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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.

Several studies have examined the relationship between spin-off announcements and stock performance. However, a comparison of the announcement effect of different schemes of spin-offs remains relatively underexplored in the literature. This study aims to find the differential impact of pure scheme and composite schemes of spin-offs on parent company stock performance. A pure scheme includes only the separation of business into independent companies, while a composite scheme includes a simultaneous merger of one of the companies with another company along with separation.

A total of 109 pure and 51 composite spin-off announcements made by Indian listed companies from 2010 to 2023 are examined using event study methodology. The cross-sectional t-test is used to measure the significance of abnormal returns. The t-test for two sample means (right-tailed) is incorporated to test the significance of variations in the stock returns of pure and composite schemes of spin-off announcements. Cross-sectional regression is also done to evaluate the impact of the type of scheme on the spin-off return.

The study found a cumulative average abnormal return of −1.06% for the pure spin-off and 8.27% for the composite spin-off over a 41-day event window. The univariate analysis revealed that the composite scheme generates a significantly higher cumulative average abnormal return than the pure scheme. Regression analysis also confirmed that the composite scheme significantly positively impacts the stock return. It can be concluded that investors favour the composite scheme, expecting that it will deliver a better strategic fit and generate synergy.

This paper makes a valuable contribution to the existing literature on corporate spin-offs. The study by analysing and comparing how the spin-off and merger combination differently affects the stock performance, helps the investor who wants to capitalize on the market imperfections and the managers to make complex business decisions.

This research investigates the differential impact of information technology (IT) investments based on their features, such as investment in data management capability, security improvement, IT outsourcing or new IT infrastructure. The Long-Horizon Event Study (LHES) is essential for providing a more appropriate measure of the value of IT investments because firms' strategic decisions often set long-horizon and large-scale organizational goals, and there is inherent uncertainty regarding future cash flows resulting from these investments. Therefore, the authors aim to analyze how announcements of IT investments affect the firm's abnormal stock returns over the long term and to compare the differential impact of different features of IT investment.

The authors gathered IT investment announcements and stock data of listed firms in Korea between 2000 and 2018, and the monthly stock market returns over the 5 years after the announcements. To measure the differential impact of IT investments based on the investment features, the authors separate announcements data into five groups. A LHES is used to estimate the long-term effects of IT investment announcements.

The results indicate that announcements of IT investments had a long-term positive effect on firm performance. Additionally, the findings reveal differential effects of IT investments across industries and investment features. Notably, news of self-developed IT investments and IT investments in the manufacturing industry had significantly positive effects. However, contrary to common belief, announcements of investments in so-called essential IT areas such as data, security, or new IT infrastructure did not yield significant effects.

Although the need for LHES has been emphasized in information systems research, few follow-up studies have been conducted since Barua and Mani (2018). This is primarily due to the challenges associated with collecting large-scale abnormal stock returns data over a long horizon. This research represents the first LHES to investigate the differential impact of IT investments based on their features. By doing so, this study can provide valuable insights for decision-makers within firms, helping them understand the time horizon of market outcomes of IT investments based on their features. Furthermore, this work extends the scope of LHES to comprehend the differential impacts of investment features. For instance, managers need to grasp that so-called essential IT investments, such as data management, security enhancements or new IT infrastructure, may not necessarily generate long-term market value.

This study aims to examine the effects of payment methods used in mergers and acquisitions (M&A) conducted by UK companies spanning the period from 2007 to 2019.

The study used the estimated expected returns method to identify abnormal returns during the deal announcement period, applying event study analysis with both univariate and multivariate regression models to detect cumulative abnormal returns around the announcement timeframe.

The results show a short-term positive return increase for acquiring firms, controlling for deal-specific characteristics like target firm location and payment methods. The authors observed a preference for cash financing across domestic and cross-border transactions. Multivariate analysis revealed insignificance between payment methods and deal characteristics like cross-border acquisitions and diversification.

The study’s focus on publicly traded firms in the UK and the absence of a comparative analysis across different regions and markets limits the sample size and may impact the generalizability of findings.

The study proposes three practical implications. Firstly, firms should tailor payment methods to each transaction, aligning with strategic goals to optimize value and mitigate risks. Secondly, decision-makers must prioritize comprehensive due diligence and strategic alignment throughout M&A processes to enhance success and maximize synergies. Finally, analysing broader strategic contexts and regulatory landscapes when structuring transactions enables goal attainment, such as market expansion or value creation.

The study’s findings can promote transparency and accountability among corporate decision-makers in M&A transactions. Stakeholders can advocate for transparent decision-making processes, enhancing trust in corporate governance.

This study provides valuable insights into the impact of payment methods on shareholder value in M&A transactions involving UK companies, informing strategic decision-making and contributing to the understanding of corporate finance dynamics.

This study aims to comprehensively understand market reactions to Bursa Malaysia's announcement on mandatory climate-change-related disclosures, exploring sector-specific dynamics and cross-sectional influences.

The study uses event study methodology on 412 listed firms to analyze market reactions around the announcement date. The sector-wise analysis further delves into variations across industries. Cross-sectional analysis explores the significance of environmental, social and governance (ESG) scores and firm controls in explaining the differences across sample firms.

The event study reveals initial negative market reactions on the event day, with a subsequent shift from positive to negative cumulative impact, indicating the evolving nature of investor sentiment. The sector-wise analysis highlights heterogeneous effects, emphasizing the need for tailored strategies based on industry-specific characteristics. The cross-sectional findings underscore the growing importance of ESG factors, with firm size and performance influencing market reactions. Financial leverage and liquidity prove insufficient to explain cumulative abnormal return (CAR) differences, while past returns and volatility are influential technical factors.

