Delayed Heterogeneous Trading Strategies and Financial Market Dynamics

Summary

This paper presents a continuous-time model of a market in which a market maker clears trades among fundamentalists, trend followers, and contrarians. Agents process market information with different delays and have path-dependent risk aversion. The model also allows investors to switch between trend-following and contrarian strategies through an evolutionary mechanism.

The authors report periodic, quasi-periodic, and chaotic system behavior, along with synchronization among technical traders. Simulated returns display features resembling those observed in the S&P 500, including excess kurtosis, volatility clustering, and long memory. These are model-generated properties rather than evidence of a deployable trading strategy or out-of-sample forecasting performance; the document gives no calibration details or empirical trading results.

Key ideas

• The market model includes fundamentalists, trend followers, and contrarians.
• Investors act on information with different time delays and path-dependent risk aversion.
• An evolutionary process permits switching between trend-following and contrarian strategies.
• The model produces periodic, quasi-periodic, and chaotic dynamics.
• Simulated returns reproduce several statistical features associated with the S&P 500.

Tags

# Asset Price Dynamics in a Financial Market with Heterogeneous Trading Strategies and Time Delays


# Asset Price Dynamics in a Financial Market with Heterogeneous Trading Strategies and Time Delays









In this paper we present a continuous time dynamical model of heterogeneous agents interacting in a financial market where transactions are cleared by a market maker. The market is composed of fundamentalist, trend following and contrarian agents who process information from the market with different time delays. Each class of investor is characterized by path dependent risk aversion. We also allow for the possibility of evolutionary switching between trend following and contrarian strategies. We find that the system shows periodic, quasi-periodic and chaotic dynamics as well as synchronization between technical traders. Furthermore, the model is able to generate time series of returns that exhibit statistical properties similar to those of the S&P500 index, which is characterized by excess kurtosis, volatility clustering and long memory

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