Intraday and interday distribution of stock returns and their asymmetric conditional volatility: Firm-level evidence

2018 ◽  
Vol 503 ◽  
pp. 905-915 ◽  
Author(s):  
Ercan Balaban ◽  
Tolga Ozgen ◽  
Socrates Karidis
Keyword(s):  
Stock Returns ◽  
Conditional Volatility ◽  
Firm Level

Can Twitter Help Predict Firm-Level Earnings and Stock Returns?

2017 ◽  
Vol 93 (3) ◽  
pp. 25-57 ◽  
Author(s):  
Eli Bartov ◽  
Lucile Faurel ◽  
Partha S. Mohanram
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Future Prospects ◽  
Traditional Media ◽  
Stock Trading ◽  
Firm Level ◽  
Quarterly Earnings ◽  
Announcement Returns ◽  
Twitter Activity ◽  
Original Information

ABSTRACT Prior research has examined how companies exploit Twitter in communicating with investors, and whether Twitter activity predicts the stock market as a whole. We test whether opinions of individuals tweeted just prior to a firm's earnings announcement predict its earnings and announcement returns. Using a broad sample from 2009 to 2012, we find that the aggregate opinion from individual tweets successfully predicts a firm's forthcoming quarterly earnings and announcement returns. These results hold for tweets that convey original information, as well as tweets that disseminate existing information, and are stronger for tweets providing information directly related to firm fundamentals and stock trading. Importantly, our results hold even after controlling for concurrent information or opinion from traditional media sources, and are stronger for firms in weaker information environments. Our findings highlight the importance of considering the aggregate opinion from individual tweets when assessing a stock's future prospects and value.

Information risk, stock returns, and asset pricing: Evidence from China

2017 ◽  
Vol 30 (4) ◽  
pp. 379-394 ◽  
Author(s):  
Raheel Safdar ◽  
Chen Yan
Keyword(s):  
Stock Returns ◽  
Factor Model ◽  
Empirical Literature ◽  
Firm Level ◽  
Cross Sectional ◽  
Market Pricing ◽  
Content Type ◽  
Information Risk ◽  
Accruals Quality ◽  
Three Factor

Purpose This study aims to investigate information risk in relation to stock returns of a firm and whether information risk is priced in China. Design/methodology/approach The authors used accruals quality (AQ) as their measure of information risk and performed Fama-Macbeth regressions to investigate association of AQ with future realized stock returns. Moreover, two-stage cross-sectional regression analysis was performed, both at firm level and at portfolio level, to test if the AQ factor is priced in China in addition to existing factors in the Fama French three-factor model. Findings The authors found poor AQ being associated with higher future realized stock returns. Moreover, they found evidence of market pricing of AQ in addition to existing factors in the Fama French three-factor model. Further, subsample analysis revealed that investors value AQ more in non-state owned enterprises than in state owned enterprises. Research limitations/implications The study sample comprises A-shares only and the generalization of the findings is limited by the peculiar institutional and economic setup in China. Originality/value This study contributes to market-based accounting literature by providing further insight into how and if investors value information risk, and it seeks to fill gap in empirical literature by providing evidence from the Chinese capital market.

IMMEDIATE AND LONGER-TERM STOCK PRICE DYNAMICS FOLLOWING LARGE STOCK PRICE CHANGES

2020 ◽  
Vol 15 (01) ◽  
pp. 2050002
Author(s):  
ANDREY KUDRYAVTSEV
Keyword(s):  
Stock Returns ◽  
Stock Price ◽  
Trading Volume ◽  
Abnormal Returns ◽  
Market Model ◽  
Conditional Volatility ◽  
Short Term ◽  
Price Increases ◽  
Cumulative Abnormal Returns ◽  
Price Reversal

The study explores the correlation between the immediate and the longer-term stock returns following large daily price moves. Following the previous literature, which documents a tendency for price reversals after initial large price moves, I suggest that if a large stock price move is immediately followed by a short-term price drift, then it may indicate that the company-specific shock is more completely incorporated in the stock price, significantly increasing the probability of subsequent longer-term price reversal. Analyzing a vast sample of large stock price moves, I document that negative (positive) longer-term stock price reversals after large price increases (decreases) are significantly more pronounced if the latter are immediately followed by relatively high (low) short-term cumulative abnormal returns, that is, by short-term price drifts. The effect remains significant after accounting for additional company-specific (size, market model beta, historical, or conditional volatility) and event-specific (stock’s return and trading volume on the event day) factors.

scholarly journals The Effects of Oil and Gas Risk Factors on Malaysian Oil and Gas Stock Returns: Do They Vary?

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3901 ◽  
Author(s):  
Mohammad Enamul Hoque ◽  
Soo-Wah Low ◽  
Mohd Azlan Shah Zaidi
Keyword(s):  
Risk Factors ◽  
Stock Returns ◽  
Oil And Gas ◽  
Market Value ◽  
Negative Effects ◽  
Firm Level ◽  
Stock Size ◽  
Positive Effects ◽  
Oil And Gas Firms ◽  
Gas Price

This study explores Malaysian oil and gas stocks’ exposure to oil and gas risk factors, paying special attention to subindustry classification, stock size, book-to-market value, and volatility state. The study employs firm-level weekly frequency data of oil and gas firms and several multi-asset pricing models within a GARCH (1,1)-X and Markov-switching framework. The empirical findings reveal that oil price, gas price, and exchange rate exhibit positive effects on the stock returns of all oil and gas sub-industries, but they exhibit negative effects on gas utilities sub-industry stock returns. The empirical findings also reveal that the extent of this effect varies across sub-industry, stock size, book-to-market value, and volatility states. Thus, the findings suggest the existence of asymmetric, heterogeneous, and non-linear exposures.

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