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The primary objective of the paper is to examine the short and long run price linkages between Malaysian unit trust funds and the stock market index as proxied by the Kuala Lumpur composite index (KLCI) over the period 1996‐2000.

Cointegration analyses are used to identify the long run relationship between unit trust funds and the stock market index while Granger causality tests are used to measure the short run price linkages.

Cointegration results show that the long run pricing performance of the unit trust funds differs significantly from that of the KLCI. Interestingly, the findings also reveal that two index funds are found not to be cointegrated with the stock market index. In the short run, one‐way Granger causality test shows that changes in the KLCI Granger causes changes in the unit trust funds. This suggests that fund managers are responding to the past changes in the stock market index over the short run.

The findings of non‐cointegration between passively managed funds and the KLCI are restricted to only two index funds in the sample among other actively managed funds. Since there were not enough index funds available over the study period, future research should include more index funds in the analysis.

In the short run, investors may gather information on the changes in their portfolio composition by observing the movement in the KLCI.

The paper represents the first evidence on the pricing relationships between unit trust funds and the local stock market index and the findings are important to investors in terms of their investment strategies.

This paper explores some institutional aspects of the Athens Stock Exchange (ASE) and investigates the time‐series properties of three major ASE stock indices. The results depict that future returns on these indices are difficult to predict. However, volatility in these indices can be predictable using the GARCH models. Various models for predicting volatility patterns are presented.

The purpose of this paper is to examine the effects of the current global crisis on the integration and co‐movements of selected stock index futures markets.

Time series techniques of cointegration and weekly data covering the period from January 2001 to December 2009 were used in this study. The period of analysis was divided into two periods, namely the pre‐crisis period (January 2001‐July 2007) and during crisis period (August 2007‐December 2009).

No evidence was found of cointegration among the stock index futures markets in both periods. Accordingly, the 2007 subprime crisis does not seem to affect the long‐run co‐movements among the stock index futures markets.

The stock index futures markets provide opportunity for the potential benefits from international portfolio diversification and hedging strategies even after the subprime crisis. The stock index futures significantly extended the variety of investment and risk management strategies available to investors.

Examining the effects of the US subprime crisis on the stock index futures markets integration, to the best of the authors' knowledge, goes clearly beyond the existing literature on the subject matter.

The various stock market indexes are interrelated due to the similar fundamentals which determine the movement in the respective markets. Applying the efficient market hypothesis, an investor should not be able to predict the movement of one index based on the past movement of another index. If the stock markets are efficient, then no long term comovement should exist between stock market indexes. The existence of a long term relation can be tested by use of cointegration tests and common serial correlation feature tests. If no cointegration exists and if no common serial correlation feature exists, then we would not be rejecting efficiency of the stock markets. Using the S&P 500 stock index, the Wilshire 5000 index, and the NASDAQ index, the Hang Seng index, the Footsie index, and the Nikkei index to proxy world stock market indexes, the empirical results of the cointegration and common feature test support the efficiency of the stock markets in most instances. However, the Footsie index consistently demonstrated a relation with the three US stock market indexes included in the study.

The purpose of this paper is to examine changes in stock returns, liquidity, institutional ownership, analyst following and investor awareness for companies added to and deleted from the Dow Jones Industrial Average (DJIA) index. Previous studies report conflicting evidence regarding the market reactions to changes in the DJIA index membership.

This study uses the event-study methodology to calculate abnormal returns and trading volume around the announcement and effective days of DJIA index changes from 1929 to 2015. It also tests for significant changes in liquidity, institutional ownership, analyst following and investor awareness in the 1990–2015 period. Multivariate regressions are used to perform a simultaneous analysis of competing hypotheses.

This study resolves the mixed results of previous DJIA index papers by documenting different stock price and trading volume reactions over the 1929–2015 period. Focusing on the most recent period, 1990–2015, the study finds that stocks added to (deleted from) the index experience a significant permanent stock price gain (loss). The observed stock price reaction seems to be associated with changes in liquidity proxies thus lending support for the liquidity hypothesis.

