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While the short-term performance of M&A described above measures the reaction of the market around the announcement date, the long-term performance measures whether the short-term returns found transpire into the long-term share price improvements for these bidder firms. There is evidence from prior studies that long-term stock performance measurement is sensitive to both the methodology and benchmark used (Fama, 1998). It is also worth noting that, “... the estimation of abnormal returns over long event windows is a matter of some intense debate” (Sudarsanam & Mahate, 2006, p. S15). On one hand, (Barber & Lyon, 1997) and (Lyon et al., 1999) recommend the use of the buy-and-hold abnormal returns (BHAR) for estimating long-term abnormal returns because it measures investor experience. On the other hand, (Fama, 1998) recommends the calendar-time portfolio returns (CTPR) approach arguing that BHAR method worsens the ‘bad model’ problems by compounding. Given all the controversies surrounding measuring long-term abnormal returns, we adopted the two alternative methods: BHAR and calendar-time portfolio returns (Bouwman et al., 2009).

5.5.3.1Buy-and-Hold Abnormal Returns (BHAR)

Following Barber and Lyon (1997), we adopt the BHAR methodology to examine the performance of M&A and also examine various explanations for the long-term performance of Chinese bidders related to corporate governance, firm-specific and deal-specific attributes. BHAR methodology is one of the widely used and according to Lyon et al. (1999) is the appropriate method because it “specifically measures investor experience” (p. 198).

BHAR is calculated as the difference between long-term compound buy-and-hold abnormal returns of sample firms and the compound buy-and-hold abnormal returns of an appropriate benchmark (Barber & Lyon, 1997). Literature has identified various methods used for

113 calculating benchmark returns such as reference portfolios, control firm portfolio or the Fama-French three-factor model (Barber & Lyon, 1997). However, concerns have been raised by researchers on the BHAR (Barber & Lyon, 1997; Kothari & Warner, 1997; Lyon et al., 1999) such as rebalancing bias, new listing/survivor bias and skewness bias. These are briefly discussed below.

New listing and survivor bias happen because sample firms are tracked for a long post- event period, while firms in the reference portfolio typically include firms that begin trading after the event taking place. Rebalancing bias arises because the returns of sample firms are compounded without rebalancing, whereas the returns of a reference portfolio, for example, an equally weighted market index, are typically calculated assuming periodic rebalancing. Finally, the Skewness bias arises because the distribution of long-term abnormal stock returns is positively skewed. This final cause contributes to the misspecification of test statistics. In general, the new listing bias creates a positive bias in test statistics, and the rebalancing and skewness bias creates a negative bias. Cross-sectional dependence in sample observations and a poorly specified asset pricing model is mentioned as additional sources of misspecification. This study applies suggested remedies identified in the literature to address some of the BHAR problems.

5.5.3.2Creating reference portfolios

To control for rebalancing and new listing bias problems, sample firm long-term abnormal returns were compared to benchmark reference portfolios created using such key factors such as stock market, systematic risk, size and book-to-market ratio as envisaged by (Fama & French, 1993; Sudarsanam & Mahate, 2006). Following Boateng and Bi (2013), the benchmark used for this study is the ten size and fifty size/book-to-market ratio reference portfolios built based on all Chinese listed firms (excluding sample firms in the spirit of (Loughran & Ritter, 2000)) A-Shares recorded in CSMAR database between 2002 and 2011. Adjusting for size and book-to-market effects is important since M&A sample are not distributed equally across the size and book-to-market spectrum. Adjusting for size and book- to-market value also focuses on the impact of book-to-market of broadly comparable size control firms. Appendix D explains in detail the process followed to create ten-size and fifty- size- and- market-to-book value ratio reference portfolios.

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5.5.3.3Estimating long-term abnormal returns

To estimate the long-term abnormal returns, the present study follows the methodology proposed by Lyon et al. (1999). First, the returns on the firms constituting the reference portfolio are compounded and then summed up across the firms using the following:

𝑅

= ∑∏

[1 + 𝑅

] − 1

𝑛

𝑅𝑝𝑡, is the preference portfolio return, 𝑅𝑗𝑡 is the month t simple return on firm j, 𝑛𝑠is the

number of firms traded in the month s, the beginning of the return calculation and T is the investment horizon in months. We calculate the monthly returns for each of the fifty size and book-to-market reference portfolios by averaging the monthly returns across all securities in a size and book-to-market decile.

Finally, the difference between the returns of the bidder firm from the sample and that of the equally weighted matched reference portfolio is computed to derive the BHAR. BHAR is calculated for 24 months post-M&A period, starting with one month after the announcement date.

𝐵𝐻𝐴𝑅

= ∏

(1 + 𝑅

) − 1 − 𝑅

𝑡=𝑠 𝑅_𝑖𝑡, is the month 𝑡 return for firm 𝑖.

5.5.3.4Bootstrapped skewness-adjusted statistic

Sudarsanam and Mahate (2006), highlight that BHARs are positively skewed and this problem may increase as the holding period length increases and may also have a weakening effect on statistical tests. We minimise the skewness problem, as recommended by Lyon et al. (1999), by drawing inferences based on bootstrapped skewness-adjusted t-statistic. We take 1000 randomly selected subsamples of size n/4 from the original sample. Calculate skewness- adjusted t-statistics for each subsample. Compute the standard deviation for each subsample, that is, 1000 t-statistics. Standardise the t-statistics of the empirical sample by dividing through the standard deviation of the bootstrapped 1000 t-statistics. Compare the resulting value to the corresponding critical value of the standard normal distribution. Mitchell and Stafford (2000) argue that M&A announcements are not independent events and therefore to preserve the dependence structure of the original data, the 1000 subsamples

115 are clustered by bidder firm as outlined by Horowitz (2003) and implemented by Bouwman et al. (2009).