Sell-side Analyst Recommendation Revisions and Institutional Trading before and after Regulation FD

2020 ◽  
Author(s):  
Mustafa Onur Caglayan ◽  
Umut Celiker ◽  
Edward R. Lawrence
Keyword(s):  
Institutional Trading ◽  
Before And After ◽  
Regulation Fd ◽  
Analyst Recommendation ◽  
Recommendation Revisions

scholarly journals The correlation between stock returns before and after analyst recommendation revisions

2021 ◽  
Vol 66 (228) ◽  
pp. 69-100
Author(s):  
Andrey Kudryavtsev
Keyword(s):  
Stock Returns ◽  
Stock Price ◽  
Trading Volume ◽  
Abnormal Returns ◽  
Market Model ◽  
Short Period ◽  
Before And After ◽  
Analyst Recommendation ◽  
Experience Recommendation ◽  
Recommendation Revisions

In this study I analyse the correlation between stock returns before and after analyst recommendation revisions. I hypothesise that if a recommendation revision for a given stock takes place after a short period when the stock?s price moves in the opposite direction, it may indicate that the fundamentals that caused the analyst to revise their recommendation are less completely (if at all) incorporated in the stock price, significantly increasing the probability of subsequent post-event price drift. Analysing a large sample of recommendation revisions, I document that both recommendation upgrades and downgrades are followed by significant one-tosix-month price drifts (reversals) if they are preceded by the opposite-sign (same-sign) short-term cumulative abnormal returns. The effect remains significant after accounting for additional relevant company specific (size, Market Model beta, historical volatility) and event-specific (stock?s return and trading volume on the event day, brokerage firm size, analyst experience, recommendation category before the revision, number of categories changed in the revision) factors.

Institutional Investors and the Information Production Theory of Stock Splits

2015 ◽  
Vol 50 (3) ◽  
pp. 413-445 ◽  
Author(s):  
Thomas J. Chemmanur ◽  
Gang Hu ◽  
Jiekun Huang
Keyword(s):  
Private Information ◽  
Institutional Investors ◽  
Stock Splits ◽  
Production Theory ◽  
Information Production ◽  
Extended Version ◽  
Institutional Trading ◽  
Before And After ◽  
Brokerage Commissions ◽  
Abnormal Profits

AbstractWe make use of a large sample of transaction-level institutional trading data to test an extended version of Brennan and Hughes’ (1991) information production theory of stock splits. We compare brokerage commissions paid by institutional investors before and after a split, assess the private information held by them, and relate the informativeness of their trading to brokerage commissions paid. We show that institutions make abnormal profits net of brokerage commissions by trading in splitting stocks. We also show that the information asymmetry faced by firms goes down after stock splits. Overall, our empirical results support the information production theory.

scholarly journals Is herding spurious or intentional? Evidence from analyst recommendation revisions and sentiment

2020 ◽  
Vol 71 ◽  
pp. 101539
Author(s):  
Jiaqi Guo ◽  
Phil Holmes ◽  
Ali Altanlar
Keyword(s):  
Analyst Recommendation ◽  
Recommendation Revisions

scholarly journals The time‐varying performance of UK analyst recommendation revisions: Do market conditions matter?

2020 ◽  
Vol 29 (2) ◽  
pp. 65-89
Author(s):  
Chen Su ◽  
Hanxiong Zhang ◽  
Robert S. Hudson
Keyword(s):  
Time Varying ◽  
Market Conditions ◽  
Analyst Recommendation ◽  
Recommendation Revisions

Investor attention, analyst recommendation revisions, and stock prices

2017 ◽  
Vol 45 ◽  
pp. 211-223 ◽  
Author(s):  
Venura Welagedara ◽  
Saikat Sovan Deb ◽  
Harminder Singh
Keyword(s):  
Stock Prices ◽  
Investor Attention ◽  
Analyst Recommendation ◽  
Recommendation Revisions

The information content of analyst recommendation revisions — Evidence from the Chinese stock market

2014 ◽  
Vol 29 ◽  
pp. 1-17 ◽  
Author(s):  
George J. Jiang ◽  
Liangliang Lu ◽  
Dongming Zhu
Keyword(s):  
Stock Market ◽  
Information Content ◽  
Chinese Stock Market ◽  
Analyst Recommendation ◽  
Recommendation Revisions
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