"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.
The goal of this report is to equip equity portfolio managers with a new tool to assist them in the crucial task of narrowing down a broad universe to a list of stocks to be analysed in depth. We explore a number of alternative approaches to building a recommender system, i.e. a predictive model which generates stock recommendations based on observable characteristics and previous investor behaviour. The empirical analysis uses data on a large set of global active mutual funds, observed between 2005 and 2016, to calibrate the models and test their predictive ability out of sample. Our main conclusion is that a simple dimension reduction technique achieves the best compromise between precision and recall. Moreover, our recommender system displays good predictive power, particularly when used to forecast future buy trades.
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