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Excessive Volatility is Also a Feature of Individual Level Forecasts

Nursimulu, Anjali D. and Bossaerts, Peter (2014) Excessive Volatility is Also a Feature of Individual Level Forecasts. Journal of Behavioral Finance, 15 (1). pp. 16-29. ISSN 1542-7560. https://resolver.caltech.edu/CaltechAUTHORS:20141124-140559812

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Abstract

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.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1080/15427560.2014.877016DOIArticle
http://www.tandfonline.com/doi/abs/10.1080/15427560.2014.877016#previewPublisherArticle
ORCID:
AuthorORCID
Bossaerts, Peter0000-0003-2308-2603
Additional Information:© 2014 The Institute of Behavioral Finance.
Subject Keywords:Excess volatility, Financial prediction, Reinforcement learning, Least-squares learning, Learning biases
Issue or Number:1
Record Number:CaltechAUTHORS:20141124-140559812
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20141124-140559812
Official Citation:Excessive Volatility is Also a Feature of Individual Level Forecasts Anjali Nursimulu & Peter Bossaerts pages 16-29 DOI:10.1080/15427560.2014.877016 Published online: 06 Mar 2014
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:52116
Collection:CaltechAUTHORS
Deposited By: Ruth Sustaita
Deposited On:25 Nov 2014 20:13
Last Modified:03 Oct 2019 07:39

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