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The purpose of this study is to compare market reactions to the change in the demand by index funds between large and small company stocks by examining the transition of the S&P 500, S&P 400 MidCap and S&P 600 SmallCap indexes from market capitalization to free-float weighting. This unique information-free event allows not only avoiding confounding information signaling and investor awareness effects but also comparing the effect of the decrease in demand on stocks of different sizes.

This study uses the event study methodology to calculate abnormal returns and trading volume around the full-float adjustment day. It also tests for significant changes in institutional ownership and liquidity. Multivariate regressions are used to examine the relation of liquidity changes and price elasticity of demand to the cumulative abnormal returns around the full-float adjustment day.

This study finds significant decreases in stock price accompanied with significant increases in trading volume on the full-float adjustment day, and significant gains in quasi-indexer institutional ownership and liquidity. The main finding is that cumulative abnormal returns around the event period are related to changes in the number of quasi-indexer and transient institutional shareholders, not to changes in liquidity or price elasticity of demand.

This study provides the first comprehensive comparison analysis of stock market reactions to the decline in demand between large and small company stocks. As an important implication for future studies of the index effect, changes in institutional ownership should be considered in the analysis.

The objective of this paper is to investigate the pandemic’s function as a driver of investor herding in international stock markets, given that the current coronavirus disease 2019 (COVID-19) crisis has caused a large rise in uncertainty.

The paper investigates the presence of herding behavior among the developed and BRICS (Brazil, Russia, India, China and South Africa) stock market indices during the COVID-19 crisis, by using a modified Cross-Sectional Absolute Deviation (CSAD) measure which is considered a proxy for herding and the wavelet coherence (WC) analysis between CSAD that captures the different inter-linkages between stock markets.

Using the CSAD model, the authors' findings indicate that the herding behavior of investors is present in stock markets during the four waves of COVID-19 crisis. The results also demonstrate that the transaction volume improve the herding behavior in the stock markets. As for the news concerning the number of cases caused by the pandemic, the results show that the pandemic does not stimulate herding; however, the number of deaths caused by this pandemic turns out to be a great stimulator of herding. By using the WC analysis, the authors' findings indicate the presence of herding behavior between the Chinese and stock markets (developed and emerging), especially during the first wave of the crisis and the presence of herding behavior between the Indian and stock markets (developed and emerging) in the medium and long run, especially during the third wave of the COVID-19 crisis.

The authors' study is among the first that examines the influence of the recent COVID-19 pandemic as a stimulator of herding behavior between stock markets. The study also uses the WC analysis next to the CSAD model to obtain robust results. The authors' results are consistent with the mental bias of behavioral finance where herding behavior is considered effective in volatility predictions and decision-making for international investors, specifically during the COVID-19 crisis.

The purpose of this study is to analyze the environmental efficiency level and trend of the transportation sector in the upper–mid–downstream of the Yangtze River Economic Belt and the JingJinJi region in China and assess the effectiveness of policies for protecting the low-carbon environment.

This study uses the meta-frontier slack-based measure (SBM) approach to evaluate environmental efficiency, which targets and classifies specific regions into regional groups. First, this study employs the SBM with the undesirable outputs to construct the environmental efficiency measurement models of the four regions under the meta-frontier and group frontiers, respectively. Then, this study uses the technology gap ratio to evaluate the gap between the group frontier and the meta-frontier.

The analysis reveals several key findings: (1) the JingJinJi region and the downstream of the YEB had achieved the overall optimal production technology in transportation than the other two regions; (2) significant technology gaps in environmental efficiency were observed among these four regions in China; and (3) the downstream region of the YEB exhibited the lowest levels of energy consumption and excessive CO₂ emissions.

To evaluate the differences in environmental efficiency resulting from regions and technological gaps in transportation, this study employs the meta-frontier model, which overcomes the limitation of traditional environmental efficiency methods. Furthermore, in the practical, the study provides the advantage of observing the disparities in transportation efficiency performed by the Yangtze River Economic Belt and the Beijing–Tianjin–Hebei regions.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

This study aims to examine the relationship between the fear index and initial public offering (IPO) aftermarket liquidity in ASEAN during the bearish time, the COVID-19 pandemic.

This study uses random effect panel regression analysis using two proxies of IPO aftermarket liquidity, namely, volume and turnover, on data of 90 IPO companies in the ASEAN-5 countries over four study periods: 30, 60, 90 and 100 days, after their IPOs.

The results indicate that the COVID-19 fear index significantly affects liquidity for all periods. The fear index decreases the stock aftermarket liquidity of ASEAN-5 IPO companies. The findings are consistent with additional tests.

This study initiates research during the COVID-19 pandemic in ASEAN-5 countries. Furthermore, while the other studies examine the stock performance of existing listed companies, this study focuses exclusively on the liquidity of companies that went public through IPOs in 2020.

This study aims to investigate the asymmetric impact of daily announcements regarding COVID-19 on investor sentiment in the stock market.

This study uses a Non-Linear Autoregressive Distribution Lag (NARDL) model that relies on positive and negative partial sum decompositions of the Coronavirus indicators. Five investor sentiments had been used and the analysis is conducted on the full sample period from 24th February 2020 to 25th March 2021.

