Information acquisition and expected returns: Evidence from EDGAR search traffic
This paper examines expected return information embedded in investors' information acquisition activity. Using a novel dataset containing investors' access of company filings through SEC's EDGAR system, we reverse engineer their expectations over future payoffs and show that the abnor...
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sg-smu-ink.lkcsb_research-63212022-08-29T13:17:19Z Information acquisition and expected returns: Evidence from EDGAR search traffic LI, Frank Weikai SUN, Chengzhu This paper examines expected return information embedded in investors' information acquisition activity. Using a novel dataset containing investors' access of company filings through SEC's EDGAR system, we reverse engineer their expectations over future payoffs and show that the abnormal number of IPs searching for firms' financial statements strongly predict future returns. The return predictability stems from investors allocating more effort to firms with improving fundamentals and following exogeneous shock to underpricing. A long-short portfolio based on our measure of information acquisition activity generate monthly abnormal return of 80 basis points and does not reverse over the long-run.. In addition, the return predictability is stronger among firms with larger and lengthy financial filings that are more costly to process. Collectively, these findings support theoretical predictions that costly information acquisition reveals the value of information. 2022-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/lkcsb_research/5322 info:doi/10.1016/j.jedc.2022.104384 https://ink.library.smu.edu.sg/context/lkcsb_research/article/6321/viewcontent/SSRN_id3031977__1_.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection Lee Kong Chian School Of Business eng Institutional Knowledge at Singapore Management University Information Acquisition EDGAR Search Return Predictability Market Efficiency Finance and Financial Management Portfolio and Security Analysis |
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Information Acquisition EDGAR Search Return Predictability Market Efficiency Finance and Financial Management Portfolio and Security Analysis LI, Frank Weikai SUN, Chengzhu Information acquisition and expected returns: Evidence from EDGAR search traffic |
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This paper examines expected return information embedded in investors' information acquisition activity. Using a novel dataset containing investors' access of company filings through SEC's EDGAR system, we reverse engineer their expectations over future payoffs and show that the abnormal number of IPs searching for firms' financial statements strongly predict future returns. The return predictability stems from investors allocating more effort to firms with improving fundamentals and following exogeneous shock to underpricing. A long-short portfolio based on our measure of information acquisition activity generate monthly abnormal return of 80 basis points and does not reverse over the long-run.. In addition, the return predictability is stronger among firms with larger and lengthy financial filings that are more costly to process. Collectively, these findings support theoretical predictions that costly information acquisition reveals the value of information. |
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LI, Frank Weikai SUN, Chengzhu |
author_facet |
LI, Frank Weikai SUN, Chengzhu |
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LI, Frank Weikai |
title |
Information acquisition and expected returns: Evidence from EDGAR search traffic |
title_short |
Information acquisition and expected returns: Evidence from EDGAR search traffic |
title_full |
Information acquisition and expected returns: Evidence from EDGAR search traffic |
title_fullStr |
Information acquisition and expected returns: Evidence from EDGAR search traffic |
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Information acquisition and expected returns: Evidence from EDGAR search traffic |
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information acquisition and expected returns: evidence from edgar search traffic |
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Institutional Knowledge at Singapore Management University |
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2022 |
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https://ink.library.smu.edu.sg/lkcsb_research/5322 https://ink.library.smu.edu.sg/context/lkcsb_research/article/6321/viewcontent/SSRN_id3031977__1_.pdf |
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