scholarly journals The Dynamic Relationship between Price and Trading Volume: Evidence from Indian Stock Market

2009 ◽  
Author(s):  
Brajesh Kumar ◽  
Priyanka Singh ◽  
Ajay Pandey
Keyword(s):  
Stock Market ◽  
Trading Volume ◽  
Dynamic Relationship ◽  
Indian Stock Market

scholarly journals The dynamic relationship between stock returns and trading volumes in Nepalese stock market

2020 ◽  
Vol 23 (2) ◽  
pp. 161-172
Author(s):  
Prem Lal Adhikari
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Trading Volume ◽  
Time Series Data ◽  
Stock Exchange ◽  
Causal Effect ◽  
Empirical Relationship ◽  
Insurance Companies ◽  
Series Data ◽  
Dynamic Relationship

 In finance, the relationship between stock returns and trading volume has been the subject of extensive research over the past years. The main motivation for these studies is the central role that trading volume plays in the pricing of financial assets when new information comes in. As being interrelated and interdependent subjects, a study regarding the trading volume and stock returns seem to be vital. It is a well-researched area in developed markets. However, very few pieces of literature are available regarding the Nepalese stock market that explores the association between trading volume and stock return. Realizing this fact, this paper aims to examine the empirical relationship between trading volume and stock returns in the Nepalese stock market using time series data. The study sample is comprised of 49 stocks traded on the Nepal Stock Exchange (NEPSE) from mid-July 2011 to mid-July 2018. This study examines the Granger Causality relationship between stock returns and trading volume using the bivariate VAR model used by de Medeiros and Van Doornik (2008). The study found that the overall Nepalese stock market does not have a causal relationship between trading volume and return on the stock. In the case of sector-wise study, there is a unidirectional causality running from trading volume to stock returns in commercial banks and stock returns to trading volume in finance companies, hydropower companies, and insurance companies. There is no indication of any causal effect in the development bank, hotel, and other sectors. This study also finds that there is no evidence of bidirectional causality relationships in any sector of the Nepalese stock market.

scholarly journals A Study of Black–Scholes Model’s Applicability in Indian Capital Markets

Paradigm ◽  
2020 ◽  
Vol 24 (1) ◽  
pp. 73-92
Author(s):  
Anubha Srivastava ◽  
Manjula Shastri
Keyword(s):  
Stock Market ◽  
Option Pricing ◽  
Trading Volume ◽  
Stock Exchange ◽  
Market Price ◽  
Significant Degree ◽  
Pricing Model ◽  
Fair Price ◽  
Black Scholes ◽  
Indian Stock Market

Derivative trading, started in mid-2000, has become an integral and significant part of Indian stock market. The tremendous increase in trading volume in Indian stock market has reflected into high volatility in the option prices. The pricing of options is very complex aspect of applied finance and has been subject of extensive research. Black–Scholes option model is a scientific pricing model which is applied for determining the fair price for option contracts. This article examines if Black–Scholes option pricing model (BSOPM) is a good indicator of option pricing in Indian context. The literature review highlights that various studies have been conducted on BSOPM in various stock exchange across the world with mixed outcome on its relevance and applicability. This article is an empirical study to test the relevance of BSOPM for which 10 most popular industry’s stock listed on National Stock Exchange have been taken. Then the BSOPM has been applied using volatility and risk-free rate. Furthermore, t-test has been used to test the hypothesis and determine the significant relationship between BS model values and actual model values. This study concludes that BSOPM involves significant degree of mispricing. Hence, this model alone cannot be adopted as an indicator for option pricing. The variation from market price is synchronised with respect to moneyness and time to maturity of the option.

Momentum anomaly: evidence from the Indian stock market

2017 ◽  
Vol 14 (1) ◽  
pp. 3-22 ◽  
Author(s):  
Supriya Maheshwari ◽  
Raj Singh Dhankar
Keyword(s):  
Stock Market ◽  
Trading Volume ◽  
Investment Strategy ◽  
Momentum Effect ◽  
Momentum Strategies ◽  
Content Type ◽  
Momentum Profits ◽  
The Usa ◽  
Short Run ◽  
Indian Stock Market

Purpose The purpose of this paper is to provide insights into the profitability of momentum strategies in the Indian stock market. This study further evaluates whether the momentum effect is a manifestation of size, value or an illiquidity effect. Design/methodology/approach Monthly stock return data of 470 BSE listed stocks over the sample period from January 1997 to March 2013 were used to create extreme portfolios (winner and loser). The returns of extreme portfolios were evaluated using t-statistics and a risk-adjusted measure. Further checks were imposed by controlling for other potential sources of risk including size, value and illiquidity. Findings The study provides support in favor of momentum profitability in the Indian stock market. In contrast to the literature, momentum profitability is driven by winning stocks, and hence, buying past winning stocks generates higher returns than shorting loosing stocks in the Indian stock market. Strong momentum profits were observed even after controlling for size, value and trading volume of stocks. This suggests that the momentum effect in the Indian stock market is not a manifestation of small size effect, value effect or an illiquidity effect. Practical implications From the practitioner’s perspective, the study indicates that a momentum-based investment strategy in the short run is still persistent and can generate potential profits in the Indian stock market. Originality/value There is little empirical evidence on the momentum profitability, especially in the Indian stock market. The study contributes toward the literature by analyzing the momentum profitability even after controlling for size, value and an illiquidity effect. Some aspects of the momentum effect were observed to be dissimilar from those observed in literature for the USA and other countries. Such findings justify the need for testing the momentum profitability in stock markets other than the USA.

