Volatility of stockmarketreturn is of great concern to providers of funds as it increases the uncertainty of their future wealth. Many studies have address of environmental disclosure and the Nigerian stockmarket but few considered the effect of environmental disclosure on the volatility in the stockmarket. This study investigated the combined effects of environmental pollution and control policy (EPC), energy policy (ENP), impact of biodiversity (IB), waste management cost (WMC), environmental research and development cost (ERD), cost of compliance to environmental laws (CCEL), firm size (FSZ), and firm age (FA) on stockmarketreturn volatility. The study adopted ex post facto research design. The target population for the study comprised 48 companies quoted on the Nigerian Stock Exchange, under the consumer goods and industrial goods sectors, as at December 31, 2016. A sample size of 17 companies was determined using Cochran’s formula. Stratified proportionate sampling was adopted to select the number of companies studied from each stratum and samples from each stratum were purposively selected based on companies with higher total asset as at December 31, 2016. The results revealed that EPC, ENP, IB, WMC, ERD, CCEL, FSZ and FAG jointly had significant effect on StockMarketReturn Volatility (SMRV). The individually ENP, IB, FSZ and FAG had significant negative effect on SMRV (β = -.053, -.131, -.026, -.006; t (253) = -2.28, -5.00, -5.82, -8.67; p < .05); while WMC had significant positive effect on SMRV (β = .113,
Even though most previous studies focus on mature markets, this study provides an example of the predictive power of stockreturn on real activity in an Asian emerging market by evaluating the notion that stockreturn contains information relating to real economic activity. In other words, the predictive content of stockreturn on industrial output growth is examined. In the first part of the analysis, the standard Granger causality test using the in-sample data provides the evidence that supports the notion that stockmarketreturn is a predictor of industrial output growth during the period of investigation. In the second part, the benchmark model and the model augmented with stockmarketreturn variables are compared. The two models are nested. Using the test of equal forecasting ability for these two nested models, it is found that the model augmented with stockreturn variable outperforms the benchmark model. The results confirm the predictive role of stockmarketreturn in a short horizon of three months or a quarter. The evidence can offer a useful insight to investors, portfolio managers and policymakers on the role of stockmarket in forecasting real economic activity. An increase in stockmarketreturn is a signal for an increase in real activity in the next three months. On the contrary, a decline in stockmarketreturn will signal a fall in real activity or industrial output growth in the same manner.
The paper investigates the Santa Claus Rally on the Nigerian stockmarketreturn. The study employed GARCH (1,1) model to analyse the monthly return from January 1985 to December 2016. The empirical findings do not justify the claim of the Santa Claus Rally instead January effect on the Nigerian stockmarketreturn. Also, the study observed that the assumptions of Intergeneration and Tax-loss selling Hypotheses, and Window Dressing Effect hold in the Nigerian StockMarket. Thus, based on the findings, the study concluded that Santa Claus rally is nothing but a misleading impression of reality. In view of this, the study recommends that investors are advised to sell their stock in January since it exhibits highest return among the months of the year. The implication of this is that the investors can formulate their investment strategies and timing on the basis of this result and can earn some abnormal return by predicting future prices. The main contribution of the study is to identify the behaviour of monthly return and provide fresh evidence of Santa Claus Rally on the Nigeria stockmarketreturn.
319 economies to investigate correlation between stock returns and output growth and finds that the proportion of countries with the correlation between output growth and lagged stock returns is significantly positive when annual data are used. However, the proportion is lower when quarterly data are used. In addition, the results are almost the same for advanced and emerging market economies. Employing monthly industrial production indexes of EU countries covering the period from January 1988 to May 2005 to construct out-of-sample forecasts and evaluation, Panopoulo (2007) finds that stockmarketreturn is one of financial variables that provide most accurate forecasts for output growth. Tsouma (2009) examines the dynamic interdependence between stock returns and economic activity in emerging and advanced markets using monthly data and discovers the existence of relationship between stock returns and real activity. However, the forecasting ability running from stock returns to economic activity is confirmed for a small number of emerging markets, but for a large number of mature markets. Ibrahim (2010) finds that the predictive role of stock returns for real activity at short horizon is found in case of Malaysia. Kuosmanen and Vataja (2011) investigate the forecasting content of stock returns and volatility, and the term spread for GDP, private consumption, industrial production and the inflation rate in Finland. Their results suggest that during normal times, the term spread is a much better tool than stockmarket variables for predicting real activity. However, during the financial crisis, the forecast performance is improved by combining the term spread and the stockmarket information.