Limited data availability for the periods prior to 1990 prevents this study from identifying the exact reasons for different stock price and trading volume reactions across subperiods of the 1929–2015 period.

This study provides the most comprehensive examination of market reactions to changes in the DJIA index and resolves the mixed results of previous studies. A better understanding of market reactions around the DJIA index changes can help both individual and institutional investors with developing effective trading strategies and index managing companies with designing optimal announcement policies.

This paper aims to explore the interaction between investor attention and green security markets, including green bonds and stocks.

This study takes the Baidu index of “green finance” as the proxy for investor attention and constructs several generalized prediction error variance decomposition models to investigate the interdependence. It further analyzes the dynamic interaction between investor attention and the return and volatility of green security markets using the rolling time window.

The empirical analysis and robustness test results reveal that the spillovers between investor attention and the return and volatility of the green bond market are relatively stable. In contrast, the spillover level between investor attention and the green stock market displays significant time-varying and asymmetric effects. Moreover, the volatility spillover between investor attention and green securities is vulnerable to major financial events, while the return spillover is extremely sensitive to market performance.

The conclusion further expands the practical application and theoretical framework of behavioral finance in green finance and provides a new reference for investors and regulators. Besides, this study also lays a theoretical basis for investors to focus on the practical application of volatility prediction and risk management in green securities.

This paper aims to investigate Chinese bull and bear markets. The Chinese stock market has experienced a long period of bear cycle from early 2000 until 2006, and then it fluctuated greatly until 2010. However, the cyclical behaviour of stock markets during this period is less well established. This paper aims to answer the question why the Chinese stock market experienced a long duration of bear market and what factors would have impacted this cyclical behaviour.

By comparing the intervals of bull and bear markets between stocks and indices based on a Markov switching model, this paper examines whether different industries or A- and B-share markets could lead to different stock market cyclical behaviour and whether firm size can determine the relationship between the firm stock cycles on the market cycles.

This paper finds a high degree of overlapping of bear cycles between stocks and indices and a high level of overlapping between the bear market and a fraction of stock with increasing stock prices. This leads to the conclusion that the stock performance and trading behaviour are widely diversified. Furthermore, the paper finds that the same industry may have different overlapping intervals of bull or bear cycles in the Shanghai and Shenzhen stock markets. Firms with different sizes could have different overlapping intervals with bull or bear cycles.

This paper fills the literature gap by establishing the cyclical behaviour of stock markets.

This paper explored the new features of emerging stock markets, in order to point out the most associated indicators of increasing stock return volatility, which may lead to instability of emerging markets. The study covers a sample of five geographical areas of emerging economies, including Mexico, Korea, South Africa, Turkey, and Malaysia. It used the backward multiple‐regression technique to examine the relationship between monthly changes of stock price indices as dependent variable and the associated predicting local as well as international variables, which represent possible causes of increasing price volatility and initiating crises in emerging stock markets. The study covered monthly data for a period of forty‐eight months from January 1997 to December 2000. The study revealed that stock trading volume and currency exchange rate respectively represent the highest positive correlation to the emerging stock price changes; thus represent the most predicting variables of increasing price volatility. International stock price index, deposit interest rate, and bond trading volume were moderate predicting variables for emerging stock price volatility. While changes in inflation rate showed the least positive correlation to stock price volatility, thus represents the least predicting variable.

