Search results

This study aims to explore the risk spillover effects among different sectors of the Chinese stock market after the outbreak of COVID-19 from both Internet sentiment and price fluctuations.

The authors develop four indicators used for risk contagion analysis, including Internet investors and news sentiments constructed by the FinBERT model, together with realized and jump volatilities yielded by high-frequency data. The authors also apply the time-varying parameter vector autoregressive (TVP-VAR) model-based and the tail-based connectedness framework to investigate the interdependence of tail risk during catastrophic events.

The empirical analysis provides meaningful results related to the COVID-19 pandemic, stock market conditions and tail behavior. The results show that after the outbreak of COVID-19, the connectivity between risk spillovers in China's stock market has grown, indicating the increased instability of the connected system and enhanced connectivity in the tail. The changes in network structure during COVID-19 pandemic are not only reflected by the increased spillover connectivity but also by the closer relationships between some industries. The authors also found that major public events could significantly impact total connectedness. In addition, spillovers and network structures vary with market conditions and tend to exhibit a highly connected network structure during extreme market status.

The results confirm the connectivity between sentiments and volatilities spillovers in China's stock market, especially in the tails. The conclusion further expands the practical application and theoretical framework of behavioral finance and also lays a theoretical basis for investors to focus on the practical application of volatility prediction and risk management across stock sectors.

Financial market holds superior information that can give insights into the future trajectory of economic growth. Further, identifying sectors that hold the key to future economic growth is important for all economies, but particularly relevant to emerging markets. However, unlike existing studies, the paper provides new insights into the forward-oriented nexus between financial markets and economic growth.

This paper takes a forward-looking approach of using financial market information to predict future economic growth. The authors use ARDL modeling approach to predict economic growth using the information from stock market sectoral returns.

The authors find that sectoral stock returns significantly improve economic growth forecasts. However, the forecasting superiority is not uniform across sectors and horizons. Auto, consumers' spending, materials and realty sectors provide the most forecasting gains. In contrast, banking, capital goods and industrial sectors provide superior forecasts, but only at horizons beyond one year. The authors also find that the forecast superiority of sectors at longer horizons is inversely related to volatility.

Research highlights the need for sector-focused policy actions in driving economic growth. Further, the findings of the paper identify sectors that drive short-, medium- and long-term economic growth.

There is a significant heterogeneity among different sectors and across horizons in predicting economic growth. Results suggest that targeted policy actions in sectors like materials, metals, oil and gas, and realty are key in driving economic growth. Further, policies geared toward the grassroots industries are at least as beneficial as the large-scale industries. Evidence also suggests the need for an active fiscal policy to address infrastructural bottlenecks in primary industries like basic materials and energy. Evidence nevertheless does not undermine the role of monetary policy actions.

Unlike any paper till date, the innovation of the paper is that it takes an ARDL modeling approach to measure stock market sectoral returns' ability to forecast economic growth several months ahead in the future. Though the paper considers the Indian case, the innovation and contribution extents to the entire field of economic studies.

The purpose of this paper is to explore the dynamic interdependence structure and risk spillover effect between the Chinese stock market and the US stock market.

This paper mainly uses the multivariate R-vine copula-complex network analysis and the multivariate R-vine copula-CoVaR model and selects stock price indices and their subsector indices as samples.

The empirical results indicate that the Energy, Materials and Financials sectors have leading roles in the interdependent structure of the Chinese and US stock markets, while the Utilities and Real Estate sectors have the least important positions. The comprehensive influence of the Chinese stock market is similar to that of the US stock market but with smaller differences in the influence of different sectors of the US stock market on the overall interdependent structure system. Over time, the interdependent structure of both stock markets changed; the sector status gradually equalized; the contribution of the same sector in different countries to the interdependent structure converged; and the degree of interaction between the two stock markets was positively correlated with the degree of market volatility.

This paper employs the methods of nonlinear cointegration and the R-vine copula function to explore the interactive relationship and risk spillover effect between the Chinese stock market and the US stock market. This paper proposes the R-vine copula-complex network analysis method to creatively construct the interdependent network structure of the two stock markets. This paper combines the generalized CoVaR method with the R-vine copula function, introduces the stock market decline and rise risk and further discusses the risk spillover effect between the two stock markets.

