Information asymmetry, market segmentation, and cross-listing: Implications for event study methodology

• Published in: Journal of Asian economics Vol. 28; pp. 28 - 40
• Main Authors: Gu, Lulu, Reed, W. Robert
• Format: Journal Article
• Language: English
• Published: Greenwich Elsevier Inc 01.10.2013; Elsevier Science Ltd
• Subjects: Abnormal returns; Acquisitions & mergers; China; Event study; International markets; Market segmentation; Mergers and acquisitions; Multiple listings; Securities markets; Studies; China; C12; Multiple listings; China; G34; G15; G14; Event study; Mergers and acquisitions
• ISSN: 1049-0078; 1873-7927
• DOI: 10.1016/j.asieco.2013.04.009

•A large proportion of firms list their shares on multiple exchanges.•We generalize event study methodology to handle instances of multiple listings.•The methodology is applied to an event study of Chinese overseas mergers and acquisitions.•The empirical results are compared to conventional event study methodology.•Our generalized approach produces significantly different and – we argue – better results. This paper connects three subjects related to international financial markets – (i) information asymmetry, (ii) market segmentation, and (iii) cross-listings – and highlights their implication for event study methodology. When firms list equities on more than one exchange, and the exchanges are characterized by different information sets, a problem arises as to which exchange(s) to include in the event study sample. If market segmentation impedes the arbitrage of these multiple responses, then the use of a single listing (for a firm that is cross-listed) can yield abnormal return estimates that are biased. In such circumstances, using returns from all the markets in which a firm's securities are listed not only increases the sample size (often an important consideration when undertaking event studies in emerging markets), but also enables full-information abnormal return estimates to be obtained. What is required is a method that extracts the independent information from each listing while counting the common information only once. In this paper, we develop an estimation procedure that achieves these twin objectives. We then apply our approach to an event study of Chinese overseas mergers and acquisitions, and compare results from alternative samples and estimators. We demonstrate that including return data from cross-listings of the same firm can result in substantially different conclusions.