The current study was carried out with the objective of studying and evaluating the dynamic impact of interest rate on the stock returns of the Tehran stock exchange. Based on the literature review, two hypotheses were pointed out, and in order to test these hypotheses, the data from the Iran Central Bank were used after theoretical investigations. The collected data were investigated annually for 16 years from 2002 through 2016. The collected data were estimated using the GARCH model. The analyses were performed in Excel and Eviews, and the results were obtained to be as follows. The results imply that the changes in this variable in the studied period was initially low at the beginning of the period and increased gradually in a generally oscillatory manner and increases in 2013.Observing the changes in this variable in the studied period shows that this variable is oscillatory in a sinusoidal form. The maximum changes drop, and the maximum changes were observed in 2008 and 2013, respectively. Also, the changes to this variable in the studied period were smoothly increasing
Various studies done in Kenya have yielded varying results. Ouma and Muriu (2014) and Kirui (2014) using OLS found insignificant relationship between interest rate and stockreturn while Gatebi (2013) and Olweny and Omondi (2014) concluded a negative relationship. Olweny and Omondi (2014), Ouma and Muriu (2014) found inflation to be significant while Kirui (2014) found it to be insignificant. Olweny and Omondi (2014), Sakwa (2008) concluded a positive relationship between exchange rate and the stock returns while Kirui (2014) found exchange rate to be insignificant. However Ouma and Muriu (2014) realized a negative relationship between the returns and the exchange rate. The varying results are attributable to differences in macroeconomic variables used, research methodology applied and the period covered. In addition the reviewed studies have not clearly shown the nature of causal relationship between the variables and stock returns in Kenya. The current study introduced oil prices and government spending due to their economic importance and further investigated their effect on stockreturn volatility.
Z-statistic for joint tests 0.664 (p-value=0.941) 0.821 (p-value=0.880) By computing variances from Eq. (2) and Eq. (3) with k=2, 4, 8, and 12, the variance ratios seem to be close to one. This implies that both series of stockmarket index follow a random walk process. 5 Using variance-ratio test can cause a crucial problem because stockmarketreturn does not follow a normal distribution. Cajueiro and Tabak (2006) employ an alternative to traditional variance-ratio test on stockmarket returns, i.e. bootstrapped variance-ratio test. However, the results are similar to those of traditional one. Furthermore, the data in their study show heteroskedasticity. Therefore, the GARCH process seems to be a better measure of stockreturn preditability.
The two-step approach is employed to explain the relationship between oil price volatility and the Thai stockmarket. In the first step, a bivariate generalized autoregressive heteroskedastic model with constant conditional correlation (ccc-GARCH model proposed by Bollerslev (1990) is employed to generate stock and oil price volatilities. In the second step, these generated series along with real stockmarketreturn and the rate of change in oil price series employed in the standard Granger (1969) causality test. Pagan (1984) criticizes this procedure because it produces the generated series of volatility or uncertainty. When these generated series are used as regressors in Granger causality test, the model might be misspecified. However, the full information maximum likelihood method that simultaneously tests the impact of volatility in the mean equation can give the same results (see Oteng- Abayie and Doe, 2013). 3 Furthermore, the main advantage of the two-step procedure is that it provides room for the ability to establish causality between variables. The system equations in a ccc-GARCH(1,1) model comprises the following five equations.
determinants of sectoral stock returns in the Indonesian capital market. Investors still consider the stockmarket and macroeconomic variables can affect cash flow, discount rate and the investors’ interest in order to invest the stock in Indonesian capital market. Based on this research, the stockmarketreturn positively affects the stock returns of finance sector and trade and services sector both in bullish and bearish conditions. The results of this study are in line with the theory developed by Sharpe (1964) and Litner (1965), which implies a positive linear relationship between the stockmarketreturn or market risk and stock returns. The results of this study also supports the findings of Butt, et al (2010) which has been influenced by positive stockmarketreturn to the examined sectoral stocks return. The positive influence from the stockmarketreturn on sectoral stockreturn either in bullish or bearish condition reflects a favorable investment climate in the stockmarket, so investors will respond positively to the capital market. Thus, investors will tend to perform buying and selling action of the traded stocks based on stockmarket index occurred. These results also confirm that the stockmarketreturn factor is the main factor in determining the sensitivity of stock returns of finance sector and trade and services sector, in the Indonesian capital market. Based on this study, the effect of the interest rate varies with the stockreturn of finance sector and trade and service sector, in bullish and bearish condition. On bullish conditions, the effect of interest rates is not significant to stockreturn of finance sector and trade and service sector. This result consistent with the research conducted by Butt et al. (2010) who found that the effect
Abstract. This paper proposes a three variables’ double threshold-GRACH model, and uses this model to discuss Japan, U.S. and U.K. stockreturn rate volatility on the influence of the Singapore stockmarket. The empirical result demonstrates that the proposed model discussing Japan, U.S. and U.K. stockreturn rate volatilities to the influence of the Singapore stockmarketreturn is indeed appropriate, and also the response to the Singapore stockmarket has an asymmetrical effect. The empirical result also shows the different influence of the good news and the bad news of the eight kinds of combinations of the proposed model. The information of Japan, U.S. and U.K. stockreturn rate volatilities affects the Singapore stockmarket returns’ volatility. Besides, Japan, U.S. and U.K. stockreturn volatilities are truly affects the variation risks of the stockmarket.
