scholarly journals Price-volume relation behavior around structural breaks in Kuwait Boursa

2019 ◽  
Vol 15 (2) ◽  
pp. 1-13
Author(s):  
Fayez A. Abdulsalam ◽  
Amani kh. Bouresli
Keyword(s):  
Stock Prices ◽  
Trading Volume ◽  
Structural Breaks ◽  
Emerging Market ◽  
Securities Law ◽  
New Information ◽  
The 2008 Financial Crisis ◽  
Volume Relation ◽  
Sequential Information ◽  
The Garch Model

This study attempts to conduct a comprehensive investigation of the price-volume relation using daily stock prices of all publicly traded firms in Kuwait Boursa over the period 2005–2017. The aim is to provide evidence from an emerging market on the information arrival hypothesis, which is explained by the mixture of distribution and the sequential information arrival hypotheses. The investigation covered two main structural events; the 2008 financial crisis and the activation of Kuwait’s New Securities Law in 2010 (CMA). The GARCH-ARCH test revealed a positive contemporaneous relation between trading volume and market return, which implies that previous information shocks affect current returns and imply that Kuwait stock market is weakly efficient. When trading volume is included in the variance equation in the GARCH model, the test revealed that new information arrival is not simultaneously available to all traders and it takes time to observe, providing support to the sequential information arrival hypothesis (SAIH). Finally, there was no change in the price-volume relation around the two events and urgent assessment of the new market reform is recommended.

scholarly journals Bad Friday, Monday Effect and Political Issue: Application of ARCH-GARCH Model to Analyze Seasonal Pattern of Stock Return

2018 ◽  
Vol 7 (3.30) ◽  
pp. 38
Author(s):  
Maria Rio Rita ◽  
Sugeng Wahyudi ◽  
Harjum Muharam
Keyword(s):  
Stock Prices ◽  
Trading Volume ◽  
Stock Return ◽  
Seasonal Pattern ◽  
Garch Model ◽  
Statistical Characteristics ◽  
Political Issue ◽  
Political Issues ◽  
Monday Effect ◽  
The Garch Model

At the end of 2016, Indonesia was shaken by a demonstration of the election of the Governor of Jakarta Capital Special Region and political issues related to religious defamation. Does this condition have an impact on stock prices and returns? The aim of this study is to test the week day pattern in IDX using LQ-45 stocks during selected observation period of August 2016-January 2017. Then a GARCH model is used to investigate the presence of week day pattern in the stock market. Therefore, the GARCH model is able to describe observed statistical characteristics of many time series of financial assets return. The test results show that there is a difference in average stock return during the trading day. The lowest and the highest return are observed on Monday and Wednesday, respectively. Meanwhile, the average negative return on Friday is not proven to significantly drive the occurrence of Monday effect. Return on Monday is influenced by the frequency of trading, not by trading volume. Is there anything to do with the psychological aspect of investors solely in assessing risk acceptance to stocks? Research agenda related to this is very relevant to do in the future.  

scholarly journals Stock Price Fluctuations and Corporate Forecasts Disclosure: Evidence from an Emerging Market

2014 ◽  
Vol 9 (1) ◽  
pp. 1481-1495
Author(s):  
Abbas Bagherian Kasgari ◽  
Keyvan Sheykhi
Keyword(s):  
Capital Market ◽  
Significant Relationship ◽  
Stock Prices ◽  
Stock Price ◽  
Emerging Market ◽  
Price Change ◽  
The Other ◽  
Price Fluctuations ◽  
Price Manipulation ◽  
New Information

This research investigates the relation between forecasting report disclosure and stock price fluctuations. The first hypothesize examine if there is a relation between two variables among companies which lead to fluctuation in the stock price and the second hypothesize examined these variables over research industries. Capital market reacts to new information in most cases- at least one month before the official date of the disclosure. We found evidence of fluctuation in stock prices before disclosure indicate that information was released to the market before official disclosure. In the other word, stock prices react to the unofficially released information and rumors to the market around the releasing new officially disclosure date. This fraudulent attempt was initiated by price manipulation in cases which we don't see significant price change during forecasted disclosure even if there are significant change in reported earning values. This investigation indicates that there is a significant relationship between releasing forecasted information and stock price fluctuations in the selected listed companies in TSE.

