Impacts of Corporate Announcements on Stock Returns during the Global Pandemic: Evidence from the Indian Stock Market

2021 ◽  
Author(s):  
dharen kumar PANDEY ◽  
Vineeta Kumari
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Global Pandemic ◽  
Corporate Announcements ◽  
Indian Stock Market

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.

Does time-varying illiquidity matter for the Indian stock market? Evidence from high-frequency data

2021 ◽  
pp. 031289622110102
Author(s):  
Mousumi Bhattacharya ◽  
Sharad Nath Bhattacharya ◽  
Sumit Kumar Jha
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
High Frequency ◽  
Jel Classification ◽  
Exogenous Shock ◽  
Intraday Data ◽  
Day Of The Week ◽  
Holiday Effects ◽  
Indian Stock Market ◽  
The Impact

This article examines variations in illiquidity in the Indian stock market, using intraday data. Panel regression reveals prevalent day-of-the-week, month, and holiday effects in illiquidity across industries, especially during exogenous shock periods. Illiquidity fluctuations are higher during the second and third quarters. The ranking of most illiquid stocks varies, depending on whether illiquidity is measured using an adjusted or unadjusted Amihud measure. Using pooled quantile regression, we note that illiquidity plays an important asymmetric role in explaining stock returns under up- and down-market conditions in the presence of open interest and volatility. The impact of illiquidity is more severe during periods of extreme high and low returns. JEL Classification: G10, G12

Do Indian stock market sentiments impact contemporaneous returns?

2018 ◽  
Vol 7 (3) ◽  
pp. 332-346
Author(s):  
Divya Aggarwal ◽  
Pitabas Mohanty
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Temporal Dynamics ◽  
Stock Exchange ◽  
Principal Component ◽  
Stock Index ◽  
Content Type ◽  
Investor Sentiments ◽  
Indian Stock Market ◽  
The Impact

Purpose The purpose of this paper is to analyse the impact of Indian investor sentiments on contemporaneous stock returns of Bombay Stock Exchange, National Stock Exchange and various sectoral indices in India by developing a sentiment index. Design/methodology/approach The study uses principal component analysis to develop a sentiment index as a proxy for Indian stock market sentiments over a time frame from April 1996 to January 2017. It uses an exploratory approach to identify relevant proxies in building a sentiment index using indirect market measures and macro variables of Indian and US markets. Findings The study finds that there is a significant positive correlation between the sentiment index and stock index returns. Sectors which are more dependent on institutional fund flows show a significant impact of the change in sentiments on their respective sectoral indices. Research limitations/implications The study has used data at a monthly frequency. Analysing higher frequency data can explain short-term temporal dynamics between sentiments and returns better. Further studies can be done to explore whether sentiments can be used to predict stock returns. Practical implications The results imply that one can develop profitable trading strategies by investing in sectors like metals and capital goods, which are more susceptible to generate positive returns when the sentiment index is high. Originality/value The study supplements the existing literature on the impact of investor sentiments on contemporaneous stock returns in the context of a developing market. It identifies relevant proxies of investor sentiments for the Indian stock market.

scholarly journals Derivative Trading and Spot Market Volatility: Evidence from Indian Market

2015 ◽  
Vol 1 (3) ◽  
pp. 23-34 ◽  
Author(s):  
Dhanya Alex ◽  
Roshna Varghese
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Unit Root Test ◽  
Spot Market ◽  
Descriptive Statistics ◽  
Garch Models ◽  
Indian Market ◽  
Stock Volatility ◽  
Asymmetric Behaviour ◽  
Indian Stock Market

The present study tries to estimate the effect of introduction of individual stock derivatives on the underlying stock volatility in Indian stock market. To estimate the effect of introduction of derivatives on stock market, GARCH family models which are known for their ability to model volatility. The return series of the ten companies were tested using methods like, unit root test and descriptive statistics to confirm that GARCH models could be used. Using these models, the asymmetric nature of stock returns and the volatility of stock returns on the introduction of derivatives are checked. The results reveal that the introduction of derivatives has decreased the volatility of the underlying stock returns. It was also found that most of the stock returns show asymmetric behaviour.

Cointegration of Asian Stock Markets: Empirical Evidence from India

2016 ◽  
Vol 6 (2) ◽  
Author(s):  
Beeralaguddada Srinivasa Veerappa
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Markets ◽  
Causality Test ◽  
Long Run ◽  
Equilibrium Relationship ◽  
Short Run ◽  
Auto Regressive ◽  
Global Stock Markets ◽  
Indian Stock Market

At present stock return is significantly related to other global stock markets. The present paper empirically investigates the short run and long run equilibrium relationship between the stock market of India, Japan Hong Kong, Singapore, Malaysia, China, and Australia monthly data during January 1995 to December 2013. Researcher employs correlation test, multivariate co-integration framework, Vector Auto Regressive error-correction model and Granger causality test with reference to financial up evils in Asia and world viz., Asian crisis (1997/98), financial crisis (2008) Inflation conditions, Natural disasters, financial up evils etc. of long run relationship. Results find that the Indian stock market return is significantly co-integrated with long run and short run situations/causalities in Asian Stock returns.

Bull and Bear Phases: An Empirical Perusal of Indian Stock Market NSE and BSE Stock Markets?