This aim of this study is to examine the impact of oil prices on stockmarket performance, particularly in top oil importing countries. We used a balanced panel data over a sam- ple period from 1995 to 2017. This study also employed exchange rate, GDP and infla- tion rate as controlling variables. The results of the study suggest that the impact of oil prices is insignificant on stockmarketreturn. More precisely, fluctuations or change in oil prices doesn’t lead towards the fluctuation in the stockmarketreturn. Maghyereh and Al-Kandari (2007) revealed that the shocks in oil prices do not reveal any empiri- cal evidence on stock performance. Even though the relationship is insignificant but the FEM shows that the direction of the relationship is negative. It is opposite to the study of Adetunji Babatunde et al. (2013) who analyzed the influence of oil prices on stockmarket and proposed that oil prices are positively connected with stockmarketreturn either the country is oil importer or exporter. Furthermore, Raza, Shahbaz, Amir-ud Din, Sbia, and Shah (2018); Boubaker and Raza (2017) noted the influence of oil prices on stock perfor- mance. Hence, our findings show partial consistent results with Kumar and Maheswaran (2013) study, which shows the impact of oil prices on stockmarket.
A few papers have attempted to examine volatility on the Egyptian Exchange. Among them, Mecagni and Sourial (1999) investigated the behavior of stock returns on the Egyptian Exchange including the relationship between returns and conditional volatility using GARCH -M. The results indicated the tendency for returns to exhibit volatility clustering and a significant positive link between risk and returns. Omran and Girard (2007) studied the relationship between trading volume and stock price volatility in Cairo and Alexandria Stock Exchange (CASE). The study provided empirical support for the TGARCH specification for explaining the daily time dependence in the rate of information arrival to the market for stocks traded on the CASE. Floros (2008) examined the use of GARCH-type models for modeling volatility and explaining financial market risk using daily data from Egypt (CMA General index) and Israel (TASE-100 index). Models used include GARCH, EGARCH, threshold GARCH, asymmetric component GARCH, the component GARCH and the power GARCH model. His results provide strong evidence that daily returns can be characterized by the GARCH models. Further, Abd El Aal (2011) examined Egyptian stockmarketreturn volatility from 1998 to 2009 and his findings show that EGARCH is the best model among other models for measuring volatility.
For stock price return, the GARCH-mean (1, 1) is best Results of Seasonal Effect Models: The seasonality in risk fitted model on the basis of AIC criteria. The standard and return series is captured by introducing the time coefficient of SQR (GARCH) is significant that can be dummies in men and variance part of univariate modeling explained as if there is effect of risk on the mean return it series .The dummies represent the seasonal effect in most is may be better mentioned by both the mean part of and appropriate GARCH-Mean framework in different series. variance part in case of stock price return. The GARCH The value of mean coefficient is positive for all series value coefficient represent that stockmarketreturn series indicating the positive relationship between risk and is long mammary data .The Results of stock price returnreturn of each series. The stock models also represent a are given below in Table 6. positive relationship between market volatility and its E-GARCH Model Specification Results: The asymmetric indicates that the seasonal effect is present in series. The effect volatility of different series is captured through the results of seasonal effect of different series are given estimation of E-GARCH model .The asymmetric and below
Mansor, Ibrahim H. (2011) in the study “Financial Market Risk and Gold Investment in an Emerging Market: The Case of Malaysia” examined the relation between gold return and stockmarketreturn and whether its relation changes in times of consecutive negative market returns for an emerging market, Malaysia. The study revealed a significant positive but low correlation between gold and once-lagged stock returns. Moreover, consecutive negative market returns do not seem to intensify the co-movement between the gold and stock markets as normally documented among national stock markets in times of financial turbulences. Indeed, there was some evidence that the gold market surges when faced with consecutive market declines. Based on these results, there are potential benefits of gold investment during periods of stockmarket slumps.
