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Chapter 5. Conclusion
5.1. Summary of the Study
In this research, I use a list of M&A transactions that took place in the US
across industries. With the patent and citation data provided by NBER dataset, I
shed lights on the relation between innovation level and the acquirers’ post-merger
stock performance. In comparison with internally developed R&D investment,
resorting M&A market as external source of innovation provides enterprises an
effective and flexible alternative. I argue that outsourcing innovation effectively
enhances innovation capacities and therefore raises stock performance. After
grouping targets by innovation measures, I employ event study in long-term and
short-term perspectives, supposing acquirers bidding innovative targets enjoy
positive abnormal returns.
Under short-term event period (no longer than 5 days after announcement), I
find the abnormal returns of strong-target groups are still significantly negative.
Nonetheless, it does not mean bidding innovative target yields underperformance
relative to it peers. In fact, the two-sample t-test shows the difference is insignificant,
suggesting the negative performance just coincides with prior study’s result that
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acquirers tend to underperform in general.
On the other hand, my long-term study focuses on post-event integration.
Intriguingly, the result becomes quite consistent as I prolong my event period to
longer than 24 months. The strong-target groups reveal positive and significant
abnormal returns across all innovation metrics under 2-year, 3-year, and 5-year event
windows. Afterward, I conduct multiple regression analysis via conducting both
OLS and WLS procedures. The OLS result shows that only average citation has
positive relation under the longest event window. In contrast, the result of WLS
shows much convincing numbers. I use the bidders’ inflation-adjusted size before
announcement as weights. The result of WLS not only confirms the positive
influence of average citation on stock performance, it presents the positive relation
between other innovation metrics and abnormal returns as well, showing that
bidding innovative targets does benefit to acquirers, especially for acquirers that are
large in size.
In the last part, I employ calendar time portfolio in order to see if “mergers for
innovations” could be an idea for investment strategy. And the result also confirms
average citation to be a practical filter for stock selection.
In conclusion, my study shows the relation between innovation and stock
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performance could be observed on a long-run basis. The relation is proven to be
positive, suggesting bidding innovation is a feasible mean to improve post-M&A
performance, especially in the case of large acquirers. As to investment strategy, the
average citation is proven to be a practical measure to select targets.
5.2. Suggestions for Further Studies
With globalization and development in technology, mergers for innovation are
expected to increase year after year. Innovation-oriented M&A should be a valuable
realm for further research. There are several limitations in my study. The citation
data do not distinguish self-citation. Although self-citing might still be valuable, it
might be concern that companies exaggerate their innovation competence by
self-citing. Also, I combine the M&A list with patent data by identifying each
patent’s assignee, this approach acts as a filter, dropping firms with no patents out of
my sample. It is possible that my sample consists of relatively innovative firms. In
addition, I use applied years as I recognize the innovation measures before mergers.
However, the duration between applied date and grant date might be several years,
the result might be different if I use grant date for matching.
In this paper, I focus on post-M&A stock performance of acquirers. However,
long-term performance should be examined by stock performance as well as
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operating result. If gain on market value reflects improvement in operation, then the
positive announcement returns should be supported subsequently by outperformance
in financial statements. Moreover, improvement in innovation efforts after merger
could also be examined. NBER provides fruitful patent data, and the dataset is
expected to cover more information with the development in technology and
intellectual property. Therefore, I anticipate more insights could be unveiled in the
future.
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