Intra-group and inter-group trading differentiation analysis: A new approach for detecting the effect of trading volume on stock market fluctuations

2011 ◽  
Vol 390 (23-24) ◽  
pp. 4403-4410 ◽  
Author(s):  
Meysam Bolgorian
Keyword(s):  
Stock Market ◽  
Trading Volume ◽  
New Approach

scholarly journals Information Asymmetry, Ownership Structure and Cost of Equity Capital: The Formation for Open Innovation

2021 ◽  
Vol 7 (1) ◽  
pp. 48
Author(s):  
Ade Imam Muslim ◽  
Doddy Setiawan
Keyword(s):  
Stock Market ◽  
Information Asymmetry ◽  
Ownership Structure ◽  
Trading Volume ◽  
Foreign Ownership ◽  
Equity Capital ◽  
Cost Of Equity Capital ◽  
Cost Of Equity ◽  
Ownership Structures ◽  
The Cost

Our study aims to investigate how information asymmetry and ownership structure affect cost of equity capital. For that purpose, we collected 246 issuers over 4 years for a total of 984 observations. By using panel data processing, we found that the information asymmetry we proxied through Price non-Synchronization and trading volume had an effect on the cost of equity capital. Our results also confirmed both Agency Theory and Pecking Order Theory. Both theories are in line with the conditions of the stock market in Indonesia. In addition, we found that institutional and foreign ownership structures also had an effect on the cost of equity capital. Furthermore, our results also confirmed Interest Alignment Theory and Entrenchment Theory. Our research is expected to contribute to the debate on the existence of information asymmetry and ownership structures in relation to the cost of equity capital. We also hope that it will be a valuable input for investors in considering their investment. Moreover, from the results of this study, investors can also consider foreign ownership or institutional ownership in determining their investment. In addition, stock market regulators in Indonesia can develop approaches to minimize information asymmetry and encourage foreign investors to invest in Indonesia.

scholarly journals Relationship between Trading Volume and Asymmetric Volatility in the Korean Stock Market

2012 ◽  
Vol 03 (05) ◽  
pp. 584-589 ◽  
Author(s):  
Ki-Hong Choi ◽  
Zhu-Hua Jiang ◽  
Sang Hoon Kang ◽  
Seong-Min Yoon
Keyword(s):  
Stock Market ◽  
Trading Volume ◽  
Asymmetric Volatility ◽  
Korean Stock Market

scholarly journals Is Abnormally Large Volume a Clue?

2016 ◽  
Vol 8 (9) ◽  
pp. 226
Author(s):  
Tsung-Hsun Lu ◽  
Jun-De Lee
Keyword(s):  
Stock Market ◽  
Large Volume ◽  
Stock Prices ◽  
Stock Price ◽  
Trading Volume ◽  
Sensitive Analysis ◽  
Support Level ◽  
Out Of Sample ◽  
Sample Test ◽  
Abnormal Trading Volume

This paper investigates whether abnormal trading volume provides information about future movements in stock prices. Utilizing data from the Taiwan 50 Index from October 29, 2002 to December 31, 2013, the researchers employ trading volume rather than stock price to test the principles of resistance and support level employed by technical analysis. The empirical results suggest that abnormal trading volume provides profitable information for investors in the Taiwan stock market. An out-of-sample test and a sensitive analysis are conducted for the robustness of the results.

A NEW APPROACH OF BIVARIATE FUZZY TIME SERIES ANALYSIS TO THE FORECASTING OF A STOCK INDEX

2003 ◽  
Vol 11 (06) ◽  
pp. 671-690 ◽  
Author(s):  
YU-YUN HSU ◽  
SZE-MAN TSE ◽  
BERLIN WU
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Trading Volume ◽  
Arma Model ◽  
Future Trend ◽  
Fuzzy Time Series ◽  
Stock Index ◽  
New Approach ◽  
Series Analysis ◽  
Closing Price

In recent years, the innovation and improvement of forecasting techniques have caught more and more attention. Especially, in the fields of financial economics, management planning and control, forecasting provides indispensable information in decision-making process. If we merely use the time series with the closing price array to build a forecasting model, a question that arises is: Can the model exhibit the real case honestly? Since, the daily closing price of a stock index is uncertain and indistinct. A decision for biased future trend may result in the danger of huge lost. Moreover, there are many factors that influence daily closing price, such as trading volume and exchange rate, and so on. In this research, we propose a new approach for a bivariate fuzzy time series analysis and forecasting through fuzzy relation equations. An empirical study on closing price and trading volume of a bivariate fuzzy time series model for Taiwan Weighted Stock Index is constructed. The performance of linguistic forecasting and the comparison with the bivariate ARMA model are also illustrated.

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 Trading Volume as a Predictor of Market Movement

2019 ◽  
Vol 8 (2) ◽  
pp. 57-69
Author(s):  
Edson Kambeu
Keyword(s):  
Logistic Regression ◽  
Stock Market ◽  
Regression Model ◽  
Trading Volume ◽  
Logistic Regression Model ◽  
Predictive Analytics ◽  
Stock Exchange ◽  
Stock Market Index ◽  
Trading Volumes ◽  
Popular Language

A logistic regression model is has also become a popular model because of its ability to predict, classify and draw relationships between a dichotomous dependent variable and dependent variables. On the other hand, the R programming language has become a popular language for building and implementing predictive analytics models. In this paper, we apply a logistic regression model in the R environment in order to examine whether daily trading volume at the Botswana Stock Exchange influence daily stock market movement. Specifically, we use a logistic regression model to find the relationship between daily stock movement and the trading volumes experienced in the recent five previous trading days. Our results show that only the trading volume for the third previous day influence current stock market index movement. Overall, trading volumes of the past five days were found not have an impact on today’s stock market movement. The results can be used as a basis for building a predictive model that utilizes trading as a predictor of stock market movement.

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.

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