Excessive Volatility Is Also a Feature of Individual Level Forecasts

Journal of Behavioral Finance doi: 10.1080/15427560.2014.877016

Posted: 29 Aug 2016 Last revised: 1 Mar 2017

See all articles by Anjali Nursimulu

Anjali Nursimulu

Ecole Polytechnique Fédérale de Lausanne

Peter Bossaerts

University of Cambridge

Date Written: June 30, 2011


The excessive volatility of prices in financial markets is one of the most pressing puzzles in social science. It has led many to question economic theory, which attributes beneficial effects to markets in the allocation of risks and the aggregation of information. In exploring its causes, we investigated to what extent excessive volatility can be observed at the individual level. Economists claim that securities prices are forecasts of future outcomes.

Here, we report on a simple experiment in which participants were rewarded to make the most accurate possible forecast of a canonical financial time series. We discovered excessive volatility in individual-level forecasts, paralleling the finding at the market level.Assuming that participants updated their beliefs based on reinforcement learning, we show that excess volatility emerged because of a combination of three factors. First, we found that submitted forecasts were noisy perturbations of participants’ revealed beliefs. Second, beliefs were updated using a prediction error based on submitted forecast rather than revealed past beliefs. Third, in updating beliefs, participants maladaptively decreased learning speed with prediction risk. Our results reveal formerly undocumented features in individual-level forecasting that may be critical to understand the inherent instability of financial markets and inform regulatory policy.

Keywords: Excess volatility, Financial prediction, Reinforcement Learning, Least-squares learning, Learning biases

Suggested Citation

Nursimulu, Anjali and Bossaerts, Peter L., Excessive Volatility Is Also a Feature of Individual Level Forecasts (June 30, 2011). Journal of Behavioral Finance doi: 10.1080/15427560.2014.877016, Available at SSRN: https://ssrn.com/abstract=2831329

Anjali Nursimulu (Contact Author)

Ecole Polytechnique Fédérale de Lausanne ( email )

Station 5
BAC 0.01
1015 Lausanne, CH-1015

Peter L. Bossaerts

University of Cambridge ( email )

Faculty of Economics
Cambridge, CB3 9DD
United Kingdom

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