Event study methodology

Consistent with M&A studies, this study employs the event study to measure stock price effects associated with M&A announcements. McWilliams and Siegel (1997), define event

105 Table 5.5: Pearson’s correlation matrix for the variables

State shares Legal- person shares Executive shares Board size Board independence CEO role duality Total assets Financial leverage Tobin's q Return on assets Stock price run-up Sales growth High- tech Deal value Private target Legal-person shares -0.529 (0.00) Executive shares -0.157 0.013 (0.00) (0.54) Board size 0.166 -0.136 -0.052 (0.00) (0.00) (0.02) Board independence -0.228 -0.023 0.075 -0.098 (0.00) (0.30) (0.00) (0.00)

CEO role duality -0.125 0.061 0.119 -0.119 0.051

(0.00) (0.01) (0.00) (0.00) (0.02) Total assets 0.166 -0.272 -0.091 0.225 0.118 -0.088 (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) Fin. leverage 0.001 0.038 -0.089 0.024 -0.031 -0.030 0.052 (0.96) (0.08) (0.00) (0.27) (0.15) (0.17) (0.02) Tobin's q -0.051 -0.092 -0.132 0.055 0.092 -0.075 0.298 0.303 (0.02) (0.00) (0.00) (0.01) (0.00) (0.00) (0.00) (0.00) Return on assets 0.036 0.003 0.092 0.032 0.089 0.020 0.159 -0.269 -0.369 (0.10) (0.89) (0.00) (0.15) (0.00) (0.37) (0.00) (0.00) (0.00)

Stock price run-up -0.095 -0.021 0.054 -0.026 -0.004 0.013 0.039 -0.058 0.000 0.108 (0.00) (0.34) (0.01) (0.24) (0.87) (0.54) (0.07) (0.01) (0.98) (0.00) Sales growth -0.031 0.061 -0.008 -0.043 0.031 -0.005 -0.066 -0.059 -0.015 -0.010 0.025 (0.16) (0.01) (0.71) (0.05) (0.16) (0.83) (0.00) (0.01) (0.50) (0.66) (0.26) High-tech -0.043 0.025 0.063 -0.024 0.010 0.037 -0.051 -0.069 -0.096 0.026 -0.021 -0.016 (0.05) (0.26) (0.00) (0.28) (0.65) (0.09) (0.02) (0.00) (0.00) (0.24) (0.34) (0.47) Deal value 0.112 -0.140 -0.078 0.076 0.052 -0.062 0.340 -0.055 0.092 0.024 0.037 0.090 -0.039 (0.00) (0.00) (0.00) (0.00) (0.02) (0.00) (0.00) (0.01) (0.00) (0.27) (0.09) (0.00) (0.08) Private target -0.011 0.028 -0.028 0.013 0.003 -0.026 0.012 0.037 0.034 0.003 0.029 0.025 -0.009 0.017 (0.63) (0.20) (0.21) (0.55) (0.90) (0.24) (0.58) (0.09) (0.13) (0.89) (0.19) (0.25) (0.67) (0.45) All cash 0.008 0.076 0.034 0.053 -0.056 -0.002 0.106 0.063 -0.119 0.191 -0.005 -0.126 0.033 -0.351 -0.006 (0.72) (0.00) (0.12) (0.02) (0.01) (0.94) (0.00) (0.00) (0.00) (0.00) (0.83) (0.00) (0.14) (0.00) (0.78)

106 studies as “... powerful tools that can help researchers assess the financial impact of changes in corporate policy” (p. 626). Event studies have been the primary methodology used to assess the effect that the occurrence of an event has on the returns of a firm’s common stock price since the seminal works of (Ball & Brown, 1968; Fama, Fisher, Jensen, & Roll, 1969; Seiler, 2000, p. 101). Stock prices are also supposed to reflect the true value of firms because they are assumed to reflect the discounted value of all future cash flows and incorporate all relevant information. Therefore, event studies, which are based on stock price changes, should measure the monetary impact of a change in corporate policy, leadership, or ownership more effectively than a methodology based on accounting returns. Furthermore, the event study method is relatively easy to implement, because the only data necessary are the names of publicly traded firms, event dates, market returns and stock returns.

