Hommes, C., and LeBaron, B. (2018), Handbook of Computational Economics, Volume 4 Heterogeneous Agent Modeling.
• Iori, G. (2002), A microsimulation of traders activity in the stock market: the role of heterogeneity, agents interactions and trade frictions, J. Econ. Behav. Organ, 49, 269–285
• Kononovicius, A., and V. Gontis (2012), Agent based reasoning for the non-linear stochastic models of long-range memory.
• LeBaron, B. (2001), Evolution and time horizons in an agent-based stock market, Macroeconomic Dynamics, 5, 225–254
• LeBaron, B. (2003). Calibrating an agent-based financial market. Working paper, Graduate School of International Economics and Finance, Brandeis University.
• LeBaron, B. (2006), Agent-based computational finance, in Handbook of Computational Economics, vol. 2, edited by L. Tesfatsion and K. L. Judd, 1 ed., chap. 24, pp. 1187–1233, Elsevier.
• Levy, M., and S. Solomon (1996), Dynamical explanation for the emergence of power law in a stock market model, International Journal of Modern Physics C, 7, 65–72
• LiCalzi, M., and P. Pellizzari (2003), Fundamentalists clashing over the book: a study of order-driven stock markets, Quantitative Finance, 3, 470–480
• Lo, A. (1988), Stock market prices do not follow random walks: evidence from a simple specification test, Review of Financial Studies, 1 (1), 41–66.
• Lux, T., and M. Marchesi (2000), Volatility clustering in financial markets: a microsimulation of interacting agents, International Journal of Theoretical and Applied Finance, 3, 675–702
• Lux, T., Alfarano, S., (2016). Financial power laws: empirical evidence, models, and mechanisms. Chaos, Solitons and Fractals 88, 3–18.
• Martinez-Jaramillo, S., and E. Tsang (2009), An heterogeneous, endogenous and co-evolutionary GP-based financial market, IEEE Transactions on Evolutionary Computation, 13(1), 33–55.
• Martinez-Jaramillo, S., and E. Tsang (2009), An heterogeneous, endogenous and co-evolutionary GP-based financial market, IEEE Transactions on Evolutionary Computation, 13(1), 33–55.
• Poggio, T. and Lo, A. W. and LeBaron, B. and Chan, N. T. (2001), Agent-Based Models of Financial Markets: A Comparison with Experimental Markets. MIT Sloan Working Paper No. 4195-01. Available at SSRN: https://ssrn.com/abstract=290140 or http://dx.doi.org/10.2139/ssrn.290140
• Raberto, M., Cincotti, S., Focardi, S. and Marchesi, M. (2001). Agent-based simulation of a financial market. Physica A: Statistical Mechanics and its Applications, 299(1-2), pp.319-327.
• Tirole, J. (1982), On the possibility of speculation under rational expectations, Econometrica, 50 (5), 1163–1181.
• Tsang, E., and S. Martinez-Jaramillo (2004), Computational finance, IEEE Computational Intelligence Society Newsletter, pp. 3–8.
• Winker, P., & Gilli, M. (2001). Indirect estimation of the parameters of agent based models of financial markets. FAME International center for financial asset management and engineering.