We investigate the causal relationships between volatility implied in Major Market Index (MMI) options and its component stocks' options from January, 1987 to October, 1989. We find that MMI implied volatility Granger causes component stock implied volatility for all twenty component stocks, which is consistent with the hypothesis that changes in volatility in index options markets leads volatility in underlying component (cash) markets. When we further analyze the sample by subperiod, we find that the causal relationships are insignificant in the period after the October 1987 crash, which is consistent with the hypothesis that exchange and regulatory actions taken after the crash weakened the influence of index options markets on cash markets. Trading strategies and programs involving stock index options and futures have been blamed for increasing volatility of the stock market. Indeed, trading in index futures and options markets has been blamed for much of the drop in stock prices in the crash of October 1987. After the crash, regulators took several actions to reduce the influence of futures and options market volatility on cash market volatility. If regulators' fears were legitimate and their efforts were successful, the volatility linkage between index options markets and their underlying cash markets should have been weakened. This paper provides two important contributions to our understanding of the volatility implications of index options markets. First, we examine the causality relationships between index and component stock implied volatility to assess whether or not changes in volatility in the index option market lead changes in volatility in the underlying component stock markets. Second, we test whether the causal relationships differ before and after the October 1987 crash to assess whether or not regulatory actions after the crash caused a change in these relationships. We measure volatility using implied standard deviations (ISDs) from options on the Major Market Index (MMI) and its component stocks. We form time series of ISDs for both the MMI and its component stocks, and then apply Granger causality tests to the series. For the full sample period of January 1987 to October 1989, we find that changes in index ISDs do Granger cause changes in component stock ISDs for all twenty component stocks, evidence consistent with the notion that volatility in index option market leads volatility in the component (cash) market. When we analyze the sample by subperiod, however, we find that the significant Granger causality holds only in the period before the October 1987 crash. Post‐crash subperiods show insignificant causality relationships, which suggests that efforts taken by exchange officials and regulators to reduce the influence of volatility in the index options and futures markets on cash market volatility were successful. The remainder of the paper is structured as follows. In Section I we discuss the potential for causal relationships between index option markets and their component markets and review related literature. Section II contains a discussion of our methodology and a description of our data. Section III contains a discussion of our results and Section IV concludes.

A composite sentiment index (CSI) from quantitative proxy sentiment indicators is likely to be a lag sentiment measure as it reflects only the information absorbed in the market. Information theories and behavioral finance research suggest that market prices may not adjust to all the available information at a point in time. This study hypothesizes that the sentiment from the unincorporated information may provide possible market leads. Thus, this paper aims to discuss a method to identify the un-incorporated qualitative Sentiment from information unadjusted in the market price to test whether sentiment polarity from the information can impact stock returns. Factoring market sentiment extracted from unincorporated information (residual sentiment or sentiment backlog) in CSI is an essential step for developing an integrated sentiment index to explain deviation in asset prices from their intrinsic value. Identifying the unincorporated Sentiment also helps in text analytics to distinguish between current and future market sentiment.

Initially, this study collects the news from various textual sources and runs the NVivo tool to compute the corpus data’s sentiment polarity. Subsequently, using the predictability horizon technique, this paper mines the unincorporated component of the news’s sentiment polarity. This study regresses three months’ sentiment polarity (the current period and its lags for two months) on the NIFTY50 index of the National Stock Exchange of India. If the three-month lags are significant, it indicates that news sentiment from the three months is unabsorbed and is likely to impact the future NIFTY50 index. The sentiment is also conditionally tested for firm size, volatility and specific industry sector-dependence. This paper discusses the implications of the results.

Based on information theories and empirical findings, the paper demonstrates that it is possible to identify unincorporated information and extract the sentiment polarity to predict future market direction. The sentiment polarity variables are significant for the current period and two-month lags. The magnitude of the sentiment polarity coefficient has decreased from the current period to lag one and lag two. This study finds that the unabsorbed component or backlog of news consisted of mainly negative market news or unconfirmed news of the previous period, as illustrated in Tables 1 and 2 and Figure 2. The findings on unadjusted news effects vary with firm size, volatility and sectoral indices as depicted in Figures 3, 4, 5 and 6.

The related literature on sentiment index describes top-down/ bottom-up models using quantitative proxy sentiment indicators and natural language processing (NLP)/machine learning approaches to compute the sentiment from qualitative information to explain variance in market returns. NLP approaches use current period sentiment to understand market trends ignoring the unadjusted sentiment carried from the previous period. The underlying assumption here is that the market adjusts to all available information instantly, which is proved false in various empirical studies backed by information theories. The paper discusses a novel approach to identify and extract sentiment from unincorporated information, which is a critical sentiment measure for developing a holistic sentiment index, both in text analytics and in top-down quantitative models. Practitioners may use the methodology in the algorithmic trading models and conduct stock market research.