share prices: The companies investigated in this study

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Event study methodology

The event study methodology was introduced by Fama, Fisher, Jensen and Roll in 1969. The method was used to test the time it takes for share prices to change due to specific types of new information. This is a type of semi-strong form test and semi-strong form tests were developed in order to extend the research field about the efficient market hypothesis (Malkiel & Fama, 1970).
Fama et al. (1969) tested how share prices changed with regards to share splits. In order to conduct their research, the authors needed to isolate the effect that splits perhaps could have on returns. Hence, the general market conditions needed to be abstracted from the returns during the time periods that were close to the share splits. Therefore, the authors used a linear regression model to explain the relationship between the general market conditions and the monthly rates of returns of the different shares. Next, the authors calculated the average difference in share prices between the months due to the event of a share split. They also calculated the cumulative average residual, which corresponds to the cumulative effects of abnormal behaviour of the returns in the months close to the split month.
Fama et al. (1969) stated that one of the assumptions of the error term in the linear regression model used in event study methodology is that the error terms are serially independent. In contrast to this, the authors found strong evidence that the expected values of the error term are rather not zero during the time surrounding the share splits. Consequently, the error term in the model is therefore not valid. Moreover, they found evidence that the separate effect on returns from the announcement of a split was completely mirrored by the share price, at least in the end of the month when the split occurred, but more probably right after the announcement. The authors also concluded in their study that the market is efficient (Fama et al., 1969).
Boehmer, Musumeci and Poulsen (1991) stated that the event study methodology has become the most important method to use when investigating the effect of an event on returns of shares. An event can be defined as “anything that results in new relevant information” (McWilliams & Siegel, 1997, p. 630). Examples of such events are implementation of new corporate governance reforms (Black & Khanna, 2007), new patents (Austin, 1993), announcement regarding mergers and acquisitions (Ma, Pagan & Chu, 2009), and privacy breaches (Acquisti, Friedman & Telang, 2006).
Event study methodology is based on three assumptions (i) markets are efficient, (ii) the event is unexpected, and (iii) confounding effects are isolated (McWilliams & Siegel, 1997). The first assumption, market efficiency, means that the effects, positive or negative, of an event should be directly reflected in the company’s share prices. The second assumption implies that an event is considered to be unexpected if the market is completely unaware of the information prior to the event. Sometimes information about the event is leaked to investors beforehand, which makes the results obtained from the event study invalid. The third assumption claims that no confounding effects can have appeared during the time of the event window for the results to be valid. Confounding effects include all other kinds of events that can have an effect on share prices, for instance the announcement of a new company product or a change in a key executive (McWilliams & Siegel, 1997).
The goal with the event study methodology is to come up with and calculate the cumulative abnormal returns (CARs) for the event that is investigated (McWilliams & Siegel, 1997). The CAR measure has been used in several studies investigating the effect of events concerning one company, on share prices of connected companies (Jordan, Peek & Rosengren, 2000; Donelly, 2008; Jamal, Liu & Luo, 2018; Kang, 2008).

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The event study period

The event date is the date when the investigated event occurred (Figure 1). The event date can be quite complicated to determine because sometimes there is a distinction between when the event occurred and when the event was announced to the market (Henderson, 1990). The event window can be defined as “the event day plus and/or minus some number of days, weeks or months when the sample firms’ returns are observed to see if anything unusual happened” (Henderson, 1990, p. 284; Figure 1). A short event window, normally two days, are chosen in event studies where the date of the event can be accurately determined. A short event window indicates more valid abnormal returns (Armitage, 1995). In addition, a short event window is more complied with the efficient market hypothesis that assumes that the effect of an event will be directly shown in the market (Ding, Lam, Cheng & Zhou, 2018). Choosing a long event window will therefore more easily abandon the underlying assumption of the efficient market hypothesis. The event window can be hard to specify because the range needs to include all the relevant changes in share prices without reaching too long into the future (Kothari & Warner, 2007).
The estimation window is needed in order to be able to calculate the normal returns for the time period following the estimation window (Kang, 2008; Figure 1). Normal returns are the returns that would be most probable if the event would not have occurred (Henderson, 1990). According to Peterson (1989), a sufficient estimation window should range between 100 and 300 trading days. However, estimation windows lasting 250 trading days, which corresponds to one year, are most often chosen (Armitage, 1995). For the results to be as valid as possible, the estimation window and the event window should not be overlapped (MacKinlay, 1997). The estimation window and the post event window is equal in time length, since the data collected corresponds to the same number of days before and after the event date (Figure 1).
The event study methodology has developed in several aspects since its beginning in 1969. The methodology has been divided into two approaches, namely short term event studies and long term event studies. However, a formal distinction between the two does not yet exist. Kothari and Warner (2007) did an overview of event study methodology where they investigated 565 papers published between 1974 and 2000. The authors found that the short term event studies were used when the event window was one year or shorter. Long term event studies (with event windows longer than one year) on the other hand, has also been widely used but researches have noticed several limitations such as the lack of validity of the statistical results (Eckbo, 2007) and the increased risk of confounding events (Ding et al., 2018).
Fama et al. (1969) and Brown and Warner (1980) used data based on monthly returns in their studies. A few years later, in 1985, Brown and Warner along with other researchers began to base their data on daily studies instead (Corrado, 2011). French, Schwert and Stambaugh (1987) stated that monthly returns failed to reflect the individual fluctuations, which can be obtained from daily returns that show more frequent changes. Moreover, Kothari and Warner (2007) mentioned that daily returns result in more informational research about the impact of announcements and also give more accurate measurements of abnormal returns, compared to when monthly returns are used.

1 Introduction
1.1 Background
1.2 Problem
1.3 Purpose
2 Frame of references
2.1 Theoretical framework
2.2 Literature review
2.3 The companies investigated in this study
3 Research methodology
3.1 Data collection
3.2 Methods
3.3 Confounding effects
4 Results
4.1 Results for individual companies