A Study of the Relationships Between Returns, Volatility, and Trading Volume at the Market and Individual Share Levels Using the Jakarta Stock Exchange PDF Download
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Author: Bramantyo Djohanputro
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Bramantyo Djohanputro
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Nada Ibrahim Abu Aljarayesh
Publisher:
ISBN:
Category :
Languages : en
Pages : 8
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The three main objectives of the study have accomplished by the analysis; is to examine the relationship between stock return and trading volume in Jordan ASE market. Plus to conclude whether the relationship of trading volume and stock return on Jordan ASE market is reliable with the weak-form of the efficient market hypothesis (EMH). Least, the relationship between stocks return volatility and trading volume in Jordan ASE market has been investigated. The experimental results verify a significant positive relationship between stock return and trading volume. Thus, the first objective is satisfied. Second objective is proven that ASE market is contradicted with the weak-form of EMH. The results of the GARCH (1,1) model illustration that the ASE displays strong volatility persistence and that the past volatility be able to explicate the current.
Author: Markus Glaser
Publisher:
ISBN:
Category :
Languages : en
Pages : 38
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Anecdotal evidence suggests and recent theoretical models argue that past stock returns affect subsequent stock trading volume. We study 3,000 individual investors over a 51 month period to test this apparent link between past returns and volume using several different panel regression models (linear panel regressions, negative binomial panel regressions, Tobit panel regressions). We find that both past market returns as well as past portfolio returns affect trading activity of individual investors (as measured by stock portfolio turnover, the number of stock transactions, and the propensity to trade stocks in a given month). After high portfolio returns, investors buy high risk stocks and reduce the number of stocks in their portfolio. High past market returns do not lead to higher risk taking or underdiversification. We argue that the only explanations for our findings are overconfidence theories based on biased self-attribution and differences of opinion explanations for high levels of trading activity.
Author:
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ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 650
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Theses on any subject submitted by the academic libraries in the UK and Ireland.
Author: Rochelle L. Antoniewicz
Publisher:
ISBN:
Category : Rate of return
Languages : en
Pages : 66
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Author: Gurmeet Singh
Publisher:
ISBN:
Category :
Languages : en
Pages : 23
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This paper investigates the empirical relationship between return, volume and volatility dynamics of stock market by using data of the NIFTY index of NSE during the period from Jan 2007 to March 2014. The volatility in the Indian stock market exhibits characteristics similar to those found earlier in many of the major developed and emerging stock markets. It is shown that ARCH family models outperform the conventional OLS models. We find that, the TARCH model is better fit, when we compare the GARCH, EGARCH and TARCH models, on the basis of AIC and SC criteria. Causality from volatility to volume can be seen as some evidence that new information arrival might follow a sequential rather than a simultaneous process. Moreover, in the GARCH model, ARCH and GARCH effects remain significant, which highlights the inefficiency in the market. In addition, EGARCH and TARCH models indicate the presence of leverage effect and positive impact of volatility on returns. Finally, the findings of granger causality test records the evidence of one way causality from volatility to trading volume and from return to volume.
Author: Zaäfri A. Husodo
Publisher:
ISBN:
Category :
Languages : en
Pages : 57
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This study examines the price formation process, noise level, non-trading and component of bid-ask spreads of individual shares listed on the Jakarta Stock Exchange for the period from 2000 to 2004. The price formation process is estimated using speed of adjustment based on the simple partial adjustment method proposed by Amihud and Mendelson (1987) that was later extended into ARMA(1,1) estimation by Theobald and Yallup (2004). The results are consistent with studies for the U.S. and European markets that find short term underreaction in security returns for most stocks. We further find that large companies lead small companies in price adjustment to new information. Using the intervalling properties to approximate the time to adjustment, we find that large stocks need only one day to fully adjust to new information, while medium and small stocks need three and five days respectively. The predominant factor contributing to the speed for adjustment for stocks listed on the Jakarta Stock Exchange in the ARMA(1,1) model is the MA(1) component reflecting significant noise in the price formation process. We assume that noise contains two microstructure components, non-trading and bid-ask bounce. This study finds that the role of non-trading is too small to be reliably justified as the source of negative autocorrelation, resulting in overreaction to the speed of adjustment. Further evidence revealed that the role of bid-ask spread is significant in determining the sign of the autocorrelation coefficient. The decomposition of bid-ask spreads disclosed that the size of the adverse selection spread cost component is negatively correlated with the speed of adjustment.
Author: Matthias Burghardt
Publisher: Springer Science & Business Media
ISBN: 3834961701
Category : Business & Economics
Languages : en
Pages : 170
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Using a unique data set consisting of more than 36.5 million submitted retail investor orders over the course of five years, Matthias Burghardt constructs an innovative retail investor sentiment index. He shows that retail investors’ trading decisions are correlated, that retail investors are contrarians, and that a profitable trading strategy can be based on these aggregated sentiment measures.
Author: Gong-meng Chen
Publisher:
ISBN:
Category :
Languages : en
Pages :
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We examine the dynamic relation between returns, volume, and volatility of stock indexes. The data come from nine national markets and cover the period from 1973 to 2000. The results show a positive correlation between trading volume and the absolute value of the stock price change. Granger causality tests demonstrate that for some countries, returns cause volume and volume causes returns. Our results indicate that trading volume contributes some information to the returns process. The results also show persistence in volatility even after we incorporate contemporaneous and lagged volume effects. The results are robust across the nine national markets.
Author: Robert Maria Margaretha Jozef Bauer
Publisher:
ISBN: 9789090107905
Category :
Languages : en
Pages : 191
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