scholarly journals The impact of GST implementation on the Malaysian stock market index volatility

2019 ◽  
Vol 26 (1) ◽  
pp. 17-33
Author(s):  
Razali Haron ◽  
Salami Mansurat Ayojimi
Keyword(s):  
Stock Market ◽  
Global Financial Crisis ◽  
Stock Index ◽  
Empirical Estimation ◽  
Stock Market Index ◽  
Content Type ◽  
Noticeable Increase ◽  
Market Index ◽  
The Government ◽  
The Impact

Purpose The purpose of this paper is to examine the impact of the Goods and Service Tax (GST) implementation on Malaysian stock market index. Design/methodology/approach This study used daily closing prices of the Malaysian stock index and futures markets for the period ranging from June 2009 to November 2016. Empirical estimation is based on the generalised autoregressive conditional heteroscedasticity (1, 1) model for pre- and post-announcement of the GST. Findings Result shows that volatility of Malaysian stock market index increases in the post-announcement than in the pre-announcement of the GST which indicates that educative programs employed by the government before the GST announcement did not yield meaningful result. The volatility of the Malaysian stock market index is persistent during the GST announcement and highly persistent after the implementation. Noticeable increase in post-announcement is in support with the expectation of the market about GST policy in Malaysia. Practical implications The finding of this study is consistent with expectation of the market that GST policy will increase the price of the goods and services and might reduce standard of living. This is supported by a noticeable increase in the volatility of the Malaysian stock market index in the post-announcement of GST which is empirically shown during the announcement and after the implementation of GST. Although the GST announcement could be classified as a scheduled announcement, unwillingness to accept the policy prevails in the market as shown by the increase in the market volatility. Originality/value Past studies on Malaysian stock market index volatility focus on the impact of Asian and global financial crisis whereas this study examines the impact of the GST announcement and implementation on the volatility of the Malaysian stock market index.

scholarly journals Quantitative relationship between corruption and development of the Tunisian stock market

2018 ◽  
Vol 7 (2) ◽  
pp. 39-47
Author(s):  
Ibtissem Missaoui ◽  
Mohsen Brahmi ◽  
Jaleleddine BenRajeb
Keyword(s):  
Stock Market ◽  
Stock Exchange ◽  
Quantitative Relationship ◽  
Stock Market Index ◽  
Private Companies ◽  
Stock Market Development ◽  
Market Index ◽  
The Government ◽  
The Impact ◽  
Positive Effect

The aim of this article is to seek especially the impact of corruption on the bond and stock market development. For the methodology/approach, the authors analyze a sample of 20 listed Tunisian firms from the Stock Exchange and Financial market, covering the period from 2006 to 2016 by using pooling cross section techniques. The results find a significant positive effect of the level of corruption on the stock market index and the logarithm of capitalization. This is consistent with the view that corruption accelerates the economic growth by speeding up transactions and allowing private companies to overcome the inefficiencies imposed by the government. Furthermore, the results find a negative association is not significant with the dependent variable of traded value as a percentage of the number of listed companies.

Impact of monetary policy on house prices: case of Pakistan

2019 ◽  
Vol 13 (3) ◽  
pp. 503-512
Author(s):  
Muhammad Umar ◽  
Moin Akhtar ◽  
Muhammad Shafiq ◽  
Zia-Ur-Rehman Rao
Keyword(s):  
Monetary Policy ◽  
Stock Market ◽  
Time Series Data ◽  
House Prices ◽  
Series Data ◽  
Stock Market Index ◽  
Content Type ◽  
Market Index ◽  
The Impact ◽  
The Relationship

Purpose This study aims to explore the impact of monetary policy on house prices in Pakistan. Design/methodology/approach This study uses monthly time-series data of house prices, monetary policy, inflation and stock market index ranging from January 2011 to December 2016. All the series were checked for stationarity by using augmented Dickey–Fuller test, and lag length of 11 was decided on the basis of Schwert’z rule of thumb. Vector autoregressive (VAR) model was used because the series were not co-integrated. Findings The analysis revealed that monetary policy significantly affects house prices in Pakistan. Tight monetary policy results in lower house prices and vice versa. The relationship between monetary policy and house prices is unidirectional. The study also finds that higher inflation also leads to soaring house prices, but the variation in stock market index does not affect house prices. Originality/value To the best of authors’ knowledge, none of the existing studies explores the impact of monetary policy on house prices in Pakistan. The findings help investors and policy makers to understand the relationship between monetary policy and house prices to make better decisions.

