This paper surveys research on computational agent-based models used in finance. It will concentrate on models where the use of computational tools is critical in the process of crafting models which give insights into the importance and dynamics of investor heterogeneity in many financial settings.

One of the challenges of financial research is to develop models that are capable of explaining and forecasting market price movements and returns.Agent based models focus directly on the underlying structure of the market. The basic idea is, that the market price dynamics arises from the interaction of many individual agents. Approaching financial markets in this manner, one starts off with the modeling of the agents´ decision making schemes on the microeconomic level of the market. Thereafter, market price changes can be determined on the macroeconomic level by a superposition of the agents´ buying and selling decisions. The aim of a (micro-)economic model is to explain market prices by a detailed causal analysis of the agents´ decision making behavior. The market price results from an aggregation of the agents´ decisions. Remarkably, agent-based financial markets provide a new explanatory framework supplementing the traditional economic concepts of equilibrium theory and efficient markets. Such a supplementing framework is needed, because in real-world financial markets the underlying assumptions of equilibrium or efficient market theory are often violated.As we will show, neural networks allow the integration of the decision behavior of individual economic agents into a market model. Based on the perspective of interacting agents, the resulting market model allows us to capture the underlying dynamics of financial markets, to fit real-world financial data, and to forecast future market price movements.In addition, we point out that neural networks allow to set up a joint framework of econometric model building. Besides the learning from data, one may integrate prior knowledge about the underlying dynamical system and first principles into the modeling. These elements are incorporated into the neural networks in form of architectural enhancements. This way of model building helps to overcome the drawbacks of purely data driven approaches.

Daily returns for large and mid-cap stocks listed on the New York Exchange are not serially dependent. In contrast, order imbalances on the same stocks are highly persistent from day to day. These two empirical facts can be reconciled if sophisticated investors react to order imbalances within the trading day by engaging in countervailing trades sufficient to remove serial dependence over the daily horizon. How long does this actually take? The pattern of intra-day serial dependence, over intervals ranging from five minutes to one hour, reveals traces of efficiency-creating actions. For the stocks in our sample, it takes longer than five minutes for astute investors to begin such activities. By thirty minutes, they are well along on their daily quest.

Web-based financial commentary, in which experts routinely express market-related thought processes, is proposed as a means for college students to learn vicariously about financial markets. Undergraduate business school students from a regional university were exposed to expert market commentary from a single financial Web site for a 6week period. When compared to a control group, students in the experimental group were found to possess higher levels of financial market awareness. Degree of engagement, as approximated by measures of project exposure time and effort, was significantly related to market awareness. Finance majors were found to be more engaged in the process than nonfinance majors. Although this study should be considered exploratory in nature, findings support the notion of using Web-based vicarious learning processes in financial education. Future research can extend the generalizability of these findings, as well as shape vicarious learning mechanisms for use across business disciplines.