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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 investigate the impact of banning cryptocurrencies on stock markets.

The paper uses an event study approach and data from stock market indices in nine countries that imposed a ban. It uses the constant mean model and the market model, with two different benchmarks for global returns, to analyze if any of the stock indices show abnormal returns on or around the announcement of a cryptocurrency ban.

The analysis shows that banning cryptocurrencies did not affect the returns of stock markets in any of the countries studied, indicating that the cryptocurrency market and stock markets are decoupled from each other, or the ban was not effectively implemented.

To the best of the author’s knowledge, this paper is the first to explore the potential spillover effect of a cryptocurrency ban on stock markets. It also bridges two strands of literature: the relationship between cryptocurrencies and traditional assets, and the impact of cryptocurrency regulation on their returns.

This research investigates Airbnb’s financial implications in emerging economies and their potential to influence stock market profitability.

Employing a multifaceted approach, the study combines parametric and nonparametric tests, robustness checks, and regression analysis to assess the impact of Airbnb’s announcements on emerging economy stock markets.

Airbnb’s announcements affect emerging economies' stock markets with a distinct pattern of cumulative abnormal returns (CAR): negative before the announcement and positive afterward. Informed investors strategically leverage this opportunity through short selling before the announcement and acquiring positions following it. Regression analysis validates these trends, revealing that stock index returns and inbound tourism affect CAR before announcements, while GDP growth influences CAR afterward. Announcements pertaining to emerging economies exert a more pronounced impact on stock indices compared to city-specific announcements, with COVID-19 period announcements demonstrating greater significance in abnormal returns than non-COVID-19 period announcements.

This study advances existing literature through a comprehensive range of statistical tests, differentiation between emerging countries and cities, introduction of five macroeconomic variables, and reliance on credible primary Airbnb data. It highlights the potential for investors to leverage Airbnb announcements in emerging markets for stock market profits, emphasizing the need for adaptive investment strategies considering broader macroeconomic factors.

This paper aims to explore the election cycle and financial markets puzzle in a unique emerging market like Malaysia.

By employing an event-study methodology and wavelet analyses, the author tests for uncertain information hypothesis by examining the reactions of the Kuala Lumpur Composite Index (KLCI) and ringgit surrounding Malaysian general elections, spanning from GE5 (1978) to GE14 (2018). This paper also explores the relationship between KLCI and ringgit.

While the author does not find support for the uncertain information hypothesis, the author uncovers that KLCI tends to overreact following elections, regardless of the winning coalition. The author also records no relationship between KLCI and ringgit in the short run, but the author observes that ringgit leads KLCI in the long run.

The study’s findings bear implications for investors' disposition in the Malaysian equity market. Investors should square off their positions before the general elections to avoid equity market overreactions and potential losses.

Before Malaysia GE14 (2018) general election, Barisan Nasional carried the reputation as one of the longest-serving ruling coalitions in the world since Malaya independence in 1957. However, the ruling coalition was voted out in GE14 (2018), and the Malaysian equity has since dropped.

The authors analyze the impact of COVID-19 on listed European electricity companies and differentiate between renewable and traditional electricity, to show the heterogenous characteristics of electricity subsectors and the differences between renewable and traditional electricity.

Using the event study method, the authors calculate the cumulative average abnormal returns (ARs) before and after the World Health Organization pandemic announcement and the declaration of national lockdowns in Europe.

The results show that while the European electricity sector was overall negatively impacted by the COVID-19 announcement, this impact was larger for renewable companies due to their riskier investment profile. Moreover, after the national lockdowns came into effect, the recovery in the financial markets return was smaller for the latter.

There may be variables to be included in the model to analyze possible differences between companies and countries, as well as alternative econometric models. Limited to the data, the authors did not investigate the different impact of the economic policy uncertainty from various countries inside or outside the EU.

The results have important implications for both investors and policymakers since the heterogenous characteristics of electricity subsectors. This heterogeneity prompts different investor reactions, which are necessary to know and to understand.

As far as the authors know, this is the first study that analyses the effect of COVID-19 in heterogeneity profile of both types of electricity, renewable and traditional.