The economic significance of the results indicates a growing trend where investors prioritize companies with more substantial ESG scores, potentially driving shifts in corporate strategies toward sustainability. Better ESG performance signifies improved risk management and long-term resilience in the face of market dynamics. Regulatory bodies may respond by enhancing ESG reporting requirements, while financial institutions integrate ESG factors into their models, emphasizing the benefits of sustainability and financial performance.

This research contributes to the existing literature by providing a nuanced analysis of market responses to climate-related disclosures, incorporating sector-specific dynamics and cross-sectional influences. The findings offer valuable insights for businesses and policymakers, emphasizing the need for tailored approaches to climate-related disclosure management.

This paper examines the reaction of the Egyptian stock market to two substantial devaluations of the Egyptian pound (EGP) in 2022 and tests the informational efficiency of the Egyptian market.

The paper uses the event study framework to analyze the significance and direction of abnormal returns of the leading index of the Egyptian stock market (EGX30) on and around the devaluation days. It employs both the constant mean model and the market model to estimate the normal returns of the EGX30. Additionally, the paper uses data on two equity indices, one global and one for emerging markets, as benchmarks for normal returns.

The paper finds that the Egyptian stock market experienced significant positive abnormal returns on the devaluation days of the EGP in March and October of 2022, indicating a positive market reaction to the devaluation. Furthermore, evidence suggests that the Egyptian market may not be informationally efficient as significant positive abnormal returns were observed two weeks before and two weeks after the devaluation day, suggesting news leaks and delayed reactions, respectively.

This study is the first to examine the impact of the recent two devaluations of the EGP in 2022 on the Egyptian stock market. It complements existing literature by analyzing the immediate market reaction to two consecutive devaluations in an African country. Furthermore, the paper evaluates the efficiency of the Egyptian market in processing information related to exchange rates.

This paper aims to examine the short-term market impact of the beginning of the military conflict between Russia and Ukraine (24 February 2022), the world’s largest oil and gas companies.

The authors examine the world’s 100 largest listed oil and gas companies at and around the beginning of the military conflict between Russia and Ukraine using an event study methodology.

The authors observe a positive and statistically significant stock price reaction at and around the military conflict. These results are consistent with the asset pricing perspective. Conversely, the stock market returns of Russian oil and gas companies, as well as those companies that were “forced” to divest in Russia due to corporate activism, exhibit a negative and statistically significant impact from the conflict. These reactions are reinforced or mitigated by company-specific characteristics such as size, profitability and institutional ownership. Finally, the findings indicate that companies engaged in oil and gas exploration and production report abnormally higher returns compared to firms in the other two subsectors of the industry.

The effect of the war on stock markets has been relatively little examined in the financial theory. This study intends to fill this gap in the literature.

This paper investigates the short-term market impact of the beginning of the military conflict between Russia and Ukraine (February 24, 2022) on a set of airline stocks listed.

This study uses an event study methodology, cross-section analyses and interaction effects to study the effect of the war on airline stock prices and firm-specific characteristics that explain the cumulative abnormal return.

The authors observe a negative and statistically significant stock price reaction at and around the beginning of the military conflict between Russia and Ukraine, for 74 listed airlines. These results are consistent with investment portfolio rebalancing and asset pricing perspective. Moreover, this study's results show a higher negative stock market reaction for airlines based in Europe. Empirical evidence suggests the existence of a “proximity penalty” for European companies. Finally, this study's results provide insights into which airline-specific characteristics emerge as value drivers. Larger, well-capitalized (high liquidity and low debt) and profitable airlines firms with less institutional ownership have superior stock market returns and show more able to handle with the losses resulting from the war.

This paper fills a gap in the literature about the impact of the Russia–Ukraine war on the airline industry.

This paper aims to examine the short-term market reaction for the world’s 100 largest listed defence firms at and around the three recent largest threats to the global economy – Ukraine–Russia war, Fourth Taiwan Strait Crisis and the Hamas terrorist attack on Israel.

The author examine the impact of the three recent largest threats to the global economy in the largest listed defence firms using an event study methodology.

The results show a positive and statistically significant short-term reaction around the three geopolitical threats. The results also reveal the existence of higher abnormal returns for defence firms with greater weight of defence sales, in line with the captured regulator theory and for firms with higher research and development and capital expenditure intensity.

The effect of the war on stock markets has been relatively little examined in the financial theory. This study intends to fill this gap in the literature through the analysis of the three recent largest threats to the global economy.

This paper aims to examine the impact of announcements related to 77 interventions by 46 listed Indian pharmaceutical firms during COVID-19 on the abnormal returns of the firms. The study also finds the variables which explain cumulative abnormal returns (CARs).

This study uses standard event methodology to compute the abnormal returns of firms announcing pharmaceutical interventions in 2020 and 2021. Besides this, the multilayer perceptron technique is applied to identify the variables that influence the CARs of the sample firms.

The results show the presence of abnormal returns of 0.64% one day before the announcement, indicating information leakage. The multilayer perceptron approach identifies five variables that explain the CARs of the sample companies, which are licensing_age, licensing_size, size, commercialization_age and approval_age.

The study contributes to the efficient market literature by revealing how firm-specific nonfinancial disclosures affect stock prices, especially in times of crisis like pandemics. Prior research focused on determining the effect of COVID-19 variables on abnormal returns. This is the first research to use artificial neural networks to determine which firm-specific variables and pharmaceutical interventions can influence CARs.