The bias of growth opportunity
The bias of growth opportunity
The bias of growth opportunity (BGO), measured as the difference between market and fundamental values of a firm's growth opportunity, has an ability to predict future stock returns. In the portfolio sort, downward-biased BGO firms earn higher returns than upward-biased BGO firms, which is unexplained by the common asset pricing models. Cross-sectional regression results also confirm BGO's power in predicting stock returns. To explain the anomaly, we show that the BGO premium is more pronounced when investor sentiment is high or when limits-to-arbitrage is severe, which suggests that the BGO is more likely to capture behavioral biases than systematic risk.
926-963
Gong, Cynthia M.
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Li, Xindan
8cd50516-ebcf-4eec-939f-ed10fa89b2fc
Luo, Di
cc1b0fa7-f630-45dc-ab05-495f9023148f
Zhao, Huainan
c1914fa7-b5f5-454a-9ef0-a44114ecfe8b
2 September 2022
Gong, Cynthia M.
53691994-4f04-447a-9499-c4738e183bf0
Li, Xindan
8cd50516-ebcf-4eec-939f-ed10fa89b2fc
Luo, Di
cc1b0fa7-f630-45dc-ab05-495f9023148f
Zhao, Huainan
c1914fa7-b5f5-454a-9ef0-a44114ecfe8b
Gong, Cynthia M., Li, Xindan, Luo, Di and Zhao, Huainan
(2022)
The bias of growth opportunity.
European Financial Management, 28 (4), .
(doi:10.1111/eufm.12323).
Abstract
The bias of growth opportunity (BGO), measured as the difference between market and fundamental values of a firm's growth opportunity, has an ability to predict future stock returns. In the portfolio sort, downward-biased BGO firms earn higher returns than upward-biased BGO firms, which is unexplained by the common asset pricing models. Cross-sectional regression results also confirm BGO's power in predicting stock returns. To explain the anomaly, we show that the BGO premium is more pronounced when investor sentiment is high or when limits-to-arbitrage is severe, which suggests that the BGO is more likely to capture behavioral biases than systematic risk.
Text
BGO
- Accepted Manuscript
More information
Accepted/In Press date: 12 May 2021
e-pub ahead of print date: 11 June 2021
Published date: 2 September 2022
Identifiers
Local EPrints ID: 449394
URI: http://eprints.soton.ac.uk/id/eprint/449394
ISSN: 1354-7798
PURE UUID: 6c9010b3-6f29-4316-bcad-a6a15a5a83de
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Date deposited: 27 May 2021 16:30
Last modified: 17 Mar 2024 06:34
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Contributors
Author:
Cynthia M. Gong
Author:
Xindan Li
Author:
Di Luo
Author:
Huainan Zhao
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