Evolving traders and the business school with genetic programming: A new architecture of the agent-based artificial stock market

Genetic Algorithms and Genetic Programming in Computational Finance

Liao

2002

Self Cite

This paper studies the behavior of price discovery within a context of an agent based stock market, in which the twin assumptions, namely, rational expectations and the representative agents normally made in mainstream economics, are removed. In this model, traders stochastically update their forecasts by searching the business school whose evolution is driven by genetic programming. Via these agent based simulations, it is found that, except for some extreme cases, the mean prices generated from these artificial markets deviate from the homogeneous rational expectation equilibrium (HREE) prices no more than by 20%. This figure provides us a rough idea on how different we can possibly be when the twin assumptions are not taken. Furthermore, while the HREE price should be a deterministic constant in all of our simulations, the artificial price series generated exhibit quite wild fluctuation, which may be coined as the well-known excessive volatility in finance. Keywords: Price Discovery, Homogeneous Rational Expectation Equilibrium, Genetic Programming, Agent-Based Computational Finance, Excessive Volatility Motivation and IntroductionIt has been argued that standard asset pricing model based on the twin assumptions, the representative agent and rational expectations hypothesis, can only lead to uninteresting dynamics, which can be anything but the real world. For example, under very regular conditions, the market can end up with the wellknown zero-trade theorem (Tirole, 1982). While there are several possibilities to escape from this no-trade conundrum, recent studies based on agent-based computational finance (ABCF) indicate that we can have almost everything simply by giving up the twin assumptions 1 . Nonetheless, an important issue generally left unexploited is: under what circumstances and on what aspects, can we still regard the standard asset pricing model with its homogeneous rational expectation equilibrium (HREE) as a reasonable approximation to the dynamics generated by the ABCF methodology.In this paper, we shall start the analysis from the aspect of price discovery. We are asking how well the HREE price can predict the movement of the price dynamics generated by an agent-based stock market. Basically, we start from a standard asset pricing model (Grossman and Stiglitz, 1980) and use the HREE price as the reference. We then build an agent-based computational version of the standard asset pricing model and generate the price dynamics from there. The price series generated will further be compared with the HREE price.

Section: Discussionmentioning

confidence: 99%

“…Due to the space limit, we are not able to give the detail of the program. The interested reader is referred to Chen and Yeh (2000). …”

Section: Agent-based Modeling Of Artificial Stock Marketsmentioning

confidence: 99%

Section: Experimental Designsmentioning

confidence: 99%

Section: Motivation and Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Price Discovery in Agent-Based Computational Modeling of the Artificial Stock Market

Genetic Algorithms and Genetic Programming in Computational Finance

Liao

2002

Self Cite

“…10 This hypothesis can be loosely connected to the famous Occam's razor or the parsimony principle. 11 While Chen and Yeh (2001) simulate the evolving complexity of software agents in the artificial stock markets, the empirical counterpart of human behavior is not available in the literature. The establishment of a CE Lab would help.…”

Section: Acknowledgementsmentioning

confidence: 99%

On the Selection of Adaptive Algorithms in ABM: A Computational-Equivalence Approach

Tai

2006

Comput Econ

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Abstract. Agent-based Methodology (ABM) is becoming indispensable for the interdisciplinary study of social and economic complex adaptive systems. The essence of ABM lies in the notion of autonomous agents whose behavior may evolve endogenously and can generate and mimic the corresponding complex system dynamics that the ABM is studying. Over the past decade, many Computational Intelligence (CI) methods have been applied to the design of autonomous agents, in particular, their adaptive schemes. This design issue is non-trivial since the chosen adaptive schemes usually have a profound impact on the generated system dynamics. Robert Lucas, one of the most influential modern economic theorists, has suggested using laboratories with human agents, also known as Experimental Economics, to help solve the selection issue. While this is a promising approach, laboratories used in the current experimental economics are not computationally equipped to meet the demands of the selection task. This paper attempts to materialize Lucas' suggestion by establishing a laboratory where human subjects are equipped with the computational power that satisfies the computational equivalence condition.

Financial Applications: Stock Markets

Wiley Encyclopedia of Computer Science and Engineering

2009

The integration of computer science, computer technology, and finance has given rise to the field known as computational finance. Depending on its involvement in economic theory and its contribution to economic theory, computational finance can be divided into financial engineering and agent‐based computational finance. Financial engineering can be defined as the application of computational tools or algorithms to tackle some already existing financial optimization problems, such as forecasting, investment, portfolios, trading, and pricing. These applications usually do not challenge the fundamental principles (assumptions or axioms) from which the optimization problems are derived. They simply take advantage of the increasing computational power to address great technical difficulties inherent in financial optimization, such as nonlinearities and high dimensionality. Compared to financial engineering, agent‐based computational finance has a more general goal. It questions the foundations up on which mainstream economics and finance are built, and it reexamines some missing elements, which were absent mainly due to the limited computational resources. Then, by bringing back these elements, agent‐based computational finance attempts to provide us with a better understanding of the financial market dynamics.