scholarly journals Expiration-Day Effects of Index Futures in a Frontier Market: The Case of Ho Chi Minh Stock Exchange

2021 ◽  
Vol 10 (1) ◽  
pp. 3
Author(s):  
Anh Thi Kim Nguyen ◽  
Loc Dong Truong ◽  
H. Swint Friday
Keyword(s):  
Regression Models ◽  
Trading Volume ◽  
Stock Exchange ◽  
Market Volatility ◽  
Market Returns ◽  
Index Futures ◽  
Daily Return ◽  
Market Return ◽  
Index Stock ◽  
Level Of Significance

This study employs OLS, GARCH and EGARCH regression models to test the expiration-day effects of index stock futures on market returns, volatility and trading volume for the Ho Chi Minh Stock Exchange (HOSE). Data used in this study is from a daily return series of the VN30-Index for the period from 10August 2017 through 30 June 2020. The results derived from GARCH(1,1) and EGARCH(1,1) models consistently confirm that Index futures expiration-day effects on market returns exists in the HOSE. Specifically, the average market return for expiration days is significantly lower than other trading days, by 0.13% at the 5% level of significance. However, the results obtained from the regression models indicate that the expiration-day has no impact on market volatility and trading volume.

scholarly journals Type of Traders’ Effect on Risk and Return: The Case of Egyptian Stock Exchange

2016 ◽  
Vol 8 (2) ◽  
pp. 256
Author(s):  
Aly Saad Mohamed Dawood ◽  
Khairy El-Giziry
Keyword(s):  
Stock Market ◽  
Capital Market ◽  
Trading Volume ◽  
Stock Exchange ◽  
Garch Model ◽  
Market Returns ◽  
Market Values ◽  
Market Return ◽  
Lag Structure ◽  
Egyptian Stock Exchange

<p>This research paper aims to estimate the effect of investor categories (Foreigners, Arab, Egyptian institutions and individuals) trading volume, value and number of transactions on capital market returns and volatility.  </p><p>We depend on data Foreigners, Arabian and Egyptian trading volume, values and number of transaction of buying and selling for institutions and individuals and capital market values for the period from January 1st 2009 to December 31 2013.</p><p>We used descriptive statistics to identify normal distribution of data. Then, performing lead lag structure approach to obtain the optimum lag for the independent variable which has the maximum correlation with the dependent variable. Next, Garch model utilized to estimate the effect of trading volume, value, number of transactions on capital market return and volatility. Finally, the same model utilized to estimate the effect of investor categories on capital market return and volatility for the six periods starting from January 1<sup>st</sup> 2009 to December 31 2013 which represents the whole period and five yearly periods for the same period.</p><p>We found that institutions are the main source of volatility in the Egyptian stock market. Garch models showed weak effect on volatility for all periods. In the light of this study Foreigners and trading value items are the main source of effect on volatility. Finally, consistent with Chou (1988), the findings of GARCH model indicated that volatility persistence is less than unity which revealed that the Egyptian stock market could absorb shocks across time.</p>

scholarly journals Volatility of Dhaka Stock Exchange

2016 ◽  
Vol 8 (5) ◽  
pp. 220
Author(s):  
Md. Noman Siddikee ◽  
Noor Nahar Begum
Keyword(s):  
Standard Deviation ◽  
Emerging Markets ◽  
Stock Exchange ◽  
Research Work ◽  
Spillover Effects ◽  
Market Volatility ◽  
Factors Affecting ◽  
Daily Return ◽  
Market Return ◽  
General Index

We apply GARCH (p, q) and ARCH(m) model to the daily return of DSE general index (DGEN) ranging from 1<sup>st</sup> January, 2002 to 30<sup>th</sup> July 2013 for examining market volatility. Besides, we calculate year wise standard deviation of daily return of DGEN for the same period. The result of GARCH (1, 1) process and standard deviation of the daily return confirms an abnormal volatility episode from 2009 to 2012. The highest per day volatility was observed in the first half of 2011 in both investigations. The volatility rate found in GARCH (1, 1) process is 2.44% in 2011 followed by 2.00% and 1.99% in 2009 and 2012 respectively. The highest standard deviation of return is 2.99% in 2011 followed by 2.08% in 2012 authenticate the highest volatile periods of the study. We apply ARCH (m) model in 2004 and 2013 for volatility estimate due to inapplicability of GARCH (p, q) process in those market return. The results of ARCH (m) model confirm reliable estimates of market volatility, 1.10% and 1.46% respectively. This is a part of our total research work where our main focus is to detect the factors affecting market volatility and its spillover effects in emerging markets.

scholarly journals Forecasting stock returns on the Amman Stock Exchange: Do neural networks outperform linear regressions?

