An empirical behavioral model of liquidity and volatility

Mike, Szabolcs; Farmer, J. Doyne

doi:10.1016/j.jedc.2007.01.025

Cited by 230 publications

(247 citation statements)

References 70 publications

(98 reference statements)

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“…Following models by Engle and Bollerslev (1,2), many stochastic models have been proposed based on statistical studies of financial data to accurately reproduce price dynamics. In contrast to this stochastic approach, economists and physicists using the tools of statistical mechanics have adopted a bottom-up approach to simulate the same macroscopic regularity of price changes, with a focus on the behavior of individual market participants (3)(4)(5)(6)(7)(8)(9)(10). Although the second socalled agent-based approach has provided a qualitative understanding of price mechanisms, it has not yet achieved sufficient quantitative accuracy to be widely accepted by practitioners.…”

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confidence: 99%

Linking agent-based models and stochastic models of financial markets

Feng

Podobnik

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

137

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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.complex systems | power law | scaling laws M odeling price returns has become a central topic in the study of financial markets due to its key role in financial theory and its practical utility. Following models by Engle and Bollerslev (1, 2), many stochastic models have been proposed based on statistical studies of financial data to accurately reproduce price dynamics. In contrast to this stochastic approach, economists and physicists using the tools of statistical mechanics have adopted a bottom-up approach to simulate the same macroscopic regularity of price changes, with a focus on the behavior of individual market participants (3-10). Although the second socalled agent-based approach has provided a qualitative understanding of price mechanisms, it has not yet achieved sufficient quantitative accuracy to be widely accepted by practitioners.Here, we combine the agent-based approach with the stochastic process approach and propose a model based on the empirically proven behavior of individual market participants that quantitatively reproduces fat-tailed return distributions and long-term memory properties (11-14). Empirical and Theoretical Market BehaviorsWe start by arguing that technical traders (usually agents seeking arbitrage opportunities and make their trading decisions based on price patterns) contribute much more to the dynamics of daily stock prices S t (or log price s t ≡ lnðS t Þ) than fundamentalists (who attempt to determine the fundamental values of stocks). Although fundamentalists hold a majority of the stocks, they trade infrequently (see SI Appendix, Fig. S6). In contrast, technical traders contribute most of the trading activities (15) by trading their minority h...

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confidence: 99%

Linking agent-based models and stochastic models of financial markets

Feng

Podobnik

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

137

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“…As each incoming cancelation order arrives, the market agent will delete the relevant limit order in the order book. In order to ensure that the order flows generated by the artificial market are economically plausible, all the parameters in our model are derived from empirical evidence [4,7,8,15,21]. The parameters used in our simulation are presented in Table 5.…”

Section: Simulating An Artificial Marketmentioning

confidence: 99%

Evolving Dynamic Trade Execution Strategies Using Grammatical Evolution

Cui

Brabazon

O’Neill

2010

Applications of Evolutionary Computation

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Abstract. Although there is a plentiful literature on the use of evolutionary methodologies for the trading of financial assets, little attention has been paid to potential use of these methods for efficient trade execution. Trade execution is concerned with the actual mechanics of buying or selling the desired amount of a financial instrument of interest. Grammatical Evolution (GE) is an evolutionary automatic programming methodology which can be used to evolve rule sets. In this paper we use a GE algorithm to discover dynamic, efficient, trade execution strategies which adapt to changing market conditions. The strategies are tested in an artificial limit order market. GE was found to be able to evolve quality trade execution strategies which are highly competitive with two benchmark trade execution strategies.

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“…We say "more or less" because there is more than one way to characterize their behavior, and one can argue that some of these involve at least some strategic thinking, and so are not strictly speaking zero intelligence. The development of zero intelligence models of the continuous double auction has a long history [75,77,[118][119][120][121][122][123][124][125][126][127][128][129][130].…”

Section: Zero Intelligence Models Of Auctionsmentioning

confidence: 99%