Trading Volume and Cross-Autocorrelations of Stock Returns in Emerging Markets: Evidence from the Taiwan Stock Market

2004 ◽  
Vol 07 (04) ◽  
pp. 509-524
Author(s):  
Wen-Hsiu Kuo ◽  
Hsinan Hsu ◽  
Chwan-Yi Chiang
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Stock Prices ◽  
Trading Volume ◽  
Price Limits ◽  
Speed Of Adjustment ◽  
New Information ◽  
Weak Evidence ◽  
Market Inefficiency ◽  
Daily Returns

This study empirically investigates the interaction between trading volume and cross-autocorrelations of stock returns in the Taiwan stock market. The result shows that returns on high trading volume portfolios lead returns on low trading volume portfolios when controlled for firm size, indicating that trading volume determines lead-lag cross-autocorrelations of stock returns. Overall, the empirical findings of this study demonstrate similar results for both monthly and daily returns, suggesting that nonsynchronrous trading is not the main reason for the lead-lag cross-autocorrelations presented in this study. Consequently, the empirical results presented here support the speed of adjustment hypothesis, and suggest that some market inefficiency exists in the Taiwan stock market. Additionally, compared with evidence of lead-lag cross-autocorrelations in the larger, less regulated US stock market, as examined by Chordia and Swaminathan (2000), Taiwan stock market displays less evidence of VARs and Dimson beta regressions. We conjecture that this weak evidence may result from the regulations limiting daily price movements in the Taiwan stock market. Although the price limits policy lowers risk and stabilizes stock prices, it also prevents stock prices and trading volume from instantaneously and fully reflecting new information.

The asymmetric price-volume relation revisited: evidence from Qatar

2018 ◽  
Vol 12 (2) ◽  
pp. 193-219 ◽  
Author(s):  
Walid M.A. Ahmed
Keyword(s):  
Financial Services ◽  
Stock Price ◽  
Trading Volume ◽  
Emerging Market ◽  
Return Volatility ◽  
Data Set ◽  
Content Type ◽  
Level Data ◽  
The Future ◽  
Volume Relation

Purpose This study aims to revisit the stock price–volume relations, providing new evidence from the emerging market of Qatar. In particular, three main issues are examined using both aggregate market- and sector-level data. First, the return–volume relation and whether or not this relation is asymmetric. Second, the common characteristics of return volatility; and third, the nature of the relation between trading volume and return volatility. Design/methodology/approach The study uses the OLS and VAR modeling approaches to examine the contemporaneous and dynamic (causal) relations between index returns and trading volume, respectively, while an EGARCH-X(1,1) model is used to analyze the volatility–volume relation. The data set comprises daily index observations and the corresponding trading volumes for the entire market and the individual seven sectors of the Qatar Exchange (i.e. banks and financial services, consumer goods and services, industrials, insurance, real estate, telecommunications and transportation). Findings The empirical analysis reports evidence of a positive contemporaneous return–volume relation in all sectors barring transportation and insurance. This relation appears to be asymmetric for all sectors. For the market and almost all sectors, there is no significant causality between returns and volume. By and large, these findings lend support for the implications of the mixture of distributions hypothesis (MDH). Lastly, the information content of lagged volume seems to have an important role in predicting the future dynamics of return volatility in all sectors, with the industrials being the exception. Practical implications The findings provide important implications for portfolio managers and investors, given that the volume of transactions is generally found to be informative about the price movement of sector indices. Specifically, tracking the behavior of trading volume over time can give a broad portrayal of the future direction of market prices and volatility of equity, thereby enriching the information set available to investors for decision-making. Originality/value Based on both market- and sector-level data from the emerging stock market of Qatar, this study attempts to fill an important void in the literature by examining the return–volume and volatility–volume linkages.

scholarly journals The Turkish stock market integration with oil prices: Cointegration analysis with unknown regime shifts

2013 ◽  
Vol 60 (4) ◽  
pp. 499-513 ◽  
Author(s):  
Umut Halaç ◽  
Taşkın Dilvin ◽  
Çağlı Çağlar
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Market Integration ◽  
Structural Breaks ◽  
Emerging Market ◽  
Oil Prices ◽  
Economic Factor ◽  
Cointegration Analysis ◽  
Dynamic Relationship ◽  
Long Run

