The Idiosyncratic Volatility Puzzle with Learning and Asymmetric Signal Precision

66 Pages Posted: 7 May 2020 Last revised: 4 Jan 2021

See all articles by Xuhui (Nick) Pan

Xuhui (Nick) Pan

University of Oklahoma

Bharat Raj Parajuli

Monash University

Petra Sinagl

University of Iowa - Department of Finance

Date Written: October 7, 2020

Abstract

We document that the idiosyncratic volatility (IVOL) puzzle exists only among firms that underperform their benchmark or release negative earnings surprises. We explain these findings using a Bayesian updating model with asymmetric signal precision in which agents observe noisy signals about future cash flows. In this setting, negative news are associated with relatively lower signal precision, negative momentum, and low subsequent returns. After controlling for relative performance (our proxy of news) and signal precision, the IVOL puzzle disappears. This performance- and signal-precision-based explanation alone can account for up to 83% of the IVOL puzzle, which is more than other existing theories combined.

Keywords: Idiosyncratic volatility puzzle, Bayesian updating, asymmetric signal precision, firm underperformance

JEL Classification: G12, G14

Suggested Citation

Pan, Xuhui (Nick) and Parajuli, Bharat Raj and Sinagl, Petra, The Idiosyncratic Volatility Puzzle with Learning and Asymmetric Signal Precision (October 7, 2020). Available at SSRN: https://ssrn.com/abstract=3574790 or http://dx.doi.org/10.2139/ssrn.3574790

Xuhui (Nick) Pan

University of Oklahoma ( email )

307 W Brooks
Norman, OK 73019
United States

Bharat Raj Parajuli

Monash University ( email )

900 Dandenong Road
Caulfield East, VIC, 3145
Australia

Petra Sinagl (Contact Author)

University of Iowa - Department of Finance ( email )

Iowa City, IA 52242-1000
United States

HOME PAGE: http://andrlikova.com

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