5.5.1.1Assumptions

Conclusions drawn by an event study are only valid if the researcher can confidently identify the abnormal returns associated with the event (McWilliams & Siegel, 1997). It is appropriate to use this method when the below-listed three assumptions as identified by McWilliams and Siegel (1997) are likely to be valid. The first is that the markets are efficient in the sense that the current stock prices incorporate “all the publicly available information along with private information” (Mahmood, Xinping, Shahid, & Usman, 2010, p. 91). The Chinese markets resemble weak-form efficiency in which price of stocks instantly and fully reflect all information of the past prices and, that the future prices cannot be predicted and used to beat the market (Mahmood et al., 2010). The second is that the event was unanticipated and that the market will only know about it when it is announced in the press or through corporate releases. Thus, abnormal returns are a result of a market reaction to the ‘new’ information (McWilliams & Siegel, 1997). The third is that there are no confounding effects during the event window. These are events such as dividend declaration, the announcement of a new product and changes in management that might influence the research event during the event window. The study uses a short 5-day window period to reduce confounding effects or will isolate and eliminate the effects of other events on the research event (Masulis et al., 2007).

107 5.5.1.2Event periods

An event study is conducted over four distinct date windows; the event date, estimation period, the event window and the grace period (see Figure 5.1 below). The event date must be carefully defined because of its capability to increase the statistical power of the event study technique and its misidentification can significantly affect findings (Brown & Warner, 1985; Henderson, 1990). Prior studies that use the date of the merger completion as the event date find no significant evidence of wealth effects (Mandelker, 1974) whereas those that use the initial announcement date find significant effects (Asquith, Bruner, & Mullins, 1983). The event date, denoted by 0, is the date on which the information about the merger or acquisition is first announced through the financial press or corporate releases. Following Asquith et al. (1983), the current study uses initial announcement date as provided by the CSMAR database, as the event date.

The second window of interest is the estimation period window also known as the pre- announcement window which is incorporated to capture any information leakages about the event, for example, insider trading. Prior studies apply different estimation periods (see Appendix A). There seems to be some agreement that a shorter estimation period may not truly capture the relationship between the share return and the market return resulting in a bias of the model parameters. If there is any unexpected shock for the stock during a short estimation period, this can significantly affect the estimation of the model parameters. Some researchers have even taken this further by prescribing what they think should be standard estimation period ranges. Bartholdy, Olson, and Peare (2007) recommend an estimation period between 200 and 250 days and, Peterson (1989) and Armitage (1995) between 100 and 300 days when using daily returns data. The present study uses a 200-day period from event day -220 to event day -21. This has been influenced mainly by previous studies (see Appendix A).

The third window of interest is the event window which is the period around the announcement date, which incorporates pre-announcement days and post-announcement days, adopted to cover for uncertainty over the exact time of publication and public dispersion of information. The event window is also adopted as the benefits of the merger to bidder firms are likely to be reflected in stock values around the time when an acquisition programme is initiated (Kumar & Panneerselvam, 2009). Prior studies use different event window periods. Houston and Ryngaert (1994) and Goergen and Renneboog (2004) argue

108 that a short event window may miss some run-up returns especially in cases of insider trading or information leakage before the press release.

For the short-term, this study uses the five days before and five days after the announcement date as the event window period. This is due to the prevalence of insider trading in China (Tuan et al., 2007). The eleven-day window also tests for any delayed response by the market to the event due to its weak form of market efficiency. This gives the event window of eleven days [-5, +5] which is consistent with prior studies event windows (see Appendix A). Most long-term studies use event windows ranging from twelve months to sixty months (see Appendix B). This thesis uses twenty-four months (T24) as the event window, informed

by literature and the need to limit confounding effects from other events. Figure 5.1: Event study setup

Panel A: Short-term event study setup

-220 -21 -5 +5

0 (event date)

Estimation window (200 days) Grace period (16 days) Event window (11 days)

Note: Panel A illustrates the short-term event study setup. More specifically, (-220, -21) represents the estimation window, (-20, -5) is the grace period and 0 is the announcement date. (-5, +5) represents the event window, that is five trading days before the announcement date and five trading days after the announcement date.

Panel B: Long-term event study setup

Event month

T0 T1 T12 T24 T36

Event window (months)

Note: Panel B illustrates the long-term event study setup. T0 represents the announcement month and T1 represents a

period of one month from the announcement month which also marks the beginning of the long-term horizon. T12, T24

and T36 represent twelve, twenty-four and thirty-six months from the announcement month, and mark the end of the long-

term horizon.

The final window of interest is the grace period. The above leaves a grace period of 16 days (see Rosenstein & Wyatt, 1997). The period of 16 days prior to the announcement is excluded from the estimation period to ensure that the data are not contaminated by leaks

109 during the run-up to the official announcement date (Tuan et al., 2007). Figure 5.2 below presents the event study setup for this study.