Using Option Implied Volatilities to Predict Absolute Stock Returns - Evidence from Earnings Announcements and Annual Shareholders’ Meetings

36 Pages Posted: 27 Sep 2014

See all articles by Suresh Govindaraj

Suresh Govindaraj

Rutgers University - Rutgers Business School - Newark and New Brunswick

Wen Jin

Quantitative Management Associates (QMA) LLC

Joshua Livnat

New York University; Prudential Financial - Quantitative Management Associates

Chen Zhao

Rutgers Business School - Newark and New Brunswick

Date Written: September 25, 2014

Abstract

We provide evidence that an option implied volatility-based measure predicts future absolute excess returns of the underlying stock around earnings announcements and annual meetings of shareholders, even after controlling for the realized stock return volatility shortly before these information events, and the volatility of excess stock returns around these two events in the past. Our results imply that option traders anticipate the change in uncertainty around these two scheduled events, and also trade on the expected volatility. In addition, we show that net straddle returns (after transaction costs) around earnings announcements and annual meetings of shareholders are significantly and negatively related to the predicted volatility of returns around the events. This suggests that the writers of call and put options expect to be compensated for the predicted volatility. Overall, we find that option traders anticipate and correctly incorporate the volatility induced by the information released in quarterly earnings announcements, and annual meetings of shareholders.

Keywords: Earnings Announcement, Annual Meeting of Shareholders, Option Implied Volatility, Absolute Stock Return

JEL Classification: M41, G11, G13, G14, G17

Suggested Citation

Govindaraj, Suresh and Jin, Wen and Livnat, Joshua and Zhao, Chen, Using Option Implied Volatilities to Predict Absolute Stock Returns - Evidence from Earnings Announcements and Annual Shareholders’ Meetings (September 25, 2014). Available at SSRN: https://ssrn.com/abstract=2501750 or http://dx.doi.org/10.2139/ssrn.2501750

Suresh Govindaraj (Contact Author)

Rutgers University - Rutgers Business School - Newark and New Brunswick ( email )

1 Washington Park
Room #934
Newark, NJ 07102
United States

Wen Jin

Quantitative Management Associates (QMA) LLC ( email )

100 Mulberry Street
Gateway Center 2
Newark, NJ 07102
United States

Joshua Livnat

New York University ( email )

44 West 4th Street, Suite 10-76
Stern School of Business
New York, NY 10012-1118
United States
212-998-0022 (Phone)
212-995-4004 (Fax)

Prudential Financial - Quantitative Management Associates ( email )

2 Gateway Center
6th Fl.
Newark, NJ 07102
United States

Chen Zhao

Rutgers Business School - Newark and New Brunswick ( email )

94 Rockafeller Road
Piscataway, NJ 08854
United States

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