Agent-based computational modeling of the stock price–volume relation
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)
- et al.
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) - et al.
On the emergent properties of artificial stock markets
Journal of Economic Behavior and Organization
(2002) Inference and causality in economic time series models
Autoregressive modelling and money-income causality detection
Journal of Monetary Economics
(1981)Agent-based computational finance: suggested readings and early research
Journal of Economic Dynamics and Control
(2000)- et al.
Time series properties of an artificial stock market
Journal of Economic Dynamics and Control
(1999) - et al.
Artificial economic life: a simple model of a stock market
Physica D
(1994) - et al.
The stock price–volume relationship in emerging stock markets: the case of Latin America
International Journal of Forecasting
(1998) - et al.
Testing for linear and nonlinear Granger causality in the stock price–volume relation: Korean evidence
Quarterly Review of Economics and Finance
(1999) - et al.
An empirical analysis of the stock price–volume relationship
Journal of Banking and Finance
(1988)