英语硕士论文Literature Review范文----文献综述论文-->正如上面提到的，短期事件研究可以在实证检验的有效市场假说被应用。
在另一方面，使用长地平线（1-5年收购后的）数据事件的研究可以应用到评估并购对企业的长期股市表现一个事件的影响。
这密切关系到著名的“合并后的绩差谜”在阿格拉瓦尔及谢斐（2000 ）提出的。
在这篇文章中，他们记录了各种的研究人员对在股市收购公司的长远表现不佳的结论。
不过，本节着重在研究长远的性能使用不同的经验方法。
Literature Revieentioned above, short-run event study can be applied in the empirical test for EMH. On the other hand, event studies using long horizon (1-5 years post acquisition) data can be applied to evaluate the effect of M A event on long-term stock market performance of firms. This closely relates to the famous “post-merger underperformance puzzle”proposed in Agrap; Jaffe (2000). In that article, they documented the conclusions made by various researchers on the long-run underperformance of acquiring firms in the stock market. Hopirical methods used by various authors in examining the long-run performance. The first difficulty encountered in long-run event studies is the calculation of expected returns. Different choices of benchmark models could lead to exactly opposite conclusions, since small measurement errors accumulate in the long-run (Kothari and itchell and Stafford(2000) also shoes higher than the appropriate t-statistics. Fama (1998) advocates the use of calendar time portfolio in correcting for cross-sectional correlation problem. On the other hand, Barber and Lyon (1997) proves that the use of reference portfolio or asset pricing model in calculation of abnormal returns suffers three types of biases in long-run study. The proposed solution is to match sample firms to non-sample parable firms of similar market capitalization and market-to-book ratios. The usefulness of another benchmark model -- the Fama- French three-factor model –seems to be less efficient than the previous tethods (Barber and Lyon 1997, Brav 2000).Another difficulty arises from houlate abnormal returns over time. It is mon to use cumulated abnormal returns (CAR) for short-run study, but Barber and Lyon (1997) argues that this measure is conceptually flaeasure ore sensible based on conceptual grounds is the buy-and-hold abnormal returns (BHAR) etric sums of abnormal returns rather than arithmetic sums as in CAR. Ho simulation shoal returns more often. To correct f-->or such bias, Lyon, Barber and Tsai (1999) proposes the bootstrappedskepirical study in the current paper is focused on short-run period around the event date. Tine the significance of abnormal returns. The first one is to test ent of M A on share performance is significant or not, ation content of MA announcement. This is done by testing for the significance of abnormal returns on single days. The second one is to test for the significance of the event over a short period. This closely relates to the leakage of information ahead of such events and the question of hoarket responds to such events. This is done by testing for the significance of cumulative abnormal returns for a short interval. Since ance of all the sample panies, the individual abnormal return during the event period is averaged across 91 firms, as ulative abnormal return.Define t as the day measured in event time. The date that each acquiring firm announces its merger/acquisition corresponds to t = 0. For each firm, closing share price and contemporaneous FTSE All share index data from t = -100 (100 trading days before the firm’s merger announcement) until t = +5 (5 days after the announcement) are collected. Let and denote the simple daily return for security i and market index at day t, respectively. In the current paper, the estimation period runs from t = -100 to t = -10, and the event period runs from t = -5 to t = +5.The first step is to measure abnormal return, denoted by , for firm i at day t in the event period. Ordinary least squares (OLS) is applied to estimate the market model for firm i, that is, OLS is used to estimate alpha () and beta () parameters for each firm using daily returns from the estimation period, and the abnormal return for each day in the event period is calculated using the folloal return is often affected by firm-specific characteristics, the abnormal returns from equation (1) are averaged for analysis based on the ple of firms. For a given day t in the event period, let denote the average daily abnormal return over all the sample firms, and denote the cumulative average daily abnormal return from the first day in event period (-5) till day t. Similarly, the cumulative daily abnormal return for a specific firm i, denoted by , can be defined. Therefore,Folloal return is then normalized by the sample standard deviation for the daily return--> of corresponding security, denoted by , to yield a standardized abnormal return, (See also Serra 2002):Averaging it over all sample firms, the average standardized abnormal return for day t, denoted by , can be calculated. The test statistic for a given day in the event period (-5 through +5) is as follople size of firms, the central limit theorem that under the independent and identically distributed condition, the statistic in equation (6) should be standard normally distributed asymptotically. The null hypothesis for the first testis that the average standardized abnormal return for day t is 0, i.e. =0. The hypothesis test can also be constructed concerning a period of time. Let , (-530). The null hypothesis for the second test is that the cumulative average abnormal return during the period [,] is 0, i.e. =0.。
