Does more intelligent trading strategy win? Interacting trading strategies: an agent-based approach
DOI:
https://doi.org/10.37380/jisib.v12i3.929Abstract
An artificial financial market is built on top of the Genoa Artificial Stock Market. The market is populated with agents having different trading strategies and they are let to interact with each other. Agents differ in the trading method they use to trade, and they are grouped as noise, technical, statistical analysis, and machine learning traders. The model is validated by the replication of stylized facts in financial asset returns. We were able to replicate the leptokurtic shape of the probability density function, volatility clustering, and the absence of autocorrelation in asset returns. The wealth dynamics for each agent group are analyzed throughout the trading period. Agents with a higher time complexity trading strategy outperform those with a strategy comparing their final wealth.
References
Alfarano S, Lux T, Wagner F. Estimation of agent-based models: the case of an asymmetric herding model. Computational Economics. 2005; 26(1); 19-49.
Ariyo AA, Adewumi AO, Ayo CK. ‘Stock price prediction using the ARIMA model’. UKSim-AMSS 16th International Conference on Computer Modelling and Simulation. IEEE. 2014; pp. 106-112.
Bollerslev T. Generalized autoregressive conditional heteroskedasticity. Journal of econometrics. 1986; 31(3); 307-327.
Bouchaud J. Economics needs a scientific revolution. Nature. 2008; 455(7217); pp.1181-1181.
Brock W, Lakonishok J, LeBaron B. Simple technical trading rules and the stochastic properties of stock returns. The Journal of finance. 1992; 47(5), 1731-1764.
Brown SJ, Warner JB. Using daily stock returns: The case of event studies. Journal of financial economics. 1985; 14(1); 3-31.
Chakraborti A, Toke IM, Patriarca M, Abergel F. Econophysics review: II. Agent-based models. Quantitative Finance. 2011; 11(7); 1013-1041.
Chen SH, Yeh CH. Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market. Journal of Economic Dynamics and Control. 2001; 25(3-4); 363-393.
Cont R. Empirical properties of asset returns: stylized facts and statistical issues. Quantitative Finance. 2001; 1(2); pp.223-236.
Cristelli M, Pietronero L, Zaccaria, A. Critical overview of agent-based models for economics. arXiv preprint arXiv:1101.1847. 2011.
Dieci R, He X. Handbook of computational economics. 4th ed. San Diego: Elsevier Science & Technology. 2018; pp.257-328.
Engle RF. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society. 1982; 987-1007.
Fama E. The behavior of stock prices, Journal of Business. 1965; 38, 34–105.
Farmer J, Foley D. The economy needs agent-based modelling. Nature. 2009; 460(7256); pp.685-686.
Farmer JD, Patelli P, Zovko II. The predictive power of zero intelligence in financial markets. Proceedings of the National Academy of Sciences. 2005; 102(6); 2254-2259.
Gode DK, Sunder S. ‘Double auction dynamics: structural effects of non-binding price controls’, Journal of economic dynamics and control. 2004; 28; 1707–1731.
Hochreiter S, Schmidhuber J. Long short-term memory. Neural computation. 1997; 9(8); 1735-1780.
Hott C. Herding behavior in asset markets. Journal of Financial Stability. 2009; 5(1); 35-56.
Hsieh DA. Testing for nonlinear dependence in daily foreign exchange rates. Journal of Business. 1989; 339-368.
Kahneman D, Tversky A. Prospect Theory: An Analysis of Decision under Risk. Econometrica. 1979; 47(2); p.263.
Kwon KY, Kish RJ. Technical trading strategies and return predictability: NYSE. Applied Financial Economics. 2002;12(9); 639-653.
LeBaron B. A builder's guide to agent-based financial markets. Quantitative finance. 2001; 1(2); 254.
LeBaron B. Agent-based computational finance. Handbook of computational economics. 2006; 2; 1187-1233.
LeBaron B. Building the Santa Fe artificial stock market. Physica A. 2002; 1-20.
LeBaron B. Calibrating an agent-based financial market. Working paper, Graduate School of International Economics and Finance, Brandeis University. 2003.
Levy H, Levy M, Solomon S. Microscopic simulation of financial markets: from investor behavior to market phenomena. Elsevier. 2000.
Llacay B, Peffer G. Using realistic trading strategies in an agent-based stock market model. Computational and Mathematical Organization Theory. 2018; 24(3); 308-350.
Lux T, Marchesi M. Scaling and criticality in a stochastic multi-agent model of a financial market. Nature. 1999; 397(6719); 498-500.
Lux T, Schornstein S. Genetic learning as an explanation of stylized facts of foreign exchange markets. Journal of Mathematical Economics. 2005; 41(1-2); 169-196.
Macal C, North M. Tutorial on agent-based modelling and simulation. Journal of Simulation. 2010; 4(3); pp.151-162.
Marchesi M, Cincotti S, Focardi S, Raberto M. The Genoa Artificial Stock Market: Microstructure and Simulations. Lecture Notes in Economics and Mathematical Systems. 2003; pp. 277-289.
Market capitalization of listed domestic companies. (2020). Retrieved 10 Jan 2022, from https://data.worldbank.org/indicator/CM.MKT.LCAP.CD
Martinez-Jaramillo S, Tsang EP. An heterogeneous, endogenous and coevolutionary GP-based financial market. IEEE Transactions on Evolutionary Computation. 2009; 13(1); 33-55.
Maymin PZ. The minimal model of financial complexity. Quantitative Finance. 2011; 11(9); 1371-1378.
Nelson DM, Pereira AC, De Oliveira RA. ‘Stock market's price movement prediction with LSTM neural networks’. International joint conference on neural networks (IJCNN). IEEE. 2017; pp. 1419-1426.
Palit I, Phelps S, Ng WL. Can a zero-intelligence plus model explain the stylized facts of financial time series data?. In International Conference on Autonomous Agents and Multiagent Systems. International Foundation for Autonomous Agents and Multiagent Systems. 2012; (pp. 653-660).
Ponta L, Scalas E, Raberto M, Cincotti S. Statistical analysis and agent-based microstructure modeling of high-frequency financial trading. IEEE Journal of selected topics in signal processing. 2011; 6(4); 381-387.
Raberto M, Cincotti S, Focardi SM, Marchesi M. Agent-based simulation of a financial market. Physica A: Statistical Mechanics and its Applications. 2001; 299(1-2); 319-327.
Roondiwala M, Patel H, Varma S. Predicting stock prices using LSTM. International Journal of Science and Research (IJSR). 2017; 6(4); 1754-1756.
Samanidou E, Zschischang E, Stauffer D, Lux T. Agent-based models of financial markets. Reports on Progress in Physics. 2007; 70(3); 409.
Siami-Namini S, Tavakoli N, Namin AS. A comparison of ARIMA and LSTM in forecasting time series. 17th IEEE international conference on machine learning and applications (ICMLA), IEEE. 2018; pp. 1394-1401.
Tsay RS. Analysis of financial time series. John wiley & sons. 200; 543.
Vyetrenko S, Byrd D, Petosa N, Mahfouz M, Dervovic D, Veloso M, Balch T. Get real: Realism metrics for robust limit order book market simulations. In Proceedings of the First ACM International Conference on AI in Finance. 2020; pp. 1-8.
Wilder JW. New concepts in technical trading systems. Trend Research. 1978.
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