Analyst Bias, Firm Characteristics, and Stock Returns in the Australian Stock Market

2010 ◽  
Author(s):  
Shibely Sadique ◽  
Francis Haeuck In ◽  
Madhu Veeraraghavan
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Firm Characteristics ◽  
Analyst Bias

Stock market crashes, firm characteristics, and stock returns

2009 ◽  
Vol 33 (9) ◽  
pp. 1563-1574 ◽  
Author(s):  
Jia Wang ◽  
Gulser Meric ◽  
Zugang Liu ◽  
Ilhan Meric
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Firm Characteristics ◽  
Market Crashes

When the Market Went Viral: COVID-19, Stock Returns, and Firm Characteristics

2020 ◽  
Author(s):  
Avijit Bansal ◽  
Balagopal Gopalakrishnan ◽  
Joshy Jacob ◽  
Pranjal Srivastava
Keyword(s):  
Stock Returns ◽  
Firm Characteristics

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.

Financial Constraints, Stock Returns and R&D in Indian Stock Market

2021 ◽  
pp. 097226292098839
Author(s):  
Pankaj Sinha ◽  
Priya Sawaliya
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Financial Constraints ◽  
Fixed Effect Model ◽  
Quantitative Information ◽  
Common Shocks ◽  
External Finance ◽  
Negative Effect ◽  
Security Returns ◽  
Indian Stock Market

When the accessibility of external finance prohibits a firm from taking the optimum decision related to investment, that firm is called financially constrained. By applying the methodology of Kaplan and Zingales (1997) and Lamont et al. (2001), the current study has created a construct to gauge the level of financial constraints (FC) of the companies which emanate from quantitative information. The study explores whether FC factor is present in the Indian stock market and explores whether the security returns of those firms that are financially constrained move in tandem. The study also attempts to establish the association between security returns and R&D of financially constrained firms. On a sample of 63 R&D reporting companies of S&P BSE 500, traded over the period March 2008 to February 2019, the study used the Fama–French methodology, fixed effect model and the ordered logistic regression. The study finds that firms that are highly constrained earn more returns than low constrained firms. Second, the security returns of firms that are financially constrained move in tandem because these firms are affected by common shocks. This suggests that the FC factor exists in the Indian stock market. Finally, when R&D interacts with the level of FC, then this interaction effect has a negative effect on returns.

scholarly journals Information-Theoretic Measures and Modeling Stock Market Volatility: A Comparative Approach

Risks ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 89
Author(s):  
Muhammad Sheraz ◽  
Imran Nasir
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Exchange ◽  
Approximate Entropy ◽  
Market Volatility ◽  
Comparative Approach ◽  
Stock Market Volatility ◽  
Information Theoretic ◽  
Information Theoretic Measures ◽  
Garch Modeling

The volatility analysis of stock returns data is paramount in financial studies. We investigate the dynamics of volatility and randomness of the Pakistan Stock Exchange (PSX-100) and obtain insights into the behavior of investors during and before the coronavirus disease (COVID-19 pandemic). The paper aims to present the volatility estimations and quantification of the randomness of PSX-100. The methodology includes two approaches: (i) the implementation of EGARCH, GJR-GARCH, and TGARCH models to estimate the volatilities; and (ii) analysis of randomness in volatilities series, return series, and PSX-100 closing prices for pre-pandemic and pandemic period by using Shannon’s, Tsallis, approximate and sample entropies. Volatility modeling suggests the existence of the leverage effect in both the underlying periods of study. The results obtained using GARCH modeling reveal that the stock market volatility has increased during the pandemic period. However, information-theoretic results based on Shannon and Tsallis entropies do not suggest notable variation in the estimated volatilities series and closing prices. We have examined regularity and randomness based on the approximate entropy and sample entropy. We have noticed both entropies are extremely sensitive to choices of the parameters.

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