Agent-based modeling to investigate the disposition effect in financial markets

One of the behavioral patterns that deviate from what is predicted by traditional financial theories is the disposition effect. Although most empirical studies have reported a significant disposition effect, researchers have yet to conduct a conclusive test of thiseffect because a competing hypothesis or confounding effects might explain the documented significance. Thus, we use the tools of computational intelligence, instead of empirical approaches, to explore market behavior. In particular, we allow agents with different investment strategies to interact and to compete with each other in an artificial futures market. We found that the S-shaped value curve proposed by prospect theory may be one of the causes of the observed behavior of the disposition effect. However, rational expectation such as short-term mean reversion can even be more decisive

Higher-Order Simulations: Strategic Investment Under Model-Induced Price Patterns

The trading and investment decision processes in financial markets become ever more dependent on the use of valuation and risk models. In the case of risk management for instance, modelling practice has become quite homogeneous and the question arises as to the effect this has on the price formation process. Furthermore, sophisticated investors who have private information about the use and characteristics of these models might be able to make superior gains in such an environment. The aim of this article is to test this hypothesis in a stylised market, where a strategic investor trades on information about the valuation and risk management models used by other market participants. Simulation results show that under certain market conditions, such a \'higher-order\' strategy generates higher profits than standard fundamental and momentum strategies that do not draw on information about model use.Financial Markets, Multi-Agent Simulation, Performativity, Higher-Order Strategies

Simulación realista de los mercados financieros con sistemas multi-agentes

Els mercats financers són un exemple paradigmàtic de sistemes complexos adaptatius. Els paradigmes tradicionals de modelització no permeten reflexar aquesta realitat i hem de recórrer a noves eines. Presentem la simulació basada en sistemes multi-agents com el paradigma idoni per a analitzar els mercats financers en tota la seva complexitat. És un mètode que permet estudiar el comportament global del mercat a partir de la seva microestructura, i ja ha suggerit explicacions per a algunes de les regularitats estadístiques observades en una gran varietat de mercats. Exposem, a més amés, els primers passos en la construcció d'una simulació realista del mercat de bons.Financial markets are a paradigmatic case of complex adaptive systems. However, their complexity cannot be captured by traditional modelisation paradigms and we thus need to turn to new modelling tools. We present agent-based simulation as the suitable paradigm to analyse financial markets: this method allows to study the market macro behaviour on the basis of its microstructure, and some advances have already been done in the analysis and explanation of the stylised facts observed in a range of markets. We moreover describe the first steps we have undertaken to build a realistic simulation of a bond market

Social Simulation of Stock Markets: Taking It to the Next Level

This paper studies the use of social simulation in linking micro level investor behaviour and macro level stock market dynamics. Empirical data from a survey on individual investors\' decision-making and social interaction was used to formalize the trading and interaction rules of the agents of the artificial stock market SimStockExchange. Multiple simulation runs were performed with this artificial stock market, which generated macro level results, like stock market prices and returns over time. These outcomes were subsequently compared to empirical macro level data from real stock markets. Partial qualitative as well as quantitative agreement between the simulated asset returns distributions and the asset returns distributions of the real stock markets was found.Agent-Based Computational Finance, Artificial Stock Markets, Behavioral Finance, Micro-Macro Links, Multi-Agent Simulation, Stock Market Characteristics

An Analysis of the Influence of Fundamental Values' Estimation Accuracy on Financial Markets

This research analyzed the influence of the differences in the forecast accuracy of fundamental values on the financial market. As a result of intensive experiments in the market, we made the following interesting findings: (1) improvements in forecast accuracy of fundamentalists can contribute to an increase in the number of fundamentalists; (2) certain situations might occur, according to the level of forecast accuracy of fundamentalists, in which fundamentalists and passive management coexist, or in which fundamentalists die out of the market, and furthermore; (3) where a variety of investors exist in the market, improvements in the forecast accuracy could increase the number of fundamentalists more than the number of investors that employ passive investment strategy. These results contribute to clarifying the mechanism of price fluctuations in financial markets and also indicate one of the factors for the low ratio of passive investors in asset management business