Please use this identifier to cite or link to this item: https://doi.org/10.1073/pnas.1205013109
Title: Linking agent-based models and stochastic models of financial markets
Authors: Feng, L. 
Li, B. 
Podobnik, B.
Preis, T.
Stanley, H.E.
Keywords: Complex systems
Power law
Scaling laws
Issue Date: 29-May-2012
Source: Feng, L., Li, B., Podobnik, B., Preis, T., Stanley, H.E. (2012-05-29). Linking agent-based models and stochastic models of financial markets. Proceedings of the National Academy of Sciences of the United States of America 109 (22) : 8388-8393. ScholarBank@NUS Repository. https://doi.org/10.1073/pnas.1205013109
Abstract: It is well-known that financial asset returns exhibit fat-tailed distributions and long-term memory. These empirical features are the main objectives of modeling efforts using (i) stochastic processes to quantitatively reproduce these features and (ii) agent-based simulations to understand the underlying microscopic interactions. After reviewing selected empirical and theoretical evidence documenting the behavior of traders, we construct an agent-based model to quantitatively demonstrate that "fat" tails in return distributions arise when traders share similar technical trading strategies and decisions. Extending our behavioral model to a stochastic model, we derive and explain a set of quantitative scaling relations of long-term memory from the empirical behavior of individual market participants. Our analysis provides a behavioral interpretation of the long-term memory of absolute and squared price returns: They are directly linked to the way investors evaluate their investments by applying technical strategies at different investment horizons, and this quantitative relationship is in agreement with empirical findings. Our approach provides a possible behavioral explanation for stochastic models for financial systems in general and provides a method to parameterize such models from market data rather than from statistical fitting.
Source Title: Proceedings of the National Academy of Sciences of the United States of America
URI: http://scholarbank.nus.edu.sg/handle/10635/53012
ISSN: 00278424
DOI: 10.1073/pnas.1205013109
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