Long Memory in Stock Trading Volume: Evidence from Indian Stock Market

2004 ◽  
Author(s):  
Alok Kumar
Keyword(s):  
Stock Market ◽  
Long Memory ◽  
Trading Volume ◽  
Stock Trading ◽  
Indian Stock Market

Single step estimation of ARMA roots for non-fundamental nonstationary fractional models

2022 ◽  
Author(s):  
Ignacio N Lobato ◽  
Carlos Velasco
Keyword(s):  
Stock Market ◽  
Strong Evidence ◽  
Long Memory ◽  
Trading Volume ◽  
Moving Average ◽  
Single Step ◽  
Arma Process ◽  
Dow Jones Industrial Average ◽  
Fractional Models ◽  
Stock Market Trading

Abstract We propose a single step estimator for the autoregressive and moving-average roots (without imposing causality or invertibility restrictions) of a nonstationary Fractional ARMA process. These estimators employ an efficient tapering procedure, which allows for a long memory component in the process, but avoid estimating the nonstationarity component, which can be stochastic and/or deterministic. After selecting automatically the order of the model, we robustly estimate the AR and MA roots for trading volume for the thirty stocks in the Dow Jones Industrial Average Index in the last decade. Two empirical results are found. First, there is strong evidence that stock market trading volume exhibits non-fundamentalness. Second, non-causality is more common than non-invertibility.

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.

scholarly journals Long Memory in Stock-Market Trading Volume

2000 ◽  
Vol 18 (4) ◽  
pp. 410-427 ◽  
Author(s):  
Ignacio N. Lobato ◽  
Carlos Velasco
Keyword(s):  
Stock Market ◽  
Long Memory ◽  
Trading Volume ◽  
Stock Market Trading

Examination of Long Memory in Indian Stock Market: A Sectoral Juxtaposition

2021 ◽  
pp. 231971452110402
Author(s):  
Ramashanti Naik ◽  
Y. V. Reddy
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Long Memory ◽  
Stock Exchange ◽  
Moving Average ◽  
Long Range Dependence ◽  
Dynamic Nature ◽  
Portfolio Managers ◽  
Indian Stock Market ◽  
Data Frequency

One of the situations encountered in time series analysis is long-range dependence, also known as Long memory. We investigated the presence of long memory in the Indian sectoral indices returns and investigated whether the long memory behaviour is affected by the data frequency. We applied the autoregressive fractionally integrated moving average (ARFIMA) models to 13 sectoral indices of the National Stock Exchange of India and examined the long memory in daily, monthly and quarterly return series. The results indicate the persistence in daily return series and anti-persistence in monthly and quarterly return series. Thus, we conclude that the frequency of data does have a significant effect on the behaviour of long memory patterns. The results will be helpful for present and potential investors, institutional investors, portfolio managers and policymakers to understand the dynamic nature of long memory in the Indian stock market.

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.

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