Learning about Uncertainty from Options Trading

59 Pages Posted: 9 Apr 2020 Last revised: 13 Apr 2021

See all articles by Da-Hea Kim

Da-Hea Kim

Sungkyunkwan University

Sie Ting Lau

Nanyang Technological University (NTU) - Division of Banking & Finance

Bohui Zhang

The Chinese University of Hong Kong, Shenzhen

Date Written: March 19, 2020

Abstract

We hypothesize that managers can learn about a firm’s investment uncertainty from the equity options market. Using a US sample of 1,865 merger and acquisition attempts during 1996–2015, we show that the volatility implied from an acquiring firm’s equity options around an acquisition announcement negatively predicts the likelihood of acquisition attempts being completed. This negative impact is robust to controls for stock prices, alternative uncertainty proxies, and endogeneity tests. Moreover, we document three economic channels, finding that the effect of option implied volatility on deal completion is stronger among acquirers in which disinvestment is more difficult, whose managers are more susceptible to risk aversion, and whose options market is expected to have more information. Our findings suggest that options trading functions as a feedback mechanism to help managers learn about riskiness when making investment decisions.

Keywords: Learning, Feedback effect, Uncertainty, Equity options, Option implied volatility, Acquisition

JEL Classification: G14, G34

Suggested Citation

Kim, Da-Hea and Lau, Sie Ting and Zhang, Bohui, Learning about Uncertainty from Options Trading (March 19, 2020). Nanyang Business School Research Paper No. 20-30, Available at SSRN: https://ssrn.com/abstract=3556977 or http://dx.doi.org/10.2139/ssrn.3556977

Da-Hea Kim

Sungkyunkwan University ( email )

53 Myeongnyun-dong 3-ga Jongno-ju
Seoul, 110-745

Sie Ting Lau

Nanyang Technological University (NTU) - Division of Banking & Finance ( email )

S3-B1B-76 Nanyang Avenue
Singapore, 639798
Singapore
65 790 6051 (Phone)
65 791 3697 (Fax)

Bohui Zhang (Contact Author)

The Chinese University of Hong Kong, Shenzhen ( email )

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