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It is well known that government plays an important role in the business activities of Chinese firms. Less certain is the effect this influence has on the wealth of those firms’ shareholders. We contribute to the literature by analysing stock market reactions to announcements by Chinese firms of overseas mergers and acquisitions (OMAs). OMAs are of particular interest because there can exist a conflict between the interests of the public sector in acquiring overseas assets, and the interests of the private sector in maximizing shareholder wealth. Our main dataset consists of 213 observations of 157 OMA events that occurred between 1994 and 2009, using share market returns from the Shanghai, Shenzhen, Hong Kong and US markets. The aggregation of share price data across multiple markets, and the listing of firms in multiple exchanges, raise econometric issues for the standard event‐study methodology. To address these, we use a new, feasible generalized least squares (GLS) procedure developed by Gu and Reed (2012). On the basis of an analysis using both aggregated and disaggregated samples, and of daily and cumulative abnormal returns, we find consistent evidence that (i) Chinese OMAs have not lowered the wealth of shareholders of Chinese acquiring firms, and (ii) shareholders of Chinese acquiring firms have not fared worse under under China's ‘Go Global’ policy of encouraging outward investment by Chinese firms.

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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 OMAs 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.