The Long Run Performance Of Uk Takeovers

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02 Nov 2017

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Results

Here, I would examine the long run performance of UK takeovers by analysing, explaining and interpreting the results obtained from the tests that have been carried out on the three sample sets employed in this paper. The results would be explained and presented according to the samples to which they belong.

The General Sample

As explained in the data section, the general sample here refers to all M&A transactions in the UK between 1992 and 2005, by UK listed firms. On this sample, I have calculated and tested abnormal returns using an event study methodology, a buy and hold abnormal returns methodology, and the Jensen-alpha methodology. Results from three methods are explained below:

Event Study Methodology

The event study methodology has been explained in the methodology section, and as highlighted there, long-run abnormal return calculations are sensitive to model choice. For this reason, I have considered three models – the Market Model (MM), the Capital Asset Pricing Model (CAPM), and the Fama French Three Factor model (FF).

Table x (see appendix) and the diagram below show the average abnormal returns (AARs) and their associated t-statistics as calculated by the Market Model. As can be seen, the AARs are volatile but seem to trend downwards as we move further away from the event date which is in keeping with findings of negative long-run abnormal returns in other literature.

To help explain whether the trend and abnormal returns observed above are significant, we look at the cumulative abnormal returns (CARs), the cumulative average abnormal returns (CAARs) and the t-statistics. These can all be seen in the table below [1] :

 

CAAR



T-Stat

Sign Test

3 to 14

4.2815%

0.10436506

2.6803*

2.2251**

15 to 26

2.0910%

0.1054926

1.3020

2.324**

27 to 36

-6.3725%

0.10199419

-4.0354

-2.1262

3 to 36

0.0000%

0.31185185

0.0000

-0.9889

Taking months 0, 1 and 2 as making up the event window, months 3 to 14 make up the first post-acquisition year. The CAAR over this period is 4.2815% which is significant at the 1% significant level. Using the sign test as a second test of statistical significance, the first post-acquisition year CAAR is also significantly positive, but at the 5% significance level. The second and third post-acquisition year CAARs are 2.0910% and -6.3725% respectively with only the latter being significant (according to the t-test) at the 1% significance level. The second and third year sign tests are also significant. Looking at the entire post-acquisition period, the CAAR is 0%.

For the CAPM benchmark, the CAARs, variance, T-stats and Sign tests are given in the table below:

 

CAAR



T-Stat

Sign Test

3 to 14

5.8846%

0.10725039

3.6339*

3.9063*

15 to 26

3.8270%

0.10900917

2.3441**

2.9174*

27 to 36

-4.8385%

0.10627717

-3.0016

-0.0494

3 to 36

4.8730%

0.32253673

1.7353***

2.7196*

As can be seen in the table above, over the three post-acquisition periods, the CAARs are 5.8846%, 3.8270% and -4.8385% respectively. These are all significant according to the t- and sign-tests except for the third post-acquisition year where the sign test is insignificantly negative. Looking at the entire post-acquisition period, the CAAR is 4.8730% which is significant at the 10% level according to the t-stat and significant at the 1% level using the sign test.

For the Fama French model, the results can be seen in the table below:

 

CAAR



T-Stat

Sign Test

3 to 14

3.3281%

0.09557988

2.1771**

0.6428

15 to 26

2.5360%

0.10048805

1.6179

1.9284***

27 to 36

-1.4729%

0.0944426

-0.9693

-0.6428

3 to 36

4.3912%

0.29051053

1.6477***

-0.7417

The CAAR over this period is 3.3281% which is significant at the 5% significant level. The CAARs for post-acquisition years 2 and 3 are 2.5360% and -1.4729% respectively. However both these average abnormal returns are insignificantly different from zero. Looking at the entire post-acquisition period, the CAAR is 4.3912% and is significant at the 10% significant level. Using the sign test as a second test of statistical significance, only the second year post-acquisition returns are statistically significant (at the 10% significant level). Unlike the t-stat, the sign test for the entire post-acquisition period (3,36) is negative although both are similarly insignificant.

To compare all the models over the entire post-acquisition period, their AARs are shown in the diagram below:

As can be seen, the AARs are getting more significantly negative as time goes by. In the earlier years, CAPM seems to produce the stronger results while in the latter years, the MM does this. For further analysis, the graph below compares the AARs in the post-acquisition period to the AARs in the pre-acquisition period. This would help us compare post-acquisition AARs to normal period (non-event) AARs. From the graph it can be seen that the pre-event AARs are positive while the post-event AARs get increasingly negative with time.

Buy & Hold Abnormal Returns

The second method used in calculating abnormal returns is the buy & hold abnormal returns method. As explained in the methodology section, this method calculates its abnormal returns by assuming one invests in a portfolio and holds this until the end of the period under consideration. To start this analysis, I examine the returns on a portfolio of all the companies in my general sample data set, assuming I invest in the event month zero and hold this until the end of post-acquisition month 36. The buy & hold abnormal returns (BHAR) here increase from an insignificant 0.55% in month 3, to a high of 9.21% (significant at the 1% significance level) in month 26, and finally to 6.8% (significant at the 10% significance level) in month 36. This can be shown in the diagram below:

For further analysis, I create new 12-month buy & hold portfolios to better observe how each sub period actually performs. A graphical representation of these portfolios is below:

From the diagram it is clear to see that the highest buy & hold abnormal returns can be obtained in the first post-acquisition year. The graph also re-iterates the finding under the event study methodology that the abnormal returns become more significantly negative as time goes by.

As a final form of analysis, I would like to observe the BHAR over time assuming someone invests in a portfolio consisting of all my event stocks and holds this until the end of the period. The result is shown below:

As can be seen above, the BHARs increase over time until about 19 months after the acquisition, at which point the BHARs begin diminish.

The Jensen-Alpha Approach



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