2021 ◽  
Vol 18 (4) ◽  
pp. 280-296
Author(s):  
Abdel Razzaq Al Rababa’a ◽  
Zaid Saidat ◽  
Raed Hendawi
Keyword(s):  
Stock Market ◽  
Regression Models ◽  
Linear Models ◽  
Stock Exchange ◽  
Market Returns ◽  
Stock Market Returns ◽  
Statistical Measures ◽  
Amman Stock Exchange ◽  
Non Linear ◽  
Ann Models

Different models have been used in the finance literature to predict the stock market returns. However, it remains an open question whether non-linear models can outperform linear models while providing accurate predictions for future returns. This study examines the prediction of the non-linear artificial neural network (ANN) models against the baseline linear regression models. This study aims specifically to compare the prediction performance of regression models with different specifications and static and dynamic ANN models. Thus, the analysis was conducted on a growing market, namely the Amman Stock Exchange. The results show that the trading volume and interest rates on loans tend to explain the monthly returns the most, compared to other predictors in the regressions. Moreover, incorporating more variables is not found to help in explaining the fluctuations in the stock market returns. More importantly, using the root mean square error (RMSE), as well as the mean absolute error statistical measures, the static ANN becomes the most preferred model for forecasting. The associated forecasting errors from these metrics become equal to 0.0021 and 0.0005, respectively. Lastly, the analysis conducted with the dynamic ANN model produced the highest RMSE value of 0.0067 since November 2018 following the amendment to the Jordanian income tax law. The same observation is also seen since the emerging of the COVID-19 outbreak (RMSE = 0.0042).

scholarly journals PEMILU SERENTAK DALAM KAITANNYA TERHADAP REAKSI PASAR MODAL DI INDONESIA

2020 ◽  
Vol 25 (1) ◽  
pp. 54-64
Author(s):  
Niken Kusumawardani
Keyword(s):  
Capital Market ◽  
Abnormal Return ◽  
Trading Volume ◽  
Stock Exchange ◽  
Volume Activity ◽  
Political Event ◽  
The Public ◽  
Significant Difference ◽  
Before And After ◽  
Index Stock

This study aims to determine the effect of simultaneous elections in Indonesia, namely legislative and executive elections that occur simultaneously together with the reaction in the capital market. Market reaction is measured using trading volume activity and returns stock that occur within the timeframe before and after the holding of simultaneous elections, namely on the date before and after April 17, 2019. The population in this study is the issuer that actively trades its shares on the Indonesia Stock Exchange (IDX) in Compass100 Index stock category. The research hypothesis was tested with an independent sample t-test using software SPPS26. Hypothesis testing results indicate a significant difference in trading volume activity that occurs before and after simultaneous elections. While the variable abnormal return there is no significant difference before and after the election simultaneously. This research is expected to be a reference for all parties concerned including the public towards a political event that occurs in this case specifically the simultaneous elections for decision making related to investment activities in stock instruments

Relationship Between FIIs’ Herding and Returns in the Indian Equity Market: Further Empirical Evidence

2019 ◽  
pp. 097215091984522
Author(s):  
Kapil Choudhary ◽  
Parminder Singh ◽  
Amit Soni
Keyword(s):  
Stock Market ◽  
Empirical Evidence ◽  
Trading Volume ◽  
Negative Relationship ◽  
Positive Association ◽  
Vital Role ◽  
Market Volatility ◽  
Market Returns ◽  
Stock Market Returns ◽  
Stock Market Trading

Empirical evidence indicates that foreign institutional investors (FIIs) play a vital role in financial markets, and being the major players, they demonstrate positive feedback trading behaviour and usually follow one another’s actions. In order to examine this phenomenon, the present study endeavoured to unearth the relationship between foreign institutional investments (FIIs) and returns in the Indian stock market, trading volume and volatility. The return of the Nifty50 index has surrogated market returns, while volatility is represented by conditional volatility computed from Nifty50, from January 1999 to May 2017. The vector autoregression (VAR) results indicate a positive association between herding among FIIs and lagged market returns, while information asymmetry has no impact on herding. On the other hand, previous-day volatility has a significant bearing on the herding measure. Overall, the results portray a significant relationship between herding and stock market returns in India. The results of multivariate regression exhibit that market return was a primary factor for FII herding during the study period under consideration, while trading volume bore no relationship with herding. In case of market volatility, the empirical results are in congruence with the fact that during the period of the volatile market, FIIs prefer to not indulge in herding. Furthermore, the results of three sub-periods, that is, before, during and after the crisis, are similar to the results of the whole study period which indicates that the return is a prime and vital force for herding; on the contrary, market volatility appears to have a negative relationship with herding.