Linkages between cross-country business cycles, cross-country stock market cycles and cross-country real estate market cycles

2016 ◽  
Vol 9 (2) ◽  
pp. 123-146 ◽  
Author(s):  
Kim Hiang Liow
Keyword(s):  
Real Estate ◽  
Stock Market ◽  
Business Cycles ◽  
Spillover Effects ◽  
Real Estate Market ◽  
Stock Market Index ◽  
Content Type ◽  
Market Index ◽  
Cross Country ◽  
The Cross

Purpose This research aims to investigate whether and to what extent the co-movements of cross-country business cycles, cross-country stock market cycles and cross-country real estate market cycles are linked across G7 from February 1990 to June 2014. Design/methodology/approach The empirical approaches include correlation analysis on Hodrick–Prescott (HP) cycles, HP cycle return spillovers effects using Diebold and Yilmaz’s (2012) spillover index methodology, as well as Croux et al.’s (2001) dynamic correlation and cohesion methodology. Findings There are fairly strong cycle-return spillover effects between the cross-country business cycles, cross-country stock market cycles and cross-country real estate market cycles. The interactions among the cross-country business cycles, cross-country stock market cycles and cross-country real estate market cycles in G7 are less positively pronounced or exhibit counter-cyclical behavior at the traditional business cycle (medium-term) frequency band when “pure” stock market cycles are considered. Research limitations/implications The research is subject to the usual limitations concerning empirical research. Practical implications This study finds that real estate is an important factor in influencing the degree and behavior of the relationship between cross-country business cycles and cross-country stock market cycles in G7. It provides important empirical insights for portfolio investors to understand and forecast the differential benefits and pitfalls of portfolio diversification in the long-, medium- and short-cycle horizons, as well as for research studying the linkages between the real economy and financial sectors. Originality/value In adding to the existing body of knowledge concerning economic globalization and financial market interdependence, this study evaluates the linkages between business cycles, stock market cycles and public real estate market cycles cross G7 and adds to the academic real estate literature. Because public real estate market is a subset of stock market, our approach is to use an original stock market index, as well as a “pure” stock market index (with the influence of real estate market removed) to offer additional empirical insights from two key complementary perspectives.

Do Indian stock market sentiments impact contemporaneous returns?

2018 ◽  
Vol 7 (3) ◽  
pp. 332-346
Author(s):  
Divya Aggarwal ◽  
Pitabas Mohanty
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Temporal Dynamics ◽  
Stock Exchange ◽  
Principal Component ◽  
Stock Index ◽  
Content Type ◽  
Investor Sentiments ◽  
Indian Stock Market ◽  
The Impact

Purpose The purpose of this paper is to analyse the impact of Indian investor sentiments on contemporaneous stock returns of Bombay Stock Exchange, National Stock Exchange and various sectoral indices in India by developing a sentiment index. Design/methodology/approach The study uses principal component analysis to develop a sentiment index as a proxy for Indian stock market sentiments over a time frame from April 1996 to January 2017. It uses an exploratory approach to identify relevant proxies in building a sentiment index using indirect market measures and macro variables of Indian and US markets. Findings The study finds that there is a significant positive correlation between the sentiment index and stock index returns. Sectors which are more dependent on institutional fund flows show a significant impact of the change in sentiments on their respective sectoral indices. Research limitations/implications The study has used data at a monthly frequency. Analysing higher frequency data can explain short-term temporal dynamics between sentiments and returns better. Further studies can be done to explore whether sentiments can be used to predict stock returns. Practical implications The results imply that one can develop profitable trading strategies by investing in sectors like metals and capital goods, which are more susceptible to generate positive returns when the sentiment index is high. Originality/value The study supplements the existing literature on the impact of investor sentiments on contemporaneous stock returns in the context of a developing market. It identifies relevant proxies of investor sentiments for the Indian stock market.

Does macroeconomic uncertainty really matter in predicting stock market behavior? A comparative study on China and USA

2020 ◽  
Vol 10 (4) ◽  
pp. 393-427 ◽  
Author(s):  
Ghulam Abbas ◽  
Shouyang Wang
Keyword(s):  
Risk Factors ◽  
Financial Crisis ◽  
Stock Market ◽  
Global Financial Crisis ◽  
Content Type ◽  
Macroeconomic Variables ◽  
Macroeconomic Risk ◽  
Stock Market Return ◽  
Macroeconomic Uncertainty ◽  
The Impact

PurposeThe study aims to analyze the interaction between macroeconomic uncertainty and stock market return and volatility for China and USA and tries to draw some invaluable inferences for the investors, portfolio managers and policy analysts.Design/methodology/approachEmpirically the study uses GARCH family models to capture the time-varying volatility of stock market and macroeconomic risk factors by using monthly data ranging from 1995:M7 to 2018:M6. Then, these volatility series are further used in the multivariate VAR model to analyze the feedback interaction between stock market and macroeconomic risk factors for China and USA. The study also incorporates the impact of Asian financial crisis of 1997–1998 and the global financial crisis of 2007–2008 by using dummy variables in the GARCH model analysis.FindingsThe empirical results of GARCH models indicate volatility persistence in the stock markets and the macroeconomic variables of both countries. The study finds relatively weak and inconsistent unidirectional causality for China mainly running from the stock market to the macroeconomic variables; however, the volatility spillover transmission reciprocates when the impact of Asian financial crisis and Global financial crisis is incorporated. For USA, the contemporaneous relationship between stock market and macroeconomic risk factors is quite strong and bidirectional both at first and second moment level.Originality/valueThis study investigates the interaction between stock market and macroeconomic uncertainty for China and USA. The researchers believe that none of the prior studies has made such rigorous comparison of two of the big and diverse economies (China and USA) which are quite contrasting in terms of political, economic and social background. Therefore, this study also tries to test the presumed conception that macroeconomic uncertainty in China may have different impact on the stock market return and volatility than in USA.