scholarly journals Multidimensional Liquidity: Evidences from Indian Stock Market

2016 ◽  
Vol 5 (2) ◽  
Author(s):  
Sharad Nath Bhattacharya ◽  
Pramit Sengupta ◽  
Mousumi Bhattacharya ◽  
Basav Roychoudhury
Keyword(s):  
Machine Learning ◽  
Stock Market ◽  
Stock Markets ◽  
Trading Volume ◽  
Stock Exchange ◽  
Market Liquidity ◽  
Percentage Error ◽  
Efficiency Coefficient ◽  
Learning Tools ◽  
Indian Stock Market

Various dimensions of liquidity including breadth, depth, resiliency, tightness, immediacy are examined using BSE 500 and NIFTY 500 indices from Indian Equity market. Liquidity dynamics of the stock markets were examined using trading volume, trading probability, spread, Market Efficiency coefficient, and turnover rate as they gauge different dimensions of market liquidity. We provide evidences on the order of importance of these liquidity measures in Indian stock market using machine learning tools like Artificial Neural Network (ANN) and Random Forest (RF). Findings reveal that liquidity variables collectively explains the movements of stock markets. Both these machine learning tools performs satisfactorily in terms of mean absolute percentage error. We also evidenced lower level of liquidity in Bombay Stock Exchange (BSE) than National Stock Exchange (NSE) and findings supports the liquidity enhancement program recently initiated by BSE.

scholarly journals Indian Stock Market during the COVID-19 Pandemic: Vulnerable or Resilient?: Sectoral analysis

2021 ◽  
Vol 12 (1) ◽  
pp. 131-159
Author(s):  
Rishika Shankar ◽  
Priti Dubey
Keyword(s):  
Stock Market ◽  
Trading Volume ◽  
Consistent Pattern ◽  
Daily Average ◽  
Sectoral Analysis ◽  
Indian Stock Market ◽  
The Impact

This study examines the impact of COVID-19 pandemic on the performance of Indian stock market, measured by daily average returns and trading volume. The analysis is aimed at discovering the vulnerability of the general market as well as nine crucial sectors to the pandemic while also checking the impact on overall volatility in the market. The findings suggest that all the sectors followed a consistent pattern of being significantly impacted by the pandemic. However, the benchmark index remained resilient in the context of average returns. The entire market witnessed decreased returns and increased liquidity, which is explained by reduced volatility in the market.

scholarly journals Dynamic Relationship between Stock Market Returns and Trading Volume: Evidence from Indian Stock Market

2016 ◽  
Vol 12 (2) ◽  
pp. 123-136
Author(s):  
NUPUR GUPTA BHATTACHARYA ◽  
Gopal Zavar
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Trading Volume ◽  
Information Dissemination ◽  
Emerging Market ◽  
Causality Test ◽  
Stock Market Returns ◽  
Dynamic Relationship ◽  
Contemporaneous Correlation ◽  
Total Turnover

This paper empirically examines the relationship between the stock returns & the trading volume for Sensex. Three main measures of volume traded namely number of shares traded; total turnover of the shares traded & the no. of transactions are used. Their daily data for a five year period were taken for the study. The contemporaneous correlation between the volume & returns was studied after it was found that there was no unit root in the data. A positive contemporaneous relation between the volume & the returns was found. The results from Granger causality test suggest us that the returns granger causes volume for Sensex. VAR test also suggests that the stock returns are dependent on the returns of the previous days. It can be explained as in an emerging market like India, the market development cause the sequential information dissemination. It can also be concluded that in Sensex, no. of transactions can prove to be a better proxy of information than number of shares traded or turnover.

Self-attribution, Overconfidence and Dynamic Market Volatility in Indian Stock Market

2018 ◽  
Vol 21 (4) ◽  
pp. 970-989
Author(s):  
Venkata Narasimha Chary Mushinada ◽  
Venkata Subrahmanya Sarma Veluri
Keyword(s):  
Stock Market ◽  
Trading Volume ◽  
Stock Exchange ◽  
Empirical Evaluation ◽  
Market Volatility ◽  
Crisis Period ◽  
Asymmetric Volatility ◽  
Attribution Bias ◽  
Indian Stock Market ◽  
Overconfidence Bias

The article provides an empirical evaluation of self-attribution, overconfidence bias and dynamic market volatility at Bombay Stock Exchange (BSE) across various market capitalizations. First, the investors’ reaction to market gain when they make right and wrong forecasts is studied to understand whether self-attribution bias causes investors’ overconfidence. It is found that when investors make right forecasts of future returns, they become overconfident and trade more in subsequent time periods. Next, the relation between excessive trading volume of overconfident investors and excessive prices volatility is studied. The trading volume is decomposed into a first variable related to overconfidence and a second variable unrelated to investors’ overconfidence. During pre-crisis period, the analysis of small stocks shows that conditional volatility is positively related to trading volume caused by overconfidence. During post-crisis period, the analysis shows that the under-confident investors became very pessimistic in small stocks and tend to overweight the future volatility. Whereas, the analysis of large stocks indicates that the overconfidence component of trading volume is positively correlated with the market volatility. Collectively, the empirical results provide strong statistical support to the presence of self-attribution and overconfidence bias explaining a large part of excessive and asymmetric volatility in Indian stock market.

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