Impact of Forecast Dispersion as a Measure of Information Asymmetry on Stock Volatility Post-Earnings Announcements: An Empirical Study Over Three Years on 75 S&P 500 Stocks
Translated title
Impact of Forecast Dispersion as a Measure of Information Asymmetry on Stock Volatility Post-Earnings Announcements
Author
Fennjan, Raymond
Term
4. term
Publication year
2024
Submitted on
2024-06-30
Pages
58
Abstract
Earnings announcements can move stock prices. This study examines how information asymmetry—differences in what analysts and investors know—relates to unusual stock volatility right around earnings announcements. Much prior work looks at longer-run price reactions, known as Post-Earnings Announcement Drift (PEAD), while immediate effects are less explored. This project therefore focuses on short-term volatility. It uses historical prices for 75 S&P 500 stocks over the last three years and about 900 earnings announcements. The study computes idiosyncratic abnormal volatility (unusual, stock-specific price swings beyond what the overall market would normally explain) around each announcement. Information asymmetry is measured via analyst forecast dispersion, since other common measures (such as bid-ask spreads, option-implied volatility metrics, or insider trading data) were not available. Methodologically, Ordinary Least Squares (OLS) regression is used to test the relationship between information asymmetry and abnormal volatility in a 20-day window around the announcement, with special attention to the immediate effect on day 0 and day 1. The aim is to improve understanding of how differences in information shape price reactions and to offer insights that could inform short-term trading strategies, while contributing to the PEAD literature.
Regnskabsmeddelelser kan få aktiekurser til at svinge. Dette studie undersøger, hvordan informationsasymmetri—forskelle i, hvilken information analytikere og investorer har—hænger sammen med usædvanlig aktievolatilitet lige omkring regnskabsmeddelelser. Meget tidligere forskning ser på langsigtede kursreaktioner, kendt som Post-Earnings Announcement Drift (PEAD), mens den umiddelbare effekt er mindre udforsket. Dette projekt fokuserer derfor på kortsigtede udsving. Studiet bruger historiske priser for 75 S&P 500-aktier over de sidste tre år og cirka 900 regnskabsmeddelelser. Det beregner idiosynkratisk abnorm volatilitet (usædvanlige, aktiespecifikke kursudsving ud over, hvad markedet som helhed normalt forklarer) omkring hver meddelelse. Informationsasymmetri måles via spredning i analytikeres indtjeningsprognoser, da andre gængse mål (som bid-ask-spread, optionsbaserede volatilitetsmål eller insiderdata) ikke var tilgængelige. Metodisk anvendes Ordinary Least Squares (OLS)-regression til at teste sammenhængen mellem informationsasymmetri og abnorm volatilitet i et 20-dages vindue omkring meddelelsen, med særligt fokus på den umiddelbare effekt på dag 0 og dag 1. Målet er at forbedre forståelsen af, hvordan forskelle i information påvirker kursreaktioner, og at give indsigter, der kan informere kortsigtede handelsstrategier, samtidig med at studiet bidrager til PEAD-litteraturen.
[This apstract has been rewritten with the help of AI based on the project's original abstract]