Elsevier

Information Sciences

Volume 170, Issue 1, 18 February 2005, Pages 75-100
Information Sciences

Agent-based computational modeling of the stock price–volume relation

https://doi.org/10.1016/j.ins.2003.03.026Get rights and content

Abstract

From the perspective of the agent-based model of stock markets, this paper examines the possible explanations for the presence of the causal relation between stock returns and trading volume. Using the agent-based approach, we find that the explanation for the presence of the stock price–volume relation may be more fundamental. Conventional devices such as information asymmetry, reaction asymmetry, noise traders or tax motives are not explicitly required. In fact, our simulation results show that the stock price–volume relation may be regarded as a generic property of a financial market, when it is correctly represented as an evolving decentralized system of autonomous interacting agents. One striking feature of agent-based models is the rich profile of agents' behavior. This paper makes use of the advantage and investigates the micro–macro relations within the market. In particular, we trace the evolution of agents' beliefs and examine their consistency with the observed aggregate market behavior. We argue that a full understanding of the price–volume relation cannot be accomplished unless the feedback relation between individual behavior at the bottom and aggregate phenomena at the top is well understood.

Section snippets

Motivation and introduction

The agent-based modeling of stock markets, which originated at the Santa Fe Institute [2], [47], is a fertile and promising field that can be thought of as a subfield of agent-based computational economics (ACE).

The agent-based artificial stock market

The ABSM considered in this paper is the AIE-ASM, Version 3, developed by the AI-ECON Research Center [13], [15]. The basic framework of the AIE-ASM is the standard asset pricing model in the vein of Grossman and Stiglitz [29]. The dynamics of the market are determined by the interactions of many heterogeneous agents. Each of them, based on his forecast of the future, maximizes his expected utility.

Experimental designs

As mentioned earlier, our simulations are based on the software, AIE-ASM, Version 3. A tutorial on this software can be found in [15]. This tutorial explains most of the parameters shown in Table 1, the details of which we shall skip except for mentioning that most parameter values are taken from [13]. The simulations presented in this paper are mainly based on three different designs. These designs are motivated by our earlier studies on the ABSM, in particular [10], [13], [14]. These three

Wiener–Granger causality: definition and testing

The concept of causality plays a crucial role in many empirical economic studies, and is particularly important for our understanding and interpretation of dynamic economic phenomena. Nevertheless, it is difficult to give a formal notion of causality. This issue, in fact, is a philosophical one (see, e.g., Geweke [26]). Wiener [55], however, proposed a widely accepted concept of causality based on the predictive relation between the two time series in question. This notion of causality, known

Results of experiments

We first summarize some basic descriptive statistics of our simulation results in Table 3.

Conclusions

One distinguishing feature of ACE (and thus ABSMs) is that some interesting macro-phenomena of financial markets could emerge (be endogenously generated) from interactions among adaptive agents without exogenously imposing any conditions like unexpected events, information cascades, noise or dumb traders, etc. In this paper, we show that the presence of the stock price–volume causal relation does not require any explicit assumptions like information asymmetry, reaction asymmetry, noise traders,

References (58)

  • R. Antoniewicz, A Causal Relationship Between Stock Returns and Volume, vol. 208, Board of Governors of the Federal...
  • W.B. Arthur et al.

    Asset pricing under endogenous expectations in an artificial stock market

  • E. Baek, W.A. Brock, A general test for nonlinear Granger causality: bivariate model, unpublished manuscript,...
  • W.A. Brock

    Causality, Chaos, Explanation and Prediction in Economics and Finance

  • W.A. Brock

    Pathways to randomness in the economy: emergent nonlinearity and chaos in economics and finance

    Estudios Economicos

    (1993)
  • J.Y. Campbell et al.

    Trading volume and serial correlation in stock returns

    Quarterly Journal of Economics

    (1993)
  • J.Y. Campbell et al.

    The Econometrics of Financial Markets

    (1997)
  • N.T. Chan, B. LeBaron, A.W. Lo, T. Poggio, Agent-based models of financial markets: a comparison with experimental...
  • S.-H. Chen, Agent-based computational macroeconomics: a survey, in: Proceedings of the Second International Workshop on...
  • S.-H. Chen et al.

    Price discovery in agent-based computational modeling of artificial stock markets

  • S.-H. Chen, C.-C. Liao, Understanding sunspots: an analysis based on agent-based artificial stock markets, AI-ECON...
  • S.-H. Chen, C.-C. Liao, Excess volatility in agent-based artificial stock markets, AI-ECON Research Center Working...
  • S.-H. Chen et al.

    On AIE-ASM: a software to simulate artificial stock markets with genetic programming

  • P. Clark

    A subordinated stochastic process model with finite variances for speculative prices

    Econometrica

    (1973)
  • T. Copeland

    A model of asset trading under the assumption of sequential information arrival

    Journal of Finance

    (1976)
  • J. DeLong et al.

    Positive feedback investment strategies and destabilizing speculation

    Journal of Finance

    (1990)
  • C. Diks et al.

    A general nonparametric bootstrap test for Granger causality

  • T.W. Epps

    Security price changes and transactions volumes: theory and evidences

    American Economic Review

    (1975)
  • T. Epps et al.

    The stochastic dependence of security price changes and transaction volumes: implications for the mixture distributions hypothesis

    Econometrica

    (1976)
  • Cited by (0)

    View full text