This paper aims to examine the impact of the Brexit referendum on the risk structure of financial asset prices. Co-movements are analysed using daily price returns of major stock and bond indices as well as commodities and exchange rates from June 2014 to June 2018. The authors used a multivariate GARCH model to study the dynamics of the conditional correlation matrix of asset returns. It was found that the conditional variances and correlations of assets spike on and after the Brexit referendum and then quickly revert to normal levels, suggesting that the effect of the referendum was transient rather than structural. The findings are of interest to investors as co-movements of financial assets can significantly impact financial portfolios and hedging strategies.

The authors used a multivariate GARCH model to study the dynamics of the conditional correlation matrix of asset returns.

It was found that the conditional variances and correlations of assets spike on and after the Brexit referendum and then quickly revert to normal levels, suggesting that the effect of the referendum was transient rather than structural.

The findings are of interest to investors as co-movements of financial assets can significantly impact financial portfolios and hedging strategies.

To the best of the authors’ knowledge, research studying the underlying asset co-movements around Brexit does not exist.

This chapter presents a study on the work of commercial diplomats as international business promoters at foreign posts. Research has largely overlooked the actual roles and activities of commercial diplomats in explaining the effectiveness of commercial diplomacy and international business support. In this study, it is assumed that commercial diplomats’ behavior is influenced by informal institutions. Face-to-face semi-structured interviews with 23 commercial diplomats at foreign posts from different countries were conducted and analyzed. The results show three different types of role behavior and differences in proactivity per type. Informal institutions such as background, skills, and experience, cultural differences, and the working environment suggest to explain the differences in levels of proactive international business support behavior of commercial diplomats. Further research is needed to assert these findings.

This study uses an event study approach which is the development of the efficient market hypothesis theory. First, the random walk test was conducted on the Jakarta Composite Index (JCI) to test the efficiency in the weak form. Furthermore, event study analysis was carried out on JCI and nine sectoral indices to determine the impact of COVID-19 related events on price movements. The study found that JCI prices follow a random walk pattern so that the stock market in Indonesia is efficient, at least in a weak form. In the event study testing, only events related to the first confirmed case of COVID-19 and the implementation of large-scale social restriction in Indonesia affected the composite index. From a sectoral point of view, only the event of Jakarta’s call center had no impact on price changes in the sectoral index. Thus, each index had a different effect throughout the event. The reaction seen from the movement of prices for the composite and sectoral index to the public information explains that the condition of the Indonesian capital market is efficient, at least in semi-strong form.

With a sample of 22 banks, this study examines the significance of the news contents about the privatization of two public sector banks in India. New information does impact the stock markets. This study provides evidence on how the privatization of public sector banks impacted the returns of the Indian banking sector.

This study employs the standard event study methodology with the market model for estimating the normal returns.

The statistical results indicate that while the private sector banks experienced positive average abnormal returns on the event day, the cumulative effect of the announcement is negatively significant for both private and public sector banks. The statistical results also provide evidence of information leakage, with significant results before the announcement date. The shorter event windows analysis exhibits significant positive returns in the 5-days [−2, +2] window for the private sector banks and the entire sample, signifying a positive short-term impact on the private sector banks.

The event study literature captures the impacts of many events. However, to the best of our knowledge, the impacts of the privatization of the Indian public sector banks have never been examined using the event study methodology. Hence, this study anticipates being the first-ever study to fill this gap and extend the available literature in finance. In addition, although we provide Indian evidence, future studies may be oriented to capture cross-country impacts.

Literature has numerous debates about the relation between emerging financial environmental, social, and governance (ESG) factors and financial performance with mixed results. The authors use a unique data set generated by big data analytics (from web-based data mining) for three environmental areas (water, land, and air) to test hypothesis in the extreme events (defined as those that are over/under ±2.58 multiplied by the standard deviation) have a high chance of predicting equity price movements within an window of −3/+10 days, respectively, prior to and after the event. The authors repeat the similar robustness study for a sample of 2018 and the results still holds. The authors interpret these findings to suggest that: (1) studies using continuously AI-generated data for ESG categories can have significant predictive power for extreme events; and (2) that such high correlations can be used to confirm the materiality of some ESG data. The authors conclude with noting limitation of this initial study, and present specific areas for future research.