The main objective of this paper is to investigate whether the investors' behavior under optimistic (pessimistic) conditions has an impact on risk transmission between the Chinese stock and bond markets and the sector indices mainly during the COVID-19 pandemic.

This study uses a new measure of the investor's sentiment based on Google trend to construct a Chinese investor's sentiment index and a quantile causal approach to examine the causal relationship between googling investor's sentiment and the Chinese stock and bond markets as well as the sector indices. On the other hand, the network connectedness is used to estimate the spillover effect on the investor's sentiment and index returns. To check the robustness of the study results, the authors employed the Chinese VIX, as another measure of the investor's sentiment using daily data from May 2019 to December 2020.

In fact, the authors found a dual causality between the investor's sentiment and the financial market indices in optimistic or pessimistic situations, which indicates that positive and negative financial market returns may have an effect on the Chinese investor's sentiment. In addition, the results indicated that a pessimistic investor's sentiment has a negative impact on the banking, healthcare and utility sectors. In fact, the study results provide a significant peak of connectivity between the investor's sentiment, the stock market and the sector indices during the 2015–2016 and 2019–2020 turmoil periods that coincide respectively with the 2015 recession of the Chinese economy and the COVID-19 pandemic.

This finding suggests that the Chinese googling investor's sentiment is considered as a prominent channel of shock spillovers during the coronavirus crisis, which confirms the behavioral contagion. This study also identifies the contribution of a particular interest for portfolio managers and investors, which helps them to accordingly design their portfolio strategy.

The purpose of this paper is to investigate investor herding behaviour and the effect of presidential elections on investor herding behaviour in African stock markets at the sector level.

The study segregates listed firms into financial, consumer goods, consumer services and basic materials sectors and uses the cross-sectional absolute deviation approach as a metric of detecting herding in each of the sectors. The authors extend the model to tease out the effect of presidential elections on investor herding behaviour.

The study reveals that sectoral differences are fundamental to the evolution of herding. Herding is prominent in a financial services sector dominated by banks. The phenomenon also prevails in markets with smaller consumer goods and services sectors. A post-presidential election effect on investor herding is found for the consumer goods and services sectors of Ghana and a pre-presidential election effect is documented in Nigeria's consumer services sector. The authors conclude that post-presidential election effect is as a result of political connections whilst a pre-presidential election effect is attributable to political business cycles.

The study is based on four African countries due to data constraints. Nonetheless, the study is the first in Africa to the best of the authors' knowledge, and the results are very solid and have a lot of practical and policy implications.

The study has implications for investors as it guides investment behaviour in pre- and post-presidential election periods.

Past studies on investor herding behaviour in African stock markets have largely concentrated on the aggregate market. Knowledge on sectoral differences in investor herding is almost non-existent for African stock markets. Furthermore, premised on the fact that stock markets react to presidential elections, there is no known study that have attempted to examine the effect of presidential elections on investor herding behaviour. This paper contributes to the literature by providing evidence on sectoral differences in investor herding behaviour and the effect of presidential elections on sectoral herding behaviour.

This paper investigates herding behavior and its persistence among foreign institutional investors (FIIs) in the individual stocks of the energy sector of Indian stock exchange by focusing on post turmoil period. The study also examines the relation of herding with the individual return, market return, trading volume and conditional volatility of individual and market return.

The presence of herding is investigated by Lakonishok et al. (1992) model, value-based and count-based herd ratio measure among FIIs in individual stock of energy sector post turmoil period. Further, run test was employed to check the persistency in herding and multivariate distributed lag to investigate the relationship with the market determinant.

The result indicates the existence of herding in most of the companies and strong persistence in all the companies. The intensity of buy side herding is higher than sell side. Herding and individual return both are significant driving forces of FIIs herding, while trading volume and market volatility in few companies exhibit inverse relationship.

This study is limited to investigation of energy sector stock.

