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The prediction of stock market (SM) indices is a fascinating task. An in-depth analysis in this field can provide valuable information to investors, traders and policy makers in attractive SMs. This article aims to apply a correlation feature selection model to identify important technical indicators (TIs), which are combined with multiple deep learning (DL) algorithms for forecasting SM indices.

The methodology involves using a correlation feature selection model to select the most relevant features. These features are then used to predict the fluctuations of six markets using various DL algorithms, and the results are compared with predictions made using all features by using a range of performance measures.

The experimental results show that the combination of TIs selected through correlation and Artificial Neural Network (ANN) provides good results in the MADEX market. The combination of selected indicators and Convolutional Neural Network (CNN) in the NASDAQ 100 market outperforms all other combinations of variables and models. In other markets, the combination of all variables with ANN provides the best results.

This article makes several significant contributions, including the use of a correlation feature selection model to select pertinent variables, comparison between multiple DL algorithms (ANN, CNN and Long-Short-Term Memory (LSTM)), combining selected variables with algorithms to improve predictions, evaluation of the suggested model on six datasets (MASI, MADEX, FTSE 100, SP500, NASDAQ 100 and EGX 30) and application of various performance measures (Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error(RMSE), Mean Squared Logarithmic Error (MSLE) and Root Mean Squared Logarithmic Error (RMSLE)).

This paper aims to investigate and compare the sustainability reporting practices of companies in the two most successful Western economies, the USA and the UK, as per Global reporting initiative framework.

Content analysis has been applied on a sample of 136 companies listed on the Stock Exchanges of the USA and the UK (USA – NASDAQ 100, 100 companies and Amex major market index, 20 companies; UK – FTSE 100, 100 companies). It uses descriptive statistics and independent sample t-test to identify significant comparisons.

The findings of the study suggest that the level of sustainability reporting is almost similar in the USA and the UK. It is somewhat low in both the countries. Overall mean disclosure score is 39.1 per cent in case of the USA followed by UK with 34.5 per cent. The result of independent sample t-test shows that these differences are not significant.

Sustenance is not a grave issue in both the USA and the UK. Thus, sustainability reporting is a voluntary practice in both these countries. Even then these countries are fostering in the field of sustenance and sensitizing the developing nations towards its need and relevance. The present study would provide developing countries a base and understanding of need based rules for moving on the path of sustenance.

The USA and the UK are the two most successful Western economies. However, not even a single study was found while reviewing the literature that studied and compared the sustainability reporting practices of these two leading developed countries.

In this chapter, we describe the rise and fall of Germany’s Neuer Markt from its promising start to its ultimate failure. We show that the Neuer Markt was designed to serve the special needs of small and medium sized growth firms. However, some regulatory flaws, insufficient means to enforce the rules, the IPO frenzy and the bursting of the stock market bubble destroyed its reputation beyond recovery. The closing of the Neuer Markt and the rebranding and restructuring of the entire Frankfurt stock market indicate the seriousness of the crisis of German public equity markets.

The purpose of this paper is to examine the statistical properties of the volatility index of India, India Vix (Ivix), its relationship with the Indian stock market and its predictive power for forecasting future variance. Further, the paper examines the volatility transmission between India and developed markets.

The study uses quantile regression and VAR techniques to examine the empirical issues.

The results of the study show that Ivix returns are negatively related to stock market returns and the leverage effect is only significant around the middle of the joint distribution. The asymmetric response of Ivix is also not observed in the left tail and is significant again around the centre of the distribution. Monthly volatility forecasts obtained from Ivix contain important information about future market volatility. Finally, overnight volatility movements from the US market have significant effect on the Indian market's volatility and transmission in opposite direction was not observed.

If Ivix is included in a stock portfolio when the market moves up, Ivix may not fall significantly, consequently, the portfolio returns are not negatively effected. But, when market declines sharply, i.e. for large losses, Ivix may not move up significantly in the opposite direction, thereby not providing the much‐needed insurance to the portfolio returns. But for normal/average market declines, volatility derivatives on Ivix may be useful as portfolio insurance tools.

