Search results

The purpose of this paper is to examine the relation between gold return and stock market return and whether its relation changes in times of consecutive negative market returns for an emerging market, Malaysia.

The paper applies the autoregressive distributed model to link gold returns to stock returns with TGARCH/EGARCH error specification using daily data from August 1, 2001 to March 31, 2010, a total of 2,261 observations.

A significant positive but low correlation is found 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 is 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 stock market slumps. The findings should prove useful for designing financial investment portfolios.

The paper evaluates the role of gold from a domestic perspective, which should be more relevant to domestic investors in guarding against recurring heightened stock market risk.

The aim of this paper is to examine the relationship between weather (temperature) and stock market returns using daily data from Portugal; also, to examine whether the temperature is driven by calendar‐related anomalies such as the January and trading month effects.

Daily financial and weather data from Lisbon Stock Exchange (PSI 20 index) and Lisbon capital for the period 1995‐2007 are considered. The paper employs an AR(1)‐TGARCH(1,1) model under several distributional assumptions (Normal, Student's‐t and GED) for the errors.

Empirical results show that temperature affects negatively the PSI20 stock returns in Portugal. Moreover, temperature is dependent of both January and trading month effects. Stock returns were found to be positive in January and higher over the first fortnight of the month. Lower temperature in January leads to higher stock returns due to investors' aggressive risk taking.

Further research should investigate the impact of other meteorological variables (humidity, amount of sunshine) and other calendar anomalies on the course and behaviour of major international stock indices using data before and after the recent crisis.

The findings are helpful to financial managers, investors and traders dealing with the Portuguese stock market.

The contribution of this paper is to provide evidence on the empirical linkages between temperature and stock market returns using GARCH models. To better understand the relationship between the temperature and stock market returns, the paper also examines whether the returns are higher in winter (January effect) and during the first or second fortnight of the month (trading month effect). To the best of the author's knowledge, this is the first empirical investigation on weather and stock market returns relationship for Portugal.

The purpose of this paper is to investigate the spillover effect of the US macroeconomic news on the first two moments of the Vietnamese stock market returns.

The author collected market expectation and actual announcements data for 12 key US macroeconomic announcements for the period from August 2000 to September 2009 from Bloomberg. The dataset consists of monthly Non‐farm payroll (NFPM), Unemployment level (UNEMP), Gross Domestic Product percentage level (GDP), Housing statistics (HOMEST), Industrial production (INDP), Leading Indicator (LEAD), Retail Sales (SALES), Consumer Price Index (CPI), Producer Index (PPI), Current Account (CA, quarterly), Trade Balance (BOT), and the Federal Reserve's target rates (FOMC, 8 times a year and ad hoc meetings if needed). The MA‐EGARCH (1,1) model is used for the empirical test of the US macroeconomic news spillover effects on the VNI index.

In general, the US real economic news has the strongest effect on the first two moments of the Vietnamese stock returns. This can be interpreted as evidence that Vietnamese market participants believe that the USA is targeting real economic activities other than other variables. It is also shown that even though the US stock market (proxied by S&P500 index) significantly affects the Vietnamese stock market returns, the spillover effect of the US macroeconomic news is still significant.

The author does not explore further on the transmission channels of the spillover effects of the US news on the Vietnamese stock market, reserving this task for future research.

The paper contributes to the extant literature in several ways. First, to the author's knowledge, the current literature lacks empirical evidence for the impact of the US macroeconomic news on the first two moments of the Vietnamese stock markets. Given the growing integration between the two economies, evidenced by the fact that the USA is Vietnam's largest foreign direct investor and importer, the US macroeconomic news is very important, not only for Vietnamese policy makers but also for market participants. Furthermore, the choice of a small and open market with increasing exposure to the world economy and vulnerable to the US news (i.e. Vietnam) would help in reducing the problem of endogeneity bias in previous studies employing large economy pairs, as the US news might affect the Vietnamese stock market but not the reverse. Finally, previous studies tend to investigate the impact of macro news only on conditional returns. In this study, both conditional returns and the conditional variance of returns are modelled simultaneously in a time‐varying framework (MA‐GARCH) to better capture the impact of macroeconomic news on stock returns and stock market volatility.

