The impact of Facebook-Cambridge Analytica data scandal on the USA tech stock market: An event study based on clustering method

TL;DR

This paper investigates the effects of the Facebook-Cambridge Analytica scandal on U.S. tech stocks using clustering and event study methods, revealing sector-wide resilience but specific negative impacts on Facebook after earnings reports.

Contribution

It introduces a clustering-based approach to better identify directly affected firms and reduce randomness in event studies of industry-specific scandals.

Findings

Significant positive CARs across U.S. tech firms post-scandal

Facebook's earnings report showed a notable negative effect

Clustering improved identification of affected companies

Abstract

This study delves into the intra-industry effects following a firm-specific scandal, with a particular focus on the Facebook data leakage scandal and its associated events within the U.S. tech industry and two additional relevant groups. We employ various metrics including daily spread, volatility, volume-weighted return, and CAPM-beta for the pre-analysis clustering, and subsequently utilize CAR (Cumulative Abnormal Return) to evaluate the impact on firms grouped within these clusters. From a broader industry viewpoint, significant positive CAARs are observed across U.S. sample firms over the three days post-scandal announcement, indicating no adverse impact on the tech sector overall. Conversely, after Facebook's initial quarterly earnings report, it showed a notable negative effect despite reported positive performance. The clustering principle should aid in identifying directly related companies and thus reducing the influence of randomness. This was indeed achieved for the effect of the key event, namely "The Effect of Congressional Hearing on Certain Clusters across U.S. Tech Stock Market," which was identified as delayed and significantly negative. Therefore, we recommend applying the clustering method when conducting such or similar event studies.