M&A target prediction in Australia by ProQuest


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									Key words: mergers and acquisitions; takeovers; target prediction models; modelling

M&A target prediction
in Australia
our model indicates that takeover announcements are predictable, to some extent,
in the Australian market. it combines existing academic theories about the typical
characteristics of takeover target firms with our observation that information is
often leaked before such events. this suggests that fund managers are able to be
proactive about this significant risk source by integrating our recommended
prediction model with their investment process. backtests also indicate that our
recommended targets tend to outperform the index in the long term.

                                  merGers AnD Acquisition (m&A) announcements and failures, similar to
                                  profit warnings or earnings surprises, are high-impact events that usually cause large shifts
                                  in security prices. M&A is a complex combination of business and management’s incentives
                                  for the acquirer and target firms, bounded by regulatory constraints. The price impact of
                                  takeover announcements is detrimental to the active institutional investor who may be
                                  holding an underweight or short position in the target firm. Target firms, which are often
                                  subject to inefficient management, may be viewed as undesirable by active stock market
                                  investors. The same firms may, however, look attractive and undervalued to an acquirer
                                  firm that sees potential for business synergy. These potential synergies are inevitably
                                  ignored by one-dimensional models that only look at the characteristics of potential
eben vAn wyK is head              takeover candidates, but ignore the potential synergies available to active acquirers. (We
of quantitative research,         leave this as a challenge for future research.)
royal bank of scotland.
email: eben.van.wyk@rbs.com
                                  the importance of m&A prediction
                                  We conduct an event study for target firms around the day of announcement. Figure 1
Anh h. nGuyen is                  shows a cumulative plot of the average returns (stock minus S&P/ASX 300 Index) on
Assistant Analyst,                trading days around M&A announcements, demonstrating the average price effect of
quantitative research,
royal bank of scotland.           such announcements.
email: anh.h.nguyen@rbs.com            We observe an unexplained positive return up to a year before an announcement.
                                  There tends to be an accelerated run-up just before the announcement date, suggesting
                                  informed trading/information leakage. There is a large positive return on the
                                  announcement day – due to the premium offered by the acquirer – and a smaller jump
                                  the next day. Generally, we see a slight reversal in the following week and negative drift
                                  in the year following a takeover announcement.

                                  ideas from academia
                                  An extensive review (see References for the most relevant papers) of academic literature
                                  in the US and European markets provides us with a number of hypotheses for the types of
                                  firms that tend to be acquired. This information improves our understanding of M&As
                                  and gives us a sensible set of financial ratios that could be included in a prediction model.
                                  Table 1 provides a summary of the hypotheses and suggested accounting ratios.

                                  jassa the finsia journal of applied finance issue 1 2010                                15
     Different statistical techniques are available, but                including learning models like Neural Networks, with
there is a reasonable consensus on the analysis framework.              comparable results. There are also different and often
The first step is variable selection, with some authors using           conflicting opinions about the ways to evaluate the
existing hypotheses, while others simply
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