This paper considers the impact of heterogeneous gain learning in an asset pricing model. A relatively stylized model is shown to generate persistent swings of asset prices from their fundamental values which replicates long range samples of U.S financial data. The detailed mechanisms of the learning models are then explored. Evidence suggests that agents' perceptions of risk and its dynamics and persistence are important in generating appropriate price/fundamental dynamics. Agents putting large

This paper presents a new agent-based financial market. It is designed to be both simple enough to gain insights into the nature and structure of what is going on at both the agent and macro levels, but remain rich enough to allow for many interesting evolutionary experiments. The model is driven by heterogeneous agents who put varying weights on past information as they design portfolio strategies. It faithfully generates many of the common stylized features of asset markets. It also yields som

Evolutionary metaphors have been prominent in both economics and finance. They are often used as basic foundations for rational behavior and efficient markets. Theoretically, a mechanism which selects for rational investors requires many caveats, and is far from generic. This paper tests wealth-based evolution in a simple, stylized agent-based financial market. The setup borrows extensively from current research in finance that considers optimal behavior with some amount of return predictab

Evolutionary metaphors have been prominent in both economics and finance. They are often used as basic foundations for rational behavior and efficient markets. Theoretically, a mechanism which selects for rational investors requires many caveats, and is far from generic. This paper tests wealth-based evolution in a simple, stylized agent-based financial market. The setup borrows extensively from current research in finance that considers optimal behavior with some amount of return predictab