The results show that new cases have a greater impact on investor sentiment compared to daily announcements of new deaths related to COVID-19. In addition to revealing a significant impact of new COVID-19 new cases and new death announcements on a daily basis on investor sentiment over the short- and long-term, this paper also highlights the nonlinearity and asymmetry of this relationship in the short and long run. Investors' sentiments are more affected by negative news regarding Covid 19 than positive news.

Financial markets have been severely affected by COVID-19 pandemic. This study is the first to measure the extent of reaction of investors to positive and negative announcements of COVID-19. Interestingly, this study examines the asymmetric effect of daily announcements on new cases and new deaths by COVID-19 on investor sentiments and derive many implications for portfolio managers.

Turnover in high-tech companies has long been a concern for managers and executives. Recent meta-analyses from the general turnover literature consistently show that job satisfaction is a major attitudinal antecedent to turnover intention and turnover behavior. Additionally, the available research on information technology (IT) employees focuses primarily on turnover intentions and not on a risky decision-making perspective and actual turnover (turnover behavior). The paper aim is to focus on that.

This study uses hierarchical ordinary least squares, process (Preacher and Hayes, 2004) and logistic regression.

The main predictor of actual turnover is risky decision-making, whereas job satisfaction is the main predictor of turnover intention.

The joint effects of risk and job satisfaction on turnover intention and behavior have not been studied in the IT domain. Hence, this study extends our understanding of turnover in general and particularly among IT employees by studying the combined effect of risk and job satisfaction on turnover intentions and turnover behavior. The study’s theoretical and practical implications are likewise discussed.

This study identified how investor sentiment on Twitter is associated with Brazilian stock market return and trading volume.

The study analyzes 314,864 tweets between January 1, 2017, to December 31, 2018, collected with the Tweepy library. The companies’ financial data were obtained from Refinitiv Eikon. Using the netnographic method, a Twitter Investor Sentiment Index (ISI) was constructed based on terms associated with the stocks. This Twitter sentiment was attributed through machine learning using the Google Cloud Natural Language API. The associations between Twitter sentiment and market performance were performed using quantile regressions and vector auto-regression (VAR) models, because the variables of interest are heterogeneous and non-normal, even as relationships can be dynamic.

In the contemporary period, the ISI is positively correlated with stock market returns, but negatively correlated with trading volume. The autoregressive analysis did not confirm the expectation of a dynamic relationship between sentiment and market variables. The quantile analysis showed that the ISI explains the stock market return, however, only at times of lower returns. It is possible to state that this effect is due to the informational content of the tweets (sentiment), and not to the volume of tweets.

The study presents unprecedented evidence for the Brazilian market that investor sentiment can be identified on Twitter, and that this sentiment can be useful for the formation of an investment strategy, especially in times of lower returns. These findings are original and relevant to market agents, such as investors, managers and regulators, as they can be used to obtain abnormal returns.

The primary goal of this paper is to investigate the relationship between stock market liquidity and firm dividend policy within a market implementing the tax imputation system. The main aim is to understand how the tax imputation system influences the relationship between firm dividend policy and stock market liquidity within a cross-sectional framework.

This paper investigates the relationship between stock market liquidity and the dividend payout policy under the full tax imputation system in the Australian market. This study uses the Generalized Least Squares regressions with firm- and year-fixed effects.

In contrast to the negative relationship between the liquidity of common shares and the firms' dividends documented in countries with the double tax system, the study reveals that in Australia, the dividend payout ratios are positively associated with liquidity after controlling for various explanatory variables with both the contemporaneous and lagged time periods. Such a finding is robust to the use of alternative liquidity proxies and to the sub-period tests and remains during the COVID-19 pandemic period.

The insights derived from this study have significant implications for various stakeholders within the economy. The findings provide regulators with valuable insights to conduct a more holistic assessment of how the tax system impacts the economy, especially concerning the dividend choices of firms. Within the context of a full tax imputation system, investors can make investment decisions without factoring in the taxation impact. Simultaneously, firms can be relieved of concerns about losing investors who prioritize liquidity, particularly when a high dividend payout might not align optimally with their financial strategy.

This study contributes to the literature by extending the literature on the tax clientele effects on dividend policy, providing evidence that the tax imputation system can moderate the impact of liquidity on dividend policy. This study examines the impact of the dividend tax imputation system on the substitution effect between dividends and liquidity.

The primary objective of this research is to provide evidence that there are two distinct layers of investor sentiments that can affect asset valuation models. The first is general market-wide sentiments, while the second is biased approaches toward specific assets.

To achieve the goal, the authors conducted a multi-step analysis of stock returns and constructed complex sentiment indices that reflect the optimism or pessimism of stock market participants. The authors used panel regression with fixed effects and a sample of the US stock market to improve the explanatory power of the three-factor models.

The analysis showed that both market-level and stock-level sentiments have significant contributions, although they are not equal. The impact of stock-level sentiments is more profound than market-level sentiments, suggesting that neglecting the stock-level sentiment proxies in asset valuation models may lead to severe deficiencies.

In contrast to previous studies, the authors propose that investor sentiments should be measured using a multi-level factor approach rather than a single-factor approach. The authors identified two distinct levels of investor sentiment: general market-wide sentiments and individual stock-specific sentiments.