A New Predictive Measure Using Agent-Based Behavioral Finance

Todd Feldman () and Shuming Liu ()
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Todd Feldman: San Francisco State University
Shuming Liu: San Francisco State University

Computational Economics, 2018, vol. 51, issue 4, No 9, 959 pages

Abstract: Abstract We calibrate Friedman and Abraham’s (J Econ Dyn Control 33:922–937, 2009) agent-based model using actual financial data in the US stock market. The evidence shows that the estimated price series from the model is similar to real S&P price series and the model does match return moments at the second and higher order. In addition, we develop a new measure of investor heterogeneity based on the variability in the estimated position sizes across all mutual fund managers. Our results show that the volatility in individual fund manager positions is able to predict future returns in various time horizons. Moreover, increased variability in position sizes positively affects the contemporaneous change in the CBOE Volatility Index and also leads to greater probability of recession.

Keywords: Agent-based finance; Behavioral finance; Calibration (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s10614-017-9652-1

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