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Linking agent-based models and stochastic models of financial markets

Proceedings of the National Academy of Sciences - PNAS, 2012-05, Vol.109 (22), p.8388-8393 [Peer Reviewed Journal]

copyright © 1993-2008 National Academy of Sciences of the United States of America ;Copyright National Academy of Sciences May 29, 2012 ;ISSN: 0027-8424 ;EISSN: 1091-6490 ;DOI: 10.1073/pnas.1205013109 ;PMID: 22586086

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Title:
Linking agent-based models and stochastic models of financial markets
Author: Feng, Ling ; Li, Baowen ; Podobnik, Boris ; Preis, Tobias ; Stanley, H. Eugene
Subjects: Behavior modeling ; Commerce - economics ; Commerce - statistics & numerical data ; Commerce - trends ; Economic models ; Empirical evidence ; Experimental data ; financial assets ; Financial Management - economics ; Financial Management - statistics & numerical data ; Financial Management - trends ; Financial markets ; Investment horizon ; Investments ; Investments - economics ; Investments - statistics & numerical data ; Investments - trends ; Investors ; Marketing - economics ; Marketing - statistics & numerical data ; Marketing - trends ; markets ; Memory ; Models, Economic ; Physical Sciences ; Price changes ; prices ; Securities markets ; Simulation ; Social Sciences ; Stochastic models ; Stochastic Processes ; Stock shares
Is Part Of: Proceedings of the National Academy of Sciences - PNAS, 2012-05, Vol.109 (22), p.8388-8393
Description: 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.
Publisher: United States: National Academy of Sciences
Language: English
Identifier: ISSN: 0027-8424
EISSN: 1091-6490
DOI: 10.1073/pnas.1205013109
PMID: 22586086
Source: GFMER Free Medical Journals
MEDLINE
PubMed Central

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Linking agent-based models and stochastic models of financial markets

copyright © 1993-2008 National Academy of Sciences of the United States of America ;Copyright National Academy of Sciences May 29, 2012 ;ISSN: 0027-8424 ;EISSN: 1091-6490 ;DOI: 10.1073/pnas.1205013109 ;PMID: 22586086

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