The paper is novel in employing quantile regression methodology to examine the empirical relationships of a volatility index. Volatility spillovers between emerging and developed markets are studied using volatility indices that are ex ante.

The main purpose is to explore the impact of major stock markets on China's market where major markets are represented by former G8 nations (current G7 and Russia).

The article makes use of: stationarity tests (ADF and PP unit root); long-run correlation tests (Johansen integration involving trace and maximum eigenvalue); impact of G8 markets on China (VECM test); influence of G8 markets on volatility in China's market (variance decomposition analysis) and, effect from shocks in G8 markets on China (impulse response function).

Using a period of 2009–2019 that avoids detecting linkages caused by interdependencies created by two major international crises, the article offers four major findings. First, except for Germany and Russia, G8 markets have a significant causal influence on China with UK having the greatest. Second, G8 markets are not the major source of short-run fluctuation in China's market but over time exercise a noteworthy collective impact with UK having the greatest impact. Third, there are occasions for international portfolio diversification with China's market providing greater diversification than G8 nations. Fourth, all markets provide a short-run window of abnormal profit.

The indexes used to represent national markets are assumed to be adequate representations.

Short-term abnormal profits exist. Investing in China, compared to G8 countries, offers greater portfolio diversification possibilities.

Removal of trade and investment barriers cause greater market integration.

By using recent data, this study reveals that G8 stock markets influence China's market.

Internet + and Electronic Business in China is a comprehensive resource that provides insight and analysis into E-commerce in China and how it has revolutionized and continues to revolutionize business and society. Split into four distinct sections, the book first lays out the theoretical foundations and fundamental concepts of E-Business before moving on to look at internet+ innovation models and their applications in different industries such as agriculture, finance and commerce. The book then provides a comprehensive analysis of E-business platforms and their applications in China before finishing with four comprehensive case studies of major E-business projects, providing readers with successful examples of implementing E-Business entrepreneurship projects.

Internet + and Electronic Business in China is a comprehensive resource that provides insights and analysis into how E-commerce has revolutionized and continues to revolutionize business and society in China.

The purpose of this study is to examine empirically the conditional correlation between the major US indices (S&P500 index and Dow Jones Industrial index) and three selected meat commodities as: Feeder Cattle, Leen Hogs and Live Cattle during the period from July 22, 2010 to June 30, 2017.

In this study, the authors use for the first time the GARCH-DECO (1,1) to examine empirically the conditional nexus between the major US indices (S&P500 index and Dow Jones Industrial index) and three selected meat commodities as; Feeder Cattle, Leen Hogs and Live Cattle during the period from July 22, 2010 to June 30, 2017.

From the empirical findings, the authors conclude the existence of a highly significance of conditional heteroscedasticity parameters can demonstrate us to distinguish the nature of the volatility dependency between S&P500 index and Dow Jones Industrial index and three selected meat commodities indices.

This can find clear the significance of relationship in the process of financialization of the major US index and meat commodities indices in the case of this paper.

Earlier studies establish a positive relationship between volatility index (VIX) and the stock index returns. These studies are mainly restricted to developed markets and research in this regard in emerging markets is scarce. The purpose of this paper is to fill this gap.

The paper studies the direct and cross‐sectional relationship of India VIX in relation to three important parameters: viz., stock beta, market to book value of equity and market capitalization. The paper constructs value weighted portfolio sorted on the basis viz., stock beta, market to book value of equity and market capitalization. The paper employs three‐factor multiple regression to find out the results.

The paper finds that India VIX has a positive and significant relationship with the returns of the value‐weighted high‐low portfolios sorted on the basis of the above parameters. The paper examines the behavior of India VIX in the presence of the above two parameters. The India VIX yields a positive and significant relationship with the above sorted portfolio returns.