Stock market is significantly influenced by FIIs and their propensity to herd may generate instability in the stock market. Therefore, regulatory authority should continuously monitor the flow of fund by FIIs.

Herding in the individual stock of the energy sector was not previously performed.

This paper examines the extreme dependence and asymmetric risk spillovers between crude oil futures and ten US stock sector indices (consumer discretionary, consumer staples, energy, financials, health care, industrials, information technology, materials, telecommunication and utilities) before and during COVID-19 outbreak. This study is based on the rationale that stock sectors exhibit heterogeneity in their response to oil prices depending on whether they are classified as oil-intensive or non-oil-intensive sectors and the possible time variation in the dependence and risk spillover effects.

The authors employ static and dynamic symmetric and asymmetric copula models as well as Conditional Value at Risk (VaR) (CoVaR). Finally, they use robustness tests to validate their results.

Before the COVID-19 pandemic, crude oil returns showed an asymmetric tail dependence with all stock sector returns, except health care and industrials (materials), where an average (symmetric tail) dependence is identified. During the COVID-19 pandemic, crude oil returns exhibit a lower tail dependency with the returns of all stock sectors, except financials and consumer discretionary. Furthermore, there is evidence of downside and upside risk asymmetric spillovers from crude oil to stock sectors and vice versa. Finally, the risk spillovers from stock sectors to crude oil are higher than those from crude oil to stock sectors, and they significantly increase during the pandemic.

There is heterogeneity in the linkages and the asymmetric bidirectional systemic risk between crude oil and US economic sectors during bearish and bullish market conditions; this study is the first to investigate the average and extreme tail dependence and asymmetric spillovers between crude oil and US stock sectors.

Lowering of investment barriers between European nations has led to increasing integration of their capital markets. Consequently, global investors may be well‐advised to evaluate European stocks, not on the basis of the country of listing, but on the basis of the transnational industrial sector to which the stocks belong. This study utilizes performance measures, grounded in modern portfolio theory, to assess the risk‐adjusted return that has accrued to major transnational industrial sectors in Europe, such as consumer products, technology, utilities and financial services. The empirical documentation generated here can be used by international investors as input in decision making for sectorial allocation of funds in the European component of their global stock portfolios.

COVID-19 has had an immense impact on global stock markets, with no sector escaping its effects. Investor attention towards COVID-19 surged as the virus spread, the number of cases grew and its consequences imposed on everyday life. We assess whether this increase in investor attention may explain stock returns across different sectors during this unusual period.

We adopt the methodology of Da et al. (2015), using Google search volume (GSV) as a proxy for investor attention to examine the relationship between investor attention and stock returns across 11 sectors.

Our results demonstrate that heightened attention towards COVID-19 negatively influences US stock returns. However, relatively speaking, some sectors appear to have gained from the increased attention. This outperformance is centred in the sectors most likely to benefit (or likely to lose least) from the crisis and associated spending by households and government (i.e. consumer staples, healthcare and IT). Such results may be explained by an information discovery hypothesis in the sense that investors are searching online for information to enable a greater understanding of COVID-19's impact on relative stock sector performance.

While we do not claim that investor attention is the only driver of stock returns during this unique period, we do provide evidence that it contributes to the market impact and to the heterogeneity of returns across stock market sectors.

The purpose of this paper is to analyze volatility transmission between the Japanese stock and foreign exchange markets.

In contrast to the existing literature, industry‐level stock data are applied to a trivariate Baba, Engle, Kraft and Kroner‐generalised autoregressive conditional heteroscedasticity (BEKK‐GARCH) model that also includes comparable US industrial stocks returns as a control variable.

Using daily data over the study period 1994‐2007, it was found that news shocks in the Japanese currency market account for volatility transmission in eight of the ten industrial sectors considered. Evidence was also found of significant asymmetric effects in five of these industries.

While the BEKK‐GARCH model enables analysis of volatility transmission between the stock and foreign markets against a background of conditional correlation and asymmetries, the model requires the estimation of a large number of parameters, which can be problematic for a limited dataset.

The paper's findings have important implications for understanding international volatility transmission involving the stock and foreign exchange markets. This in turn can provide insight into investor behaviour.