This study aims to evaluate the structural oil shocks effect on stock returns of Indian renewable energy companies across market conditions.

This study applies the structural vector autoregression model to estimate sources of oil shocks such as oil supply shock, aggregate demand shock and oil price-specific demand shock. In the next step, the panel quantile regression model estimates the effect of these oil shocks on stock return across market conditions. Monthly data are collected from January 2009 to December 2019. All renewable energy companies listed on the National Stock Exchange of India are considered for the analysis.

In the whole sample analysis, this study finds that oil shocks negatively affect stock returns in most of the market conditions except oil price-specific demand shock. In sub-groups, oil shocks driven by supply and aggregate demand also negatively affect stock return in most market conditions. This study finds the positive interaction of oil price-specific demand shock. A majority of these positive interactions happen in bearish market conditions. In the whole sample, the asymmetric effects of shocks driven from oil supply and oil price-specific demand are seen in most quantiles or market conditions. At the same time, aggregate demand shock does not affect asymmetrically. In the sub-group analysis, standalone renewable energy companies stock returns are least asymmetrically affected by these oil shocks. The asymmetries of oil supply-driven shock on stock returns of the renewable energy sub-group companies are found in most quantiles.

First, this is a company-level study of the stock returns response to the structural oil shocks in the renewable energy sector. Second, to the best of the authors’ knowledge, this type of study is the first in the Indian context. Third using panel quantile regression model along with capital asset pricing model framework, the authors investigate these effects across market conditions.

This study aims to identify calendar anomalies that can affect stock returns and asymmetric volatility. Thus, the objective of this study is twofold: on the one hand, it examines the impact of calendar anomalies on the returns of both conventional and Islamic indices in Indonesia, and on the other hand, it analyzes the impact of these anomalies on return volatility and whether this impact differs between the two indices.

The authors apply the GJR-generalized autoregressive conditional heteroskedasticity model to daily data of the Jakarta Composite Index (JCI) and the Jakarta Islamic Index for the period ranging from October 6, 2000 to March 4, 2022.

The authors provide evidence that the turn-of-the-month (TOM) effect is present in both conventional and Islamic indices, whereas the January effect is present only for the conventional index and the Monday effect is present only for the Islamic index. The month of Ramadan exhibits a positive effect for the Islamic index and a negative effect for the conventional index. Conversely, the crisis effect seems to be the same for the two indices. Overall, the results suggest that the impact of market anomalies on returns and volatility differs significantly between conventional and Islamic indices.

This study provides useful information for understanding the characteristics of the Indonesian stock market and can help investors to make their choice between Islamic and conventional equities. Given the presence of some calendar anomalies in the Indonesia stock market, investors could obtain abnormal returns by optimizing an investment strategy based on seasonal return patterns. Regarding the day-of-the-week effect, it is found that Friday’s mean returns are the highest among the weekdays for both indices which implies that investors in the Indonesian stock market should trade more on Fridays. Similarly, the TOM effect is significantly positive for both indices, suggesting that for investors are called to concentrate their transactions from the last day of the month to the fourth day of the following month. The January effect is positive and statistically significant only for the conventional index (JCI) which implies that it is more beneficial for investors to invest only in conventional assets. In contrast, it seems that it is more advantageous for investors to invest only in Islamic assets during Ramadan. In addition, the findings reveal that the two indices exhibit lower returns and higher volatility, which implies that it is recommended for investors to find other assets that can serve as a safe refuge during turbulent periods. Overall, the existence of these calendar anomalies implies that policymakers are called to implement the required measures to increase market efficiency.

The existing literature on calendar anomalies is abundant, but it is mostly focused on conventional stocks and has not been sufficiently extended to address the presence of these anomalies in Shariah-compliant stocks. To the best of the authors’ knowledge, no study to date has examined the presence of calendar anomalies and asymmetric volatility in both Islamic and conventional stock indices in Indonesia.

This study seeks to examine the relationship between macroeconomic shock indicators, namely geopolitical risk (GPR), global economic policy uncertainty (GEPU) and financial stress (FS), and returns as well as volatilities on seven carefully selected stock markets in Africa. Specifically, the study intends to unravel the co-movement and interdependence between the respective macroeconomic shock indicators and each of the stock markets under consideration across time and frequency.