scholarly journals Macroeconomic Variables and Stock Market Returns in Sri Lanka

2017 ◽  
Vol 9 (2) ◽  
pp. 206
Author(s):  
Saseela Balagobei
Keyword(s):  
Exchange Rate ◽  
Sri Lanka ◽  
Stock Market ◽  
Inflation Rate ◽  
Stock Exchange ◽  
Market Returns ◽  
Stock Market Returns ◽  
Macroeconomic Variables ◽  
Market Return ◽  
Stock Market Return

The stock market is one of the most energetic sectors that play an important role in contributing to the wealth of the economy. It plays a crucial role in the economic growth and development of an economy which would benefit industries, trade and commerce as a whole. The aim of this study is to investigate the impact of macroeconomic variables on stock market returns in Sri Lanka. Dependent variable of this study is stock market return measured by All Share Price Index (ASPI) and All Share Total Return Index (ASTRI) and independent variables are macroeconomic variables, such as Interest Rate (IR), Inflation Rate (INF), Exchange Rate (ER), Factory Industry Production Index (FIPI) and money supply (MS).  The study targets all the companies listed and active in Colombo Stock Exchange (CSE) from 2006 to 2015. For analysis, secondary data was collected from annual reports of Central bank of Sri Lanka, Colombo Stock Exchange, Securities and Exchange Commission and Department of Census and Statistics. The results of the study reveal that the stock market returns is influenced by macroeconomic variables except money supply in Sri Lanka. Interest rate and factory industry production have negative influence on stock market return in Colombo Stock exchange while inflation rate and exchange rate have positive influence on stock market return. The findings of the study may be useful to public and economy especially stock market investors to focus the macroeconomic variables for making their effective decisions in order to enhance their stock market returns.

Modelling Regulatory Change V's Volume, of Trading Effects in HSIF and HSI Volatility

2008 ◽  
Vol 11 (01) ◽  
pp. 47-59 ◽  
Author(s):  
Gerard Gannon ◽  
Siu Pang Au-Yeung
Keyword(s):  
Trading Volume ◽  
Structural Breaks ◽  
Structural Changes ◽  
Systematic Approach ◽  
Futures Market ◽  
Likelihood Ratio Tests ◽  
Market Volatility ◽  
Regulatory Change ◽  
Index Futures ◽  
Dummy Variables

In an earlier paper, we adopted a bi-variate BEKK–GARCH framework and employed a systematic approach to examine structural breaks in the Hang Seng Index and Index Futures market volatility. Switching dummy variables were included and tested in the variance equations to check for any structural changes in the autoregressive volatility structure due to the events that have taken place in the Hong Kong market surrounding the Asian markets crisis. In this paper, we include measures of daily trading volume from both markets in the estimation. Likelihood ratio tests indicate the switching dummy variables become insignificant and the GARCH effects diminish but remain significant. There is some evidence that the Sequential Arrival of Information Model (SIM) provides a platform to explain these market induced effects when volume of trade is accounted for.

scholarly journals Economic Freedom And Investment Efficiency In The MENA Region

2017 ◽  
Vol 33 (4) ◽  
pp. 841
Author(s):  
Asma’a Al-Amarneh
Keyword(s):  
Regression Models ◽  
Economic Freedom ◽  
Investment Efficiency ◽  
Market Returns ◽  
Mena Region ◽  
Volatility Risk ◽  
Market Return ◽  
The Government ◽  
Using Data ◽  
The Cost

The purpose of this study is to investigate the effect of economic freedom level on investment efficiency; predicted by market return and volatility; using data covering the period from 1996 tell 2015 for the MENA region countries. Simple regression models and multivariate regression models were applied to test our hypothesis. The results show that the economic freedom level has a little impact on market return, and the capital market performance get better as the government regulations get highly efficient and the financial system is accessible and effi­ciently functioning. In the same time, the evidence points out that economic freedom decrease market returns’ volatility (risk), indicating that; if government’s regulation in banking and financial systems doesn’t assure transparency and honesty, then financial markets efficiency will be hindered, the cost of financing will increase and the completion will be limited. Keeping in mind that the two fundamental aspects of investment are risk and return; it is obvious that economic freedom enhances the risk-return investment efficiency in the MENA region.

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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