Oil prices are often considered as a vital economic factor due to the dependence of the world economy on oil. The goal of this paper is to contribute to the literature on the dynamic relationship between oil prices and stock prices under the presence of possible structural breaks in an emerging market, Turkey. The empirical evidence suggests that the oil prices are important in explaining the stock market movements. Stock prices, oil prices and nominal exchange rates are found as cointegrated after taking structural breaks into account. Moreover, results of parameter stability test are consistent with our findings indicating that relationship between series is strong in the long-run. The results are important in the way that they show the global factors are also dominant on the Turkish stock market.

scholarly journals Stock Market Volatility and Weak-form Efficiency: Evidence from an Emerging Market

2006 ◽  
Vol 45 (4II) ◽  
pp. 1029-1040 ◽  
Author(s):  
Abid Hameed ◽  
Hammad Ashraf
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Value At Risk ◽  
Emerging Market ◽  
Weak Form ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Economic Fundamentals ◽  
Hedging Strategies ◽  
New Information

There exists a vast literature on modeling and estimating aggregate stock market volatility over the past decade [e.g., Choudhry (1996); Mecagni and Sourial (1999) and Kabir, et al. (2000)]. Motivations for undertaking this exercise have been varied. Many value-at-risk models for measuring market risk require the estimation of volatility parameter. Portfolio diversifications and hedging strategies also require information on volatility as a key input. Volatility is defined as tendency of the assets price to fluctuate either up or down. Increased volatility is perceived as indicating a rise in financial risk which can adversely affect investor assets and wealth. It is observed that when stock market exhibit increased volatility there is a tendency on part of the investors to lose confidence in the market and they tend to exit the market. The nexus between volatility and economic fundamentals is still a moot point. Stock prices reflect information and quicker they are in absorbing accurately new information, more efficient is the stock market in allocating resources. The increase in volatility can be attributed to absorption of new information about economic fundamentals or some expectations about them. This kind of volatility is not harmful as there is no social cost associated with it. But if increased volatility is not explained by the level indicated by the fundamental economic factors, there is a tendency that stocks will be mispriced and this will lead to misallocation of resources [Karmaka (2006)].

Market Segmentation and Stock Prices: Evidence from an Emerging Market

1997 ◽  
Vol 52 (3) ◽  
pp. 1059-1085 ◽  
Author(s):  
IAN DOMOWITZ ◽  
JACK GLEN ◽  
ANANTH MADHAVAN
Keyword(s):  
Market Segmentation ◽  
Stock Prices ◽  
Emerging 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.

scholarly journals PENGARUH VOLUME PERDAGANGAN DAN SIMPLE MOVING AVERAGE TERHADAP HARGA SAHAM

2020 ◽  
Vol 1 (1) ◽  
pp. 1-16
Author(s):  
Gama Paksi Baskara ◽  
Suyanto Suyanto ◽  
Sri Retnaning Rahayu
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Trading Volume ◽  
Qualitative Data ◽  
Moving Average ◽  
Analytical Tool ◽  
T Test ◽  
Quantitative Approach ◽  
F Test ◽  
Regression Tests

Trading volume is a sheet of company shares traded on a particular transaction and has beenagreed between the seller and the buyer, Simple Moving Average is a method that studies themovement of the previous stock price based on the number of certain days in order to predict thestock price that will occur to the next.The objective of the study is to find out how much influenceTrade Volume and Simple Moving Average on Stock Prices is and what are the most dominantaspects in influencing Stock Prices. The type of the research uses a quantitative approach, namely anapproach in which the data are in the form of numbers or qualitative data that have been used asnumbers. The technique of collecting data uses documentation. The analytical tool used is multiplelinear regression tests including T Test, F Test and Coefisein R² Determination processed usingEviews. The results of the study show that partially the trading volume variable does not have asignificant effect on Stock Prices and the Simple Moving Average variable shows a positive andsignificant effect on stock prices while the results of the research simultaneously show that theTrading Volume and Simple Moving Average variables simultaneously affect the Stock Price .

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