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Predicting Takeover Targets in the US Technology Industry

Voll, Enok Andreas; Høivik, Vegard
Master thesis
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URI
http://hdl.handle.net/11250/2625304
Date
2019
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  • Master of Science [1116]
Abstract
This thesis explores which factors affect takeover prediction in the US technology

industry and whether abnormal returns are achievable with an investment portfolio

based on takeover probabilities. With a sample consisting of 581 target- and 2130

non-target observations from the period 1993-2014, the takeover prediction

probabilities are calculated through a logistic regression model. Incorporating the

fifth and sixth merger waves in a model focusing solely on the US technology industry

is new to this field of research. The results from the logistic regression indicate that

(increases in) Revenue Growth along with the Current Ratio and Debt/Assets have a

significantly negative impact on takeover probability, while (increases in) the Natural

Logarithm of Revenue, Dividend Yield, Fed Rate and Industry Disturbances have a

significantly positive impact on takeover probability. The estimates are applied on a

hold-out sample consisting of 145 target- and 675 non-target observations over the

period 2015-2018 to form two investment portfolios. The portfolio formed by the

minimum misclassification-strategy (Palepu, 1986) achieves 2.06% abnormal return

over the period, predicting 27.54% of the targets and 84.31% of the non-targets

correctly. The portfolio formed according to the maximum target-strategy (Powell,

2001) achieves –5.32% abnormal return over the period, predicting 83.33% of the

targets and 83.79% of the non-targets correctly. Thus, the results suggest that one

can predict takeover targets quite accurately, though there are limitations to the

extent to which one can achieve abnormal returns from it. This provides an exciting

basis for future extensions and utilization of the industry-specific takeover prediction

model.

Key words: Takeover prediction, logistic regression, abnormal return, investing

strategy, technology, market efficiency

JEL classification: O51, L63, L65, C53, G11, G14, G34
Description
Masteroppgave(MSc) in Master of Science in Business, Finance - Handelshøyskolen BI, 2019
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Handelshøyskolen BI

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