In order to examine whether the internal control quality affects the M&A performance (stockmarketreturn and ac- counting return) of acquiring firms after the Chinese SOX (Sarbanes-Oxley Act) issued, we select 126 mergers and ac- quisitions (M&A) deals of Chinese non-financial listed companies on Shanghai and Shenzhen Stock Exchange in 2010 from the CSMAR (China Securities Market & Accounting Research) Database. And we use content analysis to con- struct a score to quantify the internal control quality of Chinese listed companies. We examine the relationship between the quality of internal controls and the performance of bidding firms with event study and multivariate regression analysis. Our results indicate that the internal control quality of acquirers is positively related to the accounting rates of firms. The higher quality of internal control means that acquirers with better internal control system would be more likely to benefit from M&A activities and create more value for the their own companies and shareholders. Another result is that investors on the stockmarket are more optimistic about M&A activity lauched by firms which enjoy higher ICQS (the Internal Control Quality Score).
The political aspect of central bank independence does not appear to exert a strong influence on stock returns. Other aspects should be considered carefully and taken into account.  showed that leftist governments had somewhat lower interest rates than right-wing govern- ments when central bank independence is low.  also showed that high central bank independence may require a high level of conservatism. There is much room for discussion.
other words, if expected risk premiums are positively related to predictable volatility, then a positive unex- pected change in volatility increases future expected risk premiums and lowers current stock prices.  also points to a positive relationship between risk and return for USA monthly and daily returns over the period 1926-1988. [17,18] in various assets of USA market find a negative relationship before 1990.  reports no sig- nificant relationship in USA stockmarket at the same period. Also,  suggests that the relationship between risk and return may be time varying. These conflicting empirical results in the literature warrant further exami- nation using different and probably more appropriate econometric techniques.
An important topic in asset valuation research is the tradeoff between volatility and return (typically log price changes). The theoretical asset pricing models (e.g., Sharpe, 1964; Linter, 1965; Mossin, 1966; Merton, 1973, 1980) link returns of an asset to its own variance or to the covariance between the returns of stock and market portfolio. However, whether such a relationship is positive or negative has been controversial. As summarized in Baillie and DeGennarro (1990), most asset-pricing models (e.g., Sharpe, 1964; Linter, 1965; Mossin, 1966; Merton, 1973) suggest a positive tradeoff for expected returns and volatility. On the other hand, there are many empirical studies that confirm a negative relationship between returns and volatility: Black (1976), Cox and Ross (1976), Bekaert and Wu (2000), Whitelaw (2000). Pindyck (1984) indicates that there is a strong positive relationship between risk and excess return while Shiller (1981), Porteba and Summers (1986) as well as Bekaert and Wu (2000, pp. 1) found negative relationship in a variety of USA market indices. French, Schwert and Stambaugh (1987) using data from the S&P index, find a negative relationship between unexpected volatility and excess returns. Similar are the findings of Shawky and Marathe (1995) which use a two regime model. French et al. (1987) argue that the observed negative relationship provides indirect evidence of a positive relationship between expected risk premium and ex-ante volatility. In other words, if expected risk premiums are positively related to predictable volatility, then a positive unexpected change in volatility increases future expected risk premiums and lowers current stock prices. Campbell and Hentschel (1992) also point to a positive relationship between risk and return for USA monthly and daily returns over the period 1926-1988. Fama and Schwert (1977) and Nelson (1991) in various assets of USA market find a negative relationship before 1990. Chan, Karolyi and Stulz (1992) report no significant relationship in USA stockmarket at the same period. Also, Harvey (1989) suggests that the relationship between risk and return may be time varying. These conflicting empirical results in the literature warrant further examination using different and probably more appropriate econometric techniques.
The data set consists of the monthly excess return on the value-weighted CRSP index (in excess of the 3-month Treasury bill rate) from 1928:1 to 2004:12. 1 The same data from the period 1928:1 – 2000:12 were used by Lanne and Saikkonen (2006). In addition to the entire sample, models are estimated for the 1928:1 – 1966:6 and 1966:7 – 2004:12 subsample periods of equal length. This serves as a check of robustness and parameter constancy. Lanne and Saikkonen (2006) found the estimate of the slope coefficient 1 to be relatively stable once the intercept term 0 is restricted to
According to BAPEPAM-LK No. X.K.I, share buyback must be disclosed to the public, because it is a form of corporate action. Corporate action is an activity conducted by issuer who will affect numbers of outstanding stocks as well as the stock price . The announcement of share buyback will send positive signal to the market that the company has good profitability and business prospect, creating higher demand from investors which result in increasing stock price.