Estimating Trend and Value Interactions with an Extended Chiarella Model

Summary

This study uses a heterogeneous-agent framework to examine how trend-following and fundamental valuation can coexist in financial markets. It extends the Chiarella model with noise traders and a nonlinear demand response from fundamentalists. Bayesian filtering is used to calibrate the model to historical price series across several asset classes, while estimating fundamental value within the model instead of relying on an external valuation model.

The calibrated model is reported to reproduce empirical patterns, including a non-monotonic relationship between past trends and subsequent returns. The authors also find that trend-following can change the distribution of mispricing from one peak to two, consistent with markets remaining overvalued or undervalued for extended periods. The excerpt does not specify the assets, filtering details, uncertainty around estimates, or out-of-sample performance. Its results describe the behavior of a fitted model and do not establish that the mechanisms explain every market or period.

Key ideas

• The model combines trend-followers, fundamentalists, and noise traders within a heterogeneous-agent framework.
• Fundamental value is estimated as part of the calibration rather than supplied by an external pricing model.
• Bayesian filtering is applied to long-run price series spanning multiple asset classes.
• The model produces a non-monotonic relationship between past trends and future returns.
• Trend-following can produce a bimodal distribution of mispricing in the model.

Tags

# Co-existence of Trend and Value in Financial Markets: Estimating an Extended Chiarella Model


# Co-existence of Trend and Value in Financial Markets: Estimating an Extended Chiarella Model









Trend and Value are pervasive anomalies, common to all financial markets. We address the problem of their co-existence and interaction within the framework of Heterogeneous Agent Based Models (HABM). More specifically, we extend the Chiarella (1992) model by adding noise traders and a non-linear demand of fundamentalists. We use Bayesian filtering techniques to calibrate the model on time series of prices across a variety of asset classes since 1800. The fundamental value is an output of the calibration, and does not require the use of an external pricing model. Our extended model reproduces many empirical observations, including the non-monotonic relation between past trends and future returns. The destabilizing activity of trend-followers leads to a qualitative change of mispricing distribution, from unimodal to bimodal, meaning that some markets tend to be over- (or under-) valued for long periods of time.

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