Economic Dynamics, Contribution to the Encyclopedia of Nonlinear Science, Alwyn Scott (ed.), Routledge, 2004.

Contribution to the Encyclopedia of Nonlinear Science, Alwyn Scott (ed.), Routledge, 2005, pp.245-248.

Heterogeneous Agent Models in Economics and Finance, In: Handbook of Computational Economics II: Agent-Based Computational Economics, edited by Leigh Tesfatsion and Ken Judd , Elsevier, Amsterdam 2006, pp.1109-1186.

This chapter surveys work on dynamic heterogeneous agent models (HAMs) in economics and finance. Emphasis is given to simple models that, at least to some extent, are tractable by analytic methods in combination with computational tools. Most of these models are behavioral models with boundedly rational agents using different heuristics or rule of thumb strategies that may not be perfect, but perform reasonably well. Typically these models are highly nonlinear, e.g. due to evolutionary switching between strategies, and exhibit a wide range of dynamical behavior ranging from a unique stable steady state to complex, chaotic dynamics. Aggregation of simple interactions at the micro level may generate sophisticated structure at the macro level. Simple HAMs can explain important observed stylized facts in financial time series, such as excess volatility, high trading volume, temporary bubbles and trend following, sudden crashes and mean reversion, clustered volatility and fat tails in the returns distribution.

Market Mood, Adaptive Beliefs and Asset Price Dynamics

Empirical evidence has suggested that, facing different trading strategies and complicated decision, the proportions of agents relying on particular strategies may stay at constant level or vary over time. This paper presents a simple "dynamic market fraction" model of two groups of traders, fundamentalists and trend followers, under a market maker scenario. Market mood and evolutionary adaption are characterized by fixed and adaptive switching fraction among two groups, respectively. Using local stability and bifurcation analysis, as well as numerical simulation, the role played by the key parameters in the market behaviour is examined. Particular attention is payed to the impact of the market fraction, determined by the fixed proportions of confident fundamentalists and trend followers, and by the proportion of adaptively rational agents, who adopt different strategies over time depending on realized profits.

Selection mechanisms affect volatility in evolving markets

Financial asset markets are sociotechnical systems whose constituent agents are subject to evolutionary pressure as unprofitable agents exit the marketplace and more profitable agents continue to trade assets. Using a population of evolving zero-intelligence agents and a frequent batch auction price-discovery mechanism as substrate, we analyze the role played by evolutionary selection mechanisms in determining macro-observable market statistics. In particular, we show that selection mechanisms incorporating a local fitness-proportionate component are associated with high correlation between a micro, risk-aversion parameter and a commonly-used macro-volatility statistic, while a purely quantile-based selection mechanism shows significantly less correlation.Comment: 9 pages, 7 figures, to appear in proceedings of GECCO 2019 as a full pape

Modelling and trading the Greek stock market with gene expression and genetic programing algorithms

This paper presents an application of the gene expression programming (GEP) and integrated genetic programming (GP) algorithms to the modelling of ASE 20 Greek index. GEP and GP are robust evolutionary algorithms that evolve computer programs in the form of mathematical expressions, decision trees or logical expressions. The results indicate that GEP and GP produce significant trading performance when applied to ASE 20 and outperform the well-known existing methods. The trading performance of the derived models is further enhanced by applying a leverage filter

An Investigation Report on Auction Mechanism Design

Auctions are markets with strict regulations governing the information available to traders in the market and the possible actions they can take. Since well designed auctions achieve desirable economic outcomes, they have been widely used in solving real-world optimization problems, and in structuring stock or futures exchanges. Auctions also provide a very valuable testing-ground for economic theory, and they play an important role in computer-based control systems. Auction mechanism design aims to manipulate the rules of an auction in order to achieve specific goals. Economists traditionally use mathematical methods, mainly game theory, to analyze auctions and design new auction forms. However, due to the high complexity of auctions, the mathematical models are typically simplified to obtain results, and this makes it difficult to apply results derived from such models to market environments in the real world. As a result, researchers are turning to empirical approaches. This report aims to survey the theoretical and empirical approaches to designing auction mechanisms and trading strategies with more weights on empirical ones, and build the foundation for further research in the field

Efficient market hypothesis: is the Croatian stock market as (in)efficient as the U.S. market

Traditional statistical tests of serial independence of stock price changes often show that stock markets are inefficient. Our analysis on daily and monthly data confirms this finding for the Croatian and U.S. markets in the 2002-2010 period. However, this result seems to be mainly due to the impact of the crisis of 2008-2009. The observation of monthly data in the pre-crisis period suggests market efficiency in the U.S. and (rather surprisingly) in Croatia also. Daily data indicate a high degree of efficiency of the US stock market before the crisis, but it is impossible to conclude with a satisfying level of confidence that the Croatian market was inefficient in that period. Furthermore, an elementary moving average crossover trading system beats the CROBEX and S&P 500 indices from 1997 to 2010, indicating market inefficiency. Still, if the same trading rule is applied to the S&P 500 index in an extended time period between 1950 and 2010, the conclusion about market inefficiency becomes less convincing. It seems that (in)efficiency varies both across markets and in the same markets in the long run, but it still remains unknown which processes are the driving factors behind these changes.efficient market hypothesis, capital markets, CROBEX, S&P 500

Automated stock trading : a multi-agent, evolutionary approach

Includes bibliographical references (leaves 125-130).Stock market trading has garnered much interest over the past few decades as it has been made easier for the general public to trade. It is certainly an avenue for wealth growth, but like all risky undertakings, it must be understood for one to be consistently successful. There are, however, too many factors that influence it for one to make completely confident predictions. Automated computer trading has therefore been championed as a potential solution to this problem and is used in major brokerage houses world-wide. In fact, a third of all EU and US stock trades in 2006 were driven by computer algorithms. In this thesis we look at the challenges posed by the automatic generation of stock trading rules and portfolio management. We explore the viability of evolutionary algorithms, including genetic algorithms and genetic programming, for this problem and introduce an agent-based learning framework for individual and social intelligence that is applicable to general stock markets. Statistical tests were applied to determine whether or not there was a significant difference between the evolutionary trading approach and an accepted benchmark. It was found that while the evolutionary trading agents comfortably realised higher portfolio values than the ALSI, there was insufficient evidence to suggest that the agents outperformed the ALSI in terms of portfolio performance. Additionally, it was observed that while the traders combined knowledge from the expert traders to form complex trading models, these models did not result in any statistically significant positive returns. It must be said, however, that there was overwhelming evidence to suggest that the traders learned rules that were highly successful in predicting stock movement