Abstract
There is often an interest in business disciplines in investigating whether the public announcement of a business-related event has a statistically significant impact on the market value of the firm. The Capital Market reaction is typically assessed based on cumulative abnormal return (CAR), which is the sum of abnormal returns over the event window. The event study methodology is a popular approach for exploring the occurrence of CAR. Most previous event studies have used confirmatory approaches, which require the prior explicit specification of all hypotheses. However, in many situations, it could be difficult for the researcher to identify every relevant hypothesis that might be testable, particularly the local hypotheses. In this chapter, we present an exploratory data analysis methodology approach that involves the use of decision tree generation together with statistical hypothesis testing. We use two previous studies to demonstrate this methodology.
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Notes
- 1.
In some cases, steps 3 and 4 may be interchanged. In the traditional event study approach, step 3 may precede all the previous steps and may even be the reason for the research project in general. This is because the research interest in understanding how the predictor variables may influence CAR may be the reason for the entire project in the first place.
- 2.
For an event such as Announcement of Security Breaches, abnormal represents CAR with negative values and normal represents CAR with zero or positive value.
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Acknowledgments
Some of the materials in this chapter previously appeared in ‘Effects of Firm and IT Characteristics on the Value of e-Commerce Initiatives: An Inductive Theoretical Framework,’ Information Systems Frontiers 14:2, 237–259 (2012).
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Andoh-Baidoo, F.K., Amoako-Gyampah, K., Osei-Bryson, KM. (2014). Application of a Hybrid Induction-Based Approach for Exploring Cumulative Abnormal Returns. In: Osei-Bryson, KM., Ngwenyama, O. (eds) Advances in Research Methods for Information Systems Research. Integrated Series in Information Systems, vol 34. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-9463-8_5
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