"This article extends the Palepu (1986) acquisition likelihood model by incorporating measures of a technical nature, e.g. momentum, trading volume as well as a measure of market sentiment. We use the proposed model to predict takeover targets in a large sample of European and cross-border merger and acquisition deals and validate its performance on an in- and out-of-sample basis. The robustness of the proposed model is investigated across several dimensions. In addition we explore the ability of the model to form the basis of successful takeover timing investment strategies. The results of our empirical analysis suggest that the proposed model predicts European takeover targets with relatively high accuracy and is able to determine portfolios that earn significant returns which are not explained by conventional risk factors." Copyright (c) 2008 The Authors Journal compilation (c) 2008 Blackwell Publishing Ltd.

Mutual fund manager excess performance should be measured relative to their self-reported benchmark rather than the return of a passive portfolio with the same risk characteristics. Ignoring the self-reported benchmark introduces biases in the measurement of stock selection and timing components of excess performance. We revisit baseline empirical evidence in mutual fund performance evaluation utilizing stock selection and timing measures that address these biases. We introduce a new factor exposure based approach for measuring the -static and dynamic -timing capabilities of mutual fund managers. We overall conclude that current studies are likely to be overstating lack of skill because they ignore the managers' self-reported benchmark in the performance evaluation process. Revisiting Mutual Fund Performance Evaluation AbstractMutual fund manager excess performance should be measured relative to their self-reported benchmark rather than the return of a passive portfolio with the same risk characteristics. Ignoring the self-reported benchmark introduces biases in the measurement of stock selection and timing components of excess performance. We revisit baseline empirical evidence in mutual fund performance evaluation utilizing stock selection and timing measures that address these biases. We introduce a new factor exposure based approach for measuring the -static and dynamic -timing capabilities of mutual fund managers. We overall conclude that current studies are likely to be overstating lack of skill because they ignore the managers' self-reported benchmark in the performance evaluation process.

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