This study employed wavelet coherence approach to examine the strength and stability of the relationships across different time scales and frequency components, thereby providing valuable insights into specific periods and frequency ranges where the relationships are particularly pronounced.

The study found that GEPU, Financial Stress (FS) and GPR failed to induce significant influence on African stock market returns in the short term (0–4 months band), but tend to intensify in the long-term band (after 6th month). On the contrary, stock market volatilities exhibited strong coherence and interdependence with GEPU, FSI and GPR in the short-term band.

This study happens to be the first of its kind to comprehensively consider how the aforementioned macro-economic shock indicators impact stock markets returns and volatilities over time and frequency. Further, none of the earlier studies has attempted to examine the relationship between macro-economic shocks, stock returns and volatilities in different crisis periods. This study is the first of its kind in to employ data spanning from May 2007 to April 2023, thereby covering notable crisis periods such as global financial crisis (GFC) and the COVID-19 pandemic episodes.

Despite its devastating nature, the COVID-19 pandemic has also catalyzed a substantial surge in the adoption and integration of technological tools within economies, exerting a profound influence on the dissemination of information among participants in stock markets. Consequently, this present study delves into the ramifications of post-pandemic dynamics on stock market behavior. It also examines the relationship between investors' sentiments, underlying behavioral drivers and their collective impact on global stock markets.

Drawing upon data spanning from 2012 to 2023 and encompassing major world indices classified by Morgan Stanley Capital International’s (MSCI) market and regional taxonomy, this study employs a threshold regression model. This model effectively distinguishes the thresholds within these influential factors. To evaluate the statistical significance of variances across these thresholds, a Wald coefficient analysis was applied.

The empirical results highlighted the substantive role that investors' sentiments and behavioral determinants play in shaping the predictability of returns on a global scale. However, their influence on developed economies and the continents of America appears comparatively lower compared with the Asia–Pacific markets. Similarly, the regions characterized by a more pronounced influence of behavioral factors seem to reduce their reliance on these factors in the post-pandemic landscape and vice versa. Interestingly, the post COVID-19 technological advancements also appear to exert a lesser impact on developed nations.

This study pioneers the investigation of these contextual dissimilarities, thereby charting new avenues for subsequent research studies. These insights shed valuable light on the contextualized nexus between technology, societal dynamics, behavioral biases and their collective impact on stock markets. Furthermore, the study's revelations offer a unique vantage point for addressing market inefficiencies by pinpointing the pivotal factors driving such behavioral patterns.

This paper investigates the generalized Fisher hypothesis for nine equity markets in the Asian countries. It states that the real rates of return on common stocks and the expected inflation rate are independent and that nominal stock returns vary in a one‐to‐one correspondence with the expected inflation rate. The regression results indicate that stock returns in general are negatively correlated to both expected and unexpected inflation, and that common stocks provide a poor hedge against inflation. However, the results of the VAR model indicate the lack of a unidirectional causality between stock returns and inflation. It also fails to find a consistent negative response neither of inflation to shocks in stock returns nor of stock returns to shocks in inflation in all countries. It appears that the generalized Fisher hypothesis in the Asian markets is as puzzling as in the developed markets.

This study presents evidence on the relations of stock market performance and industrial production growth for a group of 20 industrial markets. Evidence supports the notion that an increase in stock returns or a rise in the market value of stocks contributes positively to industrial production growth. Evidence suggests that stock market risk has a significantly negative effect on production growth for advanced markets. The Granger test finds a unidirectional causality running from stock returns or stock volatility to industrial growth. However, the United States shows a bilateral causality between stock volatility and industrial production growth.

This paper explores the relationship between size, book-to-market, beta, and expected stock returns in the U.S. Information Technology sector over the July 1990–June 2001 period. Two models, the multivariate model and the three-factor model, are employed to test these relationships. The risk-return tests confirm the relationship between size, book-to-market, beta and stock returns in IT stocks is different from that in other non-financial stocks. However, the sub-period results (the periods before and after the technology crash in April 2000) show that the nature of the relationship between stock returns, size, book-to-market, and market factors, or the magnitude of the size, book-to-market, and market premiums, is on average unchanged for both sub-periods. This result suggests the technology stock crash in April 2000 was not a correction of stock prices.