India VIX was recently introduced in November, 2007 and therefore the research is expected to suffer from small sample bias.

The findings suggest India VIX is a distinct risk factor capable of predicting the price discovery mechanism of the market.

In the rapidly expanding emerging markets the introduction of Volatility Index is a recent phenomenon. Research in this regard is scarce, particularly in the area of finding predictive ability of the Volatility Index. This research is in this direction and would definitely help the market regulators and policy‐makers with their understanding of the market and market direction. It would help them to correct the market imbalances and avert crisis, which has been recently witnessed.

This study aims to look into how volatility significantly impacts the tracking error for daily-rebalanced leveraged bull and bear ETFs.

Using Morningstar return data and Chicago Board Options Exchange (CBOE) volatility index (VIX) data, the paper examines the daily tracking error for leveraged bull and bear ETFs. Tracking error is defined as the difference between the daily returns for a leveraged bull or bear ETF and the multiple of the daily return for that ETF's respective underlying benchmark index.

Changes in the market VIX of the CBOE have a significant and opposite effect on the daily returns for both leveraged bull and bear ETFs. Furthermore, these effects are more pronounced for bear ETFs than similarly leveraged bull ETFs.

The sample period (June 19, 2006 to September 22, 2009) contains periods of extraordinarily high volatility. Considering that the VIX reached an all-time high during this period, the results may be time-period specific and may not translate to other time periods.

The implication is that market timing may be feasible for enhancing daily returns for both leveraged bull and bear ETFs. However, any specific timing strategies go beyond the scope of this paper.

In this study, the paper examined the effects of expected market volatility on the daily tracking error of leveraged bull and bear ETFs. Specifically, the paper performed multiple linear regression analysis using Morningstar return data for the ETFs and their underlying benchmark and CBOE VIX data. The findings suggest that market timing could be beneficial for increasing daily yields for leveraged and inverse ETFs.

Prior research on additions to the S & P 500 and the smaller MidCap 400 and SmallCap 600 indexes reach different conclusions regarding the key variables that explain the cross-section of announcement period abnormal returns. Most notable in this regard is that liquidity measures, long thought to be of importance, do not appear to explain abnormal returns of the S & P 500 when other factors are controlled for. By contrast, they do appear to matter for additions to the smaller stock indexes. To explore this difference, the purpose of this paper is to analyze the abnormal returns upon announcement that a stock will be added to the Nasdaq-100 Index in a cross-sectional manner, controlling for several possible alternative factors.

This paper analyzes abnormal returns upon announcement that a stock will be added to the Nasdaq-100 Index. The authors consider several possible sources of the positive price effects in a multivariate setting that controls simultaneously for measures of liquidity, arbitrage risk, operating performance and investor interest and awareness. The authors then analyze both trading volume and the bid-ask spreads. The authors finally examine analyst and investor interest, focussing on changes in analyst coverage.

The authors find that only liquidity variables are significant, but that factors representing feedback effects on the firm’s operations and level of managerial effort are not. The authors find that the average bid/ask spreads of stocks added to the Nasdaq-100 index are lower after the addition. The authors also find that the number of analysts following a stock increases significantly after addition, verifying increased analyst interest. Both forms of evidence are consistent with the hypothesis that the additions are associated with enhanced liquidity for the stocks.

The authors conclude that what does happen to a Nasdaq stock when it is announced that it will be added to the Nasdaq-100 Index is that more analysts are drawn to it, and its market liquidity is enhanced. The authors conclude that what does not happen is that there is no evidence of significant effects of enhanced managerial effort or operating performance associated with the inclusion. This difference is noteworthy because it suggests that a certification effect of additions to the S & P indexes associated with S & P’s selection process are unique to it and do not apply to the Nasdaq-100 Index additions based on market cap alone. The results provide indirect evidence on the existence and significance of the certification effect associated with additions to the S & P indexes.