Forecasting Earnings from Home

Michael Thomas Durney; Hoyoun Kyung; Stanimir Markov; Jihwon Park

doi:10.2139/ssrn.4705771

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Forecasting Earnings from Home

Michael Thomas Durney, Hoyoun Kyung, Stanimir Markov and Jihwon Park

SSRN Electronic Journal

2024

DOI: 10.2139/ssrn.4705771

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Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

We exploit the COVID-19 pandemic-triggered lockdowns to examine the importance of in-person interactions as an information channel in capital markets. We find that equity analysts who interact more in-person with other informed parties – proxied by geographic proximity to (a) the covered firm, (b) other analysts covering the firm, (c) buy-side investors holding the firm, and (d) subject-matter experts – experience a greater decline in earnings forecast accuracy in the lockdown period. In fact, the pre-lockdown informational advantage from proximity to informed parties disappears during the lockdown period. In addition, the analysts who interact more in-person show a greater decline in the number of forecasted items during the lockdown period. We conclude that despite recent advances in communications technology, AI, and machine learning, in-person interactions are a unique and difficult-to-substitute information channel.

Details

Title: Subtitle: Forecasting Earnings from Home
Creators: Michael Thomas Durney - University of Iowa
Hoyoun Kyung - University of Missouri
Stanimir Markov - The University of Texas at Dallas
Jihwon Park - Baruch College
Resource Type: Preprint
Publication Details: SSRN Electronic Journal
DOI: 10.2139/ssrn.4705771
ISSN: 1556-5068
eISSN: 1556-5068
Language: English
Date posted: 2024
Academic Unit: Accounting
Record Identifier: 9984554826602771

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