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This study examines the influence of the ownership structure of banks on investors' behavior by dissecting the investors' response to news regarding performance indicators in private and government-owned banks.

The event study methodology is used for the analysis. The data for 35 banks (out of 38), listed on the National Stock Exchange (NSE) for a duration of 230 months (January 2001 to February 2020) is collected. A set of cross-sectional regression analyses is done to identify variables influencing the returns under differential circumstances.

Private banks seem to display a sharper response to negative changes in earnings, while government-owned banks show a more robust reaction to a positive change. The contrast is seen in the variables, having a bearing on the abnormal returns After controlling for a set of factors, the regression analysis shows the ownership structure may not matter on abnormal returns (on event day), the factors such as a change in quarterly earnings, firm-size and three-year average-sales growth influence the positive and negative changes in abnormal returns of government banks, and predictability for private banks is found to be poor regarding selected indicators.

The study evaluates the role of ownership structure on the heterogeneity in investors' responses to the financial performance of banks, thereby assisting in designing strategies to ensure the optimal outcome around the quarterly earnings announcements.

The study aims to identify the possible risk factors for electricity grids operational disruptions and to determine the most critical and influential risk indicators.

A multi-criteria decision-making best-worst method (BWM) is employed to quantitatively identify the most critical risk factors. The grey causal modeling (GCM) technique is employed to identify the causal and consequence factors and to effectively quantify them. The data used in this study consisted of two types – quantitative periodical data of critical factors taken from their respective government departments (e.g. Indian Meteorological Department, The Central Water Commission etc.) and the expert responses collected from professionals working in the Indian electric power sector.

The results of analysis for a case application in the Indian context shows that temperature dominates as the critical risk factor for electrical power grids, followed by humidity and crop production.

The study helps to understand the contribution of factors in electricity grids operational disruptions. Considering the cause consequences from the GCM causal analysis, rainfall, temperature and dam water levels are identified as the causal factors, while the crop production, stock prices, commodity prices are classified as the consequence factors. In practice, these causal factors can be controlled to reduce the overall effects.

From the results of the analysis, managers can use these outputs and compare the risk factors in electrical power grids for prioritization and subsequent considerations. It can assist the managers in efficient allocation of funds and manpower for building safeguards and creating risk management protocols based on the severity of the critical factor.

The research comprehensively analyses the risk factors of electrical power grids in India. Moreover, the study apprehends the cause-consequence pair of factors, which are having the maximum effect. Previous studies have been focused on identification of risk factors and preliminary analysis of their criticality using autoregression. This research paper takes it forward by using decision-making methods and causal analysis of the risk factors with blend of quantitative and expert response based data analysis to focus on the determination of the criticality of the risk factors for the Indian electric power grid.

The purpose of this study is to explain the mixed results to changes in the DJIA index documented in the literature. The authors show that economic cycles, especially recessionary periods, explain the difference in findings.

The authors examine changes in the Dow Jones Industrial Average (DJIA) from 1929 to 2019 to evaluate immediate and long-term market reactions after a component change. Using multiple event-study methodologies, the authors examine the full era, the pre- and post-exchange traded fund (ETF) windows and economic cycles using both pre and post-estimation windows.

In aggregate, DJIA additions do not present an increase in wealth; however, wealth effects are positive during expansions and negative during recessions. Deletions have a negative wealth effect. The authors find weak evidence of an indexing effect. Additions are positive post-1998, and deletions remain negative regardless of era. In the long run, firms added to the DJIA have positive abnormal returns in the second year after inclusion. Deletions in recessionary times have negative returns three years after removal, a signal of longer-term wealth decline for these firms.

The DJIA changes periodically to better represent industries relevant to the blue-chip market, and the findings have implications for fund managers and active investors.

The DJIA changes periodically to better represent industries relevant to the blue-chip market, and the findings have implications for fund managers and active investors.

Prior literature presents limited time series of data points and mixed results and implications. The authors find that the economic cycle is a driving factor that supports predicted signs and amounts of wealth change. Furthermore, the authors see limited ETF impact on DJIA changes and some impact of the choice of estimation period.

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 purpose of this study is to examine the impact of a recent innovation in the uranium market, the Global X Uranium Exchange-Traded Fund (URA), on the trading characteristics of constituent and non-constituent stocks.

The authors analyse bid-ask spread measures, relative effective spreads and adverse selection costs to assess changes in information asymmetry among uranium stocks. The authors also study abnormal returns to assess the impact of URA on the market.

Over a three-month period, following the introduction of URA, the authors find uranium stocks display decreased bid-ask spread measures, driven by reductions in information asymmetry. Relative effective spreads decrease by 36% after the introduction of URA, and adverse selection costs decline by 24% over the same period. Uranium stocks experience a significant positive abnormal return of 5.0% the day after the introduction of URA with subsequent price reversals. These suggest that the introduction of URA prompted uninformed traders to rebalance portfolios and migrate to the less information-sensitive Exchange-Traded Fund (ETF), causing temporary deviations in trading characteristics.

The authors demonstrate that the introduction of new financial securities to the market can have a significant impact on the trading characteristics of related equities. As URA is the only ETF in the uranium sector, the authors thereby avoid the influence of multiple ETFs that may have impacted previous studies.

This study aims to evaluate the presence of abnormal returns due to stock splits or reverse stock splits in the Brazilian capital market context.

The event study technique was used on data from 518 events that occurred in a 30-year period (1987–2016), comprising 167 stock splits and 351 reverse stock splits.