2016 ◽  
Vol 6 (2) ◽  
Author(s):  
Sunaina Kanojia ◽  
Neha Arora
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Prices ◽  
Time Series Data ◽  
Series Data ◽  
Bear Market ◽  
Bull Markets ◽  
Indian Stock Market ◽  
Increases In Risk ◽  
Considerable Support

In general, any one known to stock market is acquainted with the phenomenon of bull and bear phases, but whether the traders or investors put air to these phases while making a decision to buy, sell, or stay invested. The present paper attempts to identify and analyze the two most popular market phases, i.e. bull and bear, for better investment decisions with the use of Bry and Boschan Algorithm and time series data. Further, it seeks to analyze the distributional characteristics of the variances in stock returns and search evidence of asymmetries, if any, in volatility under different market conditions which may help to shed light on the bull and bear phases of Indian equity market. The study arrange for evidence that in bull markets, stock prices run far ahead of earnings and for fairly long periods of time. The paper indicates 12 bull and bear phases in the Sensex and Nifty during the sample period of 19 years with the associated factors responsible for the shift of bull and bear market phases. The results provide considerable support for the view that markets choose to ignore adverse possibilities and react with zest to favorable possibilities and market declines can partly be explained by increases in risk.

Variance Ratio Test of Random Walk in Indian Stock Market

2015 ◽  
Vol 2 (2) ◽  
pp. 89-107
Author(s):  
Saloni Gupta ◽  
Neha Bothra
Keyword(s):  
Random Walk ◽  
Stock Market ◽  
Stock Returns ◽  
Ratio Test ◽  
Variance Ratio ◽  
Random Walk Hypothesis ◽  
Variance Ratio Test ◽  
Indian Stock Market ◽  
High Degree ◽  
The Relationship

We conduct tests of the null hypothesis of a random walk at the aggregate level of market indices and disaggregate level of individual shares to the Indian stock market over various data periods and a comparison of two sub-periods namely the pre liberalization and the post liberalization period. For this, we use the Lo-MacKinlay (1988) variance ratio test. Although the oldest test i.e. the serial correlation coefficient test is also applied to the same data to establish the relationship between the two tests but its results are not elaborated in this paper. The strength of this paper lies in the voluminous data base and a powerful testing tool that it makes use of. It is observed that the market is highly inefficient at daily returns level, thus imbibing high degree of predictability in stock returns, and even the weekly returns show the existence of trend. Monthly returns, however, support the random walk hypothesis across all periods. Thus it is concluded that further refinement of reform measures is required.

Volatility in the Indian Stock Market: Evidence from S&P CNX 500

2020 ◽  
Vol 17 (4) ◽  
pp. 1826-1830
Author(s):  
V. Shanthaamani ◽  
V. B. Usha
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Risk Premium ◽  
Conditional Variance ◽  
Leverage Effect ◽  
Volatility Clustering ◽  
Expected Stock Returns ◽  
High Volatility ◽  
Indian Stock Market ◽  
Daily Returns

This paper uses the Generalized Autoregressive Conditional Heteroskedastic models to estimate volatility (conditional variance) in the daily returns of the S&P CNX 500 index over the period from April 2007 to March 2018. The models include both symmetric and asymmetric models that capture the most common stylized facts about index returns such as volatility clustering and leverage effect. The empirical results show that the conditional variance process is highly persistent and provide evidence on the existence of risk premium for the S&P CNX 500 index return series which support the positive correlation hypothesis between volatility and the expected stock returns. Our findings also show that the asymmetric models provide better fit than the symmetric models, which confirms the presence of leverage effect. These results, in general, explain that high volatility of index return series is present in Indian stock market over the sample period.

Investor attention using the Google search volume index – impact on stock returns

2019 ◽  
Vol 11 (1) ◽  
pp. 55-69 ◽  
Author(s):  
Vighneswara Swamy ◽  
Munusamy Dharani
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Volume Index ◽  
Trading Behavior ◽  
Excess Returns ◽  
Investor Attention ◽  
Content Type ◽  
Search Volume ◽  
Indian Stock Market ◽  
Google Search

Purpose The purpose of this paper is to investigate whether the investor attention using the Google search volume index (GSVI) can be used to forecast stock returns. The authors also find the answer to whether the “price pressure hypothesis” would hold true for the Indian stock market. Design/methodology/approach The authors employ a more recent fully balanced panel data for the period from July 2012 to Jun 2017 (260 weeks) of observations for companies of NIFTY 50 of the National Stock Exchange in the Indian stock market. The authors are motivated by Tetlock (2007) and Bijl et al. (2016) to employ regression approach of econometric estimation. Findings The authors find that high Google search volumes lead to positive returns. More precisely, the high Google search volumes predict positive and significant returns in the subsequent fourth and fifth weeks. The GSVI performs as an useful predictor of the direction as well as the magnitude of the excess returns. The higher quantiles of the GSVI have corresponding higher excess returns. The authors notice that the domestic investor searches are correlated with higher excess returns than the worldwide investor searches. The findings imply that the signals from the search volume data could be of help in the construction of profitable trading strategies. Originality/value To the best of the authors knowledge, no paper has examined the relationship between Google search intensity and stock-trading behavior in the Indian stock market. The authors use a more recent data for the period from 2012 to 2017 to investigate whether search query data on company names can be used to predict weekly stock returns for individual firms. This study complements the prior studies by investigating the relationship between search intensity and stock-trading behavior in the Indian stock market.

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