scholarly journals Capital Asset Pricing Model, Theory and Practice: Evidence from USA (2009-2016)

2017 ◽  
Vol 12 (8) ◽  
pp. 182 ◽  
Author(s):  
Mohammad AbdelMohsen Al-Afeef
Keyword(s):  
Stock Market ◽  
Multiple Regression Model ◽  
Theory And Practice ◽  
Pricing Model ◽  
Stock Market Index ◽  
Market Return ◽  
Market Index ◽  
Stock Market Return ◽  
The Us ◽  
The Impact

This study discussed the Capital Assets Pricing model (CAPM) and its ability to measure the required return, the researcher tested this model on Amazon Company listed in S&P 500 during the period (2009-2016), to measure the impact of beta stock and market index return on the required return. Multiple regression model was used to test the effect of independent variables (Beta stock, Market Index Return) on the dependent variable (Required return), it should be noted that there is a statistically significant impact of the US stock market Return (S&P500) and Amazon stock Beta factor on Amazon stock required return, and the study model explanatory was 20% , this means that 20% of the changes in the required return are due to beta and market return, and 80% of the changes due to other factors, also find that CAPM can be applied on efficiency markets and huge companies.The researcher recommends applying the variables of the study on a group of large companies in the S&P 500 index, and looking for other factors that may affect the required return.

scholarly journals The nexus between stock market index and apartment and villa prices

2017 ◽  
Vol 10 (3) ◽  
pp. 450-467 ◽  
Author(s):  
Peter Öhman ◽  
Darush Yazdanfar
Keyword(s):  
Stock Market ◽  
Housing Prices ◽  
Consumer Price Index ◽  
Causal Link ◽  
Stock Market Index ◽  
Monthly Data ◽  
Content Type ◽  
Market Index ◽  
Long Run ◽  
Causality Tests

Purpose The purpose of this study is to investigate the Granger causal link between the stock market index and housing prices in terms of apartment and villa prices. Design/methodology/approach Monthly data from September 2005 to October 2013 on apartment prices, villa prices, the stock market index, mortgage rates and the consumer price index were used. Statistical methods were applied to explore the long-run co-integration and Granger causal link between the stock market index and apartment and villa prices in Sweden. Findings The results indicate that the stock market index and housing prices are co-integrated and that a long-run equilibrium relationship exists between them. According to the Granger causality tests, bidirectional relationships exist between the stock market index and apartment and villa prices, respectively, supporting the wealth and credit-price effects. Moreover, variations in apartment and villa prices are primarily caused by endogenous shocks. Originality/value To the authors’ best knowledge, this study represents a first analysis of the causal nexus between the stock market and the housing market in terms of apartment and villa prices in the Swedish context using a vector error-correction model to analyze monthly data.

scholarly journals Econometric Modelling: Testing of Randomness,Volatility, Casualty and Cointegration of Emerging Stock Market Indices of India and MIST Countries

2019 ◽  
Vol 9 (2) ◽  
pp. 1402-1417
Keyword(s):  
Financial Crisis ◽  
Stock Market ◽  
Time Series Data ◽  
Global Financial Crisis ◽  
Good News ◽  
Series Data ◽  
Stock Market Index ◽  
Causality Test ◽  
Lm Test ◽  
The Impact

In general, stock market indices are widely interrelated to the other global markets to detect the impact of diversification opportunities. The present research paper empirically examines randomness and long term equilibrium affiliation amongst the emerging stock market of India and Mexico, Indonesia, South Korea and Turkey from the monthly time series data during February 2008 to October 2019. The researcher employs by the way, Run test, Pearson’s correlation test, Johnsen’s multivariate cointegration test, VECM and Granger causality test with reference to post-September 2008 Global financial crisis. The test results of the above finds that Nifty 50 and BSE Sensex is significantly cointegrated either among themselves or with MIST countries particularly during the post-September Global financial crisis. No random walk is found during the study period. The ADF (Augmented DickeyFuller) and PP (Phillips Pearson) tests evidenced stationarity at the level, but converted into non-stationarity in first difference. Symmetric and asymmetric volatility behaviors are studied using GARCH, EGARCH and TARCH models in order to test which model has the best forecasting ability. Leverage effect was apparent during the study period. So the influx of bad news has a bigger shock or blow on the conditional variance than the influx of good news. The residual diagnostic test (Correlogram-Squared residuals test, ARCH LM test and Jarque-Bera test) confirms GARCH (1,1) as the best suited model for estimating volatility andforecasting stock market index.

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