The results revealed the occurrence of abnormal returns around the time the shares began trading stock splits or reverse stock splits at a statistical significance level of 5%. The main conclusion is that stock split and reverse stock split operations represent opportunities for extraordinary gains and may serve as a reference for investment strategies in the Brazilian stock market.

This study innovates by including reverse stock splits, as the existing literature focuses on stock splits, and by testing two distinct “zero” dates that of the ordinary general meeting that approved the share alteration and the “ex” date of the alteration, when the shares were effectively traded, reverse split or split.

This paper aims to assess, from an empirical perspective, the research question if public media reports which relate concrete banks to concrete allegations of money laundering have an adverse impact on banks stock prices and what are the drivers of such impact?

The paper makes use of event study methodology and uses the constant mean and the market model. The event window is calibrated towards a five-day window, and the estimation window has a length of 90 days, in line with best academic practices. Drivers are identified by correlation analysis. and the market model uses ordinary least squares regression.

The application of event study methodologies yields the results that stock prices of affected banks generate, at the date of the news appearance, statistically significant negative abnormal returns under both the market model and the constant mean model. As negative abnormal returns have been mainly found at the date of the event itself, the findings confirm that the impacts of money laundering may be severe but short natured. In addition, the paper finds that the identified negative abnormal returns may be driven by the banks’ size in terms of total assets, by the bank’s profitability in terms of return on assets and by the bank’s sustainability risk.

The findings have implications in terms of banking and supervisory practices. In specific, the findings help to argue that banking consolidation is needed to lower the impacts of AML cases, as stock prices of larger banks show less sensitivity. In addition, the findings could be used to determine financial sanctions against banks violating AML regulation. Finally, the findings imply that AML news can have severe and fast-moving financial stability considerations and are, therefore, important in crisis situations.

As there appears to be no substantial research that applies event study methodology to the money laundering context, the combination of research question and methodology has an innovative character. In addition, there is no clear literature on media and money laundering.

As a financial policy, dividend policy significantly affects firm value. This chapter analyzes how stock prices react to dividend decisions. First, a dividend payment is an extraction of value; therefore, stock price theoretically drops by the dividend amount on the ex-dividend day. In practice, the price drop and the dividend magnitude are not equal because of tax clientele, short-term trading, and market microstructure. Investors are indifferent in trading stocks before and after stocks go ex-dividend if they obtain equal marginal benefits from the two trading times. The difference in tax rates on dividends and capital gains leads to the gap between the price drop and the dividend amount. Moreover, if transaction costs are considerable, investors have high incentives to short-sell stocks until they cannot obtain more profits. The final outcome of this short-term trading is the difference between the price drop and the dividend amount. Furthermore, market microstructure factors such as limit orders, bid-ask spread, and price discreteness also create this gap. Second, dividend announcements convey valuable information to outsiders. When firms announce increases (decreases) in dividends, their stock prices tend to increase (decrease). Third, dividend policy is negatively related to stock price volatility. This negative relationship is explained by duration effect, rate of return effect, arbitrage realization effect, and information effect. Empirical evidence for this relationship is found in many countries. Finally, dividend smoothing is also considered as a signal about firms' future earnings. Consequently, firms with stable dividends have higher market value. In other words, dividend stability has a positive effect on stock prices.

The present study, by using signaling perspective aims to investigate short-term valuation impact of rebranding announcements (with name change) on stock performance of 160 service firms listed on NSE NIFTY-500 over the period of 2000–2019.

An event study methodology is used to estimate the cumulative abnormal returns (CARs) and its statistical significance is tested with both parametric and non-parametric test-statistics. Separate analysis has been conducted for firms with “major vs minor” and “restructuring vs non-restructuring” name change.

Findings of the study suggest that rebranding decisions are negatively associated with abnormal returns around the announcement period indicating strong disapproval of name change event. In addition, investors formed strong adverse opinion for major name change firms as compared to minor name change firms. Further, restructured name change sample document larger negative drift than non-restructured sample.

Findings offer substantial repercussions for shareholders who can make informed judgments about name change as a signal of reinventing brand identity. Managers should announce detailed rationale behind name change decision to market for enhancing corporate reputation.

This study contributes to marketing-finance interface literature and is first to examine market reaction to name change of Indian service firms and moreover, made a distinction between major vs minor and restructured vs non-restructured name change events for these firms.

This paper aims to investigate the spillover effects of jump and crash events among Chinese nonfinancial firms.

This sample consists of more than 1.5 million weekly observations of over 3,000 Chinese listed firms over the period 1991–2015. The authors utilize univariate tests to compare the post-event performance of matched peer and non-peer control firms and cross-sectional regressions of their abnormal returns/cumulative abnormal returns (ARs/CARs) and returns on assets (ROAs).

The authors find that extreme risk-adjusted abnormal stock returns (stock price crashes and jumps) generate statistically significant ARs/CARs in the same directions in industry, size, leverage, and geographical location matched peer firms in Chinese stock market. Further tests reveal that peer firms' response to the crash event is pronounced more in the group of firms about which the information asymmetry is high between investors and firms.

Portfolio investors can adjust their portfolios accordingly by selling stocks of the matching rival firms during a crash period. Policymakers may develop policies so as to protect the interests of small investors in the events of crashes in the markets. They can reduce the information asymmetry between the firms and the investors by making information about the firms more transparent, so as to reduce the contagion in case of crash event.

This study has important implications for portfolio investment managers and policymakers.

To the best of authors' knowledge, this is the first study that combines the jump and crash events and attempts to assess their spillover effects on other firms in Chinese stock market.