Publicação
Predicting takeover targets in Europe : assessment of the main determinants and predicting quality
| Resumo: | In the past three decades, many scholars throughout the world attempted to explain the triggers of mergers and acquisitions (M&A) and claimed that it was possible to predict targets of this activity. One significant stream of this literature compares the characteristics of companies that were targets and non-targets of an acquisition attempt using a probabilistic analysis. This dissertation aims to use a similar methodology in a sample of 4431 listed companies from the EU-27. The purpose is three-fold: (i) examine the main determinants of firm targeting behavior; (ii) accurately predict and classify targets of acquisition; and (iii) test if abnormal returns can be earned by investing in this potential targets. The independent variables used in this study are motivated by the most frequently discussed hypothesis of takeover determinants. Unlike most of previous authors I examine acquisition attempts and not just complete deals. This study also provides several contributions to this research field, such as a ROC analysis of the models ability to classify and predict takeover targets and non-targets. The results unveil that a large number of these variables and the overall model are statistically significant (1% level of significance), providing evidence for nine out of the ten included takeover determinants hypothesis. All models provide consistent evidence that target firms are usually larger than non-targets, pertain to industries with recent history of takeovers and generate sales or revenues from their assets more efficiently than non-targets. The results also show a higher explanatory and classification power of models that consider both a shorter period sample in the estimation and financial variables calculated with averages of two years prior the bid. I am also able to achieve one of the highest predictive accuracies reported in the literature, although this was not a sufficient condition to earn significant positive abnormal returns from a long investment (250 trading days) in the predicted target stocks. Therefore, the results support the pessimist view that it is not possible to earn positive abnormal returns by using strictly publicly available information in the context of M&A. |
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| Autores principais: | Carvalho, Miguel Moreira |
| Assunto: | Europe Mergers and acquisitions (M&A) Prediction Probit Abnormal returns Europa Fusões e aquisições Previsão Probit Retornos anormais |
| Ano: | 2013 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | In the past three decades, many scholars throughout the world attempted to explain the triggers of mergers and acquisitions (M&A) and claimed that it was possible to predict targets of this activity. One significant stream of this literature compares the characteristics of companies that were targets and non-targets of an acquisition attempt using a probabilistic analysis. This dissertation aims to use a similar methodology in a sample of 4431 listed companies from the EU-27. The purpose is three-fold: (i) examine the main determinants of firm targeting behavior; (ii) accurately predict and classify targets of acquisition; and (iii) test if abnormal returns can be earned by investing in this potential targets. The independent variables used in this study are motivated by the most frequently discussed hypothesis of takeover determinants. Unlike most of previous authors I examine acquisition attempts and not just complete deals. This study also provides several contributions to this research field, such as a ROC analysis of the models ability to classify and predict takeover targets and non-targets. The results unveil that a large number of these variables and the overall model are statistically significant (1% level of significance), providing evidence for nine out of the ten included takeover determinants hypothesis. All models provide consistent evidence that target firms are usually larger than non-targets, pertain to industries with recent history of takeovers and generate sales or revenues from their assets more efficiently than non-targets. The results also show a higher explanatory and classification power of models that consider both a shorter period sample in the estimation and financial variables calculated with averages of two years prior the bid. I am also able to achieve one of the highest predictive accuracies reported in the literature, although this was not a sufficient condition to earn significant positive abnormal returns from a long investment (250 trading days) in the predicted target stocks. Therefore, the results support the pessimist view that it is not possible to earn positive abnormal returns by using strictly publicly available information in the context of M&A. |
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