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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.

To explain for doctoral students and new faculty, the appropriate techniques for using event study methods while identifying problems that make the method difficult for use in the context of African markets.

We review the finance and strategy literature on event studies, provide an illustrative example of the technique, summarize the prior use of the method in research using African samples, and indicate remedies for problems encountered when using the technique in African markets.

We find limited use of the technique in African markets due to limited data availability which is attributable to problems of infrequent trading, thin markets, and inadequate access to free data.

Our review of the literature on event studies using African data is limited to English-language journals and sources accessible through our library research databases.

More often, researchers will need to use nonparametric techniques to evaluate market responses for companies in or events affecting the African markets.

We make a contribution with this chapter by giving a more detailed description of event study methods and by identifying solutions to problems in using the technique in African markets.

In the last few years, information and communication technologies (ICTs) and social media have become increasingly relevant to politicians and political parties alike, often used to issue statements or campaigning, among others. At the same time, many citizens have become more involved in politics, partly due to the highly interactive and social environments that the social networking services (SNS) provide. Political events flow through these networks, influencing their users; such events, however, often start offline (outside the online platform) and are, therefore, hard to track. Event studies, a methodology often used in financial and economic studies, can be translated to social networks to help modeling the effect of external events in the network. In the present case, the event study methodology is applied to two sample cases: the tariff war between the United States and China, with multiple responses and retaliations from both sides, and the Brexit referendum. In both cases, the Twitter social networks that arise from users who discuss the respective subjects are analyzed to examine how political events shape and modify the network. Results show how event studies, combined with the possibilities offered by the ICTs both in data retrieval and analysis, can be applied to understand the effect of external political events, allowing researchers to quantitatively track, observe, and analyze the spread of political information over social network platforms. This is a first step toward obtaining a better understanding on how political messages are diffused over social networks and their effects in the network structures and behaviors.

This chapter conducts an event study of 30 quantitative easing (QE) announcements made by 21 central banks on daily government bond yields and bilateral US dollar exchange rates in March and April 2020, in the midst of the global financial turmoil triggered by the COVID-19 outbreak. The chapter also investigates the transmission of innovations to long-term interest rates in a standard GVAR model estimated with quarterly pre-COVID-19 data. The authors find that QE has not lost effectiveness in advanced economies and that its international transmission is consistent with the working of long-run uncovered interest rate parity and a large dollar shortage shock during the COVID-19 period. In emerging markets, the QE impact on bond yields is much stronger and its transmission to exchange rates is qualitatively different than in advanced economies. The GVAR evidence that the authors report illustrates the Fed’s pivotal role in the global transmission of long-term interest rate shocks, but also the ample scope for country-specific interventions to affect local financial market conditions, even after controlling for common factors and spillovers from other countries. The GVAR evidence also shows that QE interventions can have sizable real effects on output driven by a very persistent impact on long-term interest rates.

The purpose of this study is to contribute to existing financial literature within a less researched area through a systematic, organized, and holistic approach. This study advances the notion of considering terrorist attacks as a heterogeneous group of events by employing a multidimensional approach. The event study methodology was used to investigate the impact of 46 terrorist attacks occurring on the soil of OECD countries since 1990 on stock markets in US, UK, Spain, and Denmark. Thereby, terrorist attacks are considered as events conveying information to financial markets, which is processed by investors and subsequently reflected in security prices. This chapter is the first contribution within financial literature to distinguish and categorize terrorist attacks through several dimensions and investigate the effect of various characteristics on stock markets. The multidimensional analytical approach consisted of six dimensions, which included an examination of the national stock markets, differences across industries, the underlying threat characteristics, the size of the attack, and the development over time and geospatial aspects. It is concluded that terrorist attacks exhibiting international threat characteristics result in significantly larger and boundary spanning negative abnormal returns, which impact stock markets beyond the country in which the attack occurred. Additionally, the size of the terrorist attack amplifies the negative impact on stock markets. However, while the impact on stock markets was found to be immediate indicating that stock markets are quick and efficient in absorbing new information, the negative impact is likely to evaporate within five trading days.

Several studies and research related to event study and terrorist bombing acts have different conclusions. Moreover, research on terrorist bombings worldwide has never been conducted. Hence, this research aims at examining the impact of terrorist bombing using the abnormal return variables by looking at all bombings around the world in the past 10 years. This research uses a paired t-test by looking at the abnormal return before, during, and after a bombing act. Results show no significant difference between the abnormal return before, during, and after bombing acts.

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.

This paper uses two complementary approaches to estimate the effect of right-to-work (RTW) laws on wages and unionization rates. The first approach uses an event study design to analyze the impact of the adoption of RTW laws in five US states since 2011. The second approach relies on a differential exposure design that exploits the differential impact of RTW laws on industries with high unionization rates relative to industries with low unionization rates. Both approaches indicate that RTW laws lower wages and unionization rates. Under the assumption that RTW laws only affect wages by lowering the unionization rate, RTW can be used as an instrumental variable (IV) to estimate the causal effect of unions on wages. In our preferred specification based on the differential exposure design, the IV estimate of the effect of unions on log wages is 0.35, which substantially exceeds the corresponding OLS estimate of 0.16. This large wage effect suggests that RTW may also directly affect wages due to a reduced union threat effect.