Tilburg University

About: Tilburg University is a education organization based out in Tilburg, Noord-Brabant, Netherlands. It is known for research contribution in the topics: Population & Context (language use). The organization has 5550 authors who have published 22330 publications receiving 791335 citations.

Shareholder Wealth Effects of European Domestic and Cross-border Takeover Bids

Abstract: This paper analyses the short-term wealth effects of large intra-European takeover bids. We find announcement effects of 9% for the target firms compared to a statistically significant announcement effect of only 0.7% for the bidders. The type of takeover bid has a large impact on the short-term wealth effects with hostile takeovers triggering substantially larger price reactions than friendly operations. When a UK firm is involved, the abnormal returns are higher than those of bids involving both a Continental European target and bidder. There is strong evidence that the means of payment in an offer has an impact on the share price. A high market-to-book ratio of the target leads to a higher bid premium, but triggers a negative price reaction for the bidding firm. We also investigate whether the predominant reason for takeovers is synergies, agency problems or managerial hubris. Our results suggest that synergies are the prime motivation for bids and that targets and bidders share the wealth gains.

Estimating the Association between Latent Class Membership and External Variables Using Bias-adjusted Three-step Approaches

Abstract: Latent class analysis is a clustering method that is nowadays widely used in social science research. Researchers applying latent class analysis will typically not only construct a typology based on a set of observed variables but also investigate how the encountered clusters are related to other, external variables. Although it is possible to incorporate such external variables into the latent class model itself, researchers usually prefer using a three-step approach. This is the approach wherein after establishing the latent class model for clustering (step 1), one obtains predictions for the class membership scores (step 2) and subsequently uses these predicted scores to assess the relationship between class membership and other variables (step 3). Bolck, Croon, and Hagenaars (2004) showed that this approach leads to severely downward-biased estimates of the strength of the relationships studied in step 3. These authors and later also Vermunt (2010) developed methods to correct for this bias. In the cu...

A review of multi-component maintenance models with economic dependence

Abstract: In this paper we review the literature on multi-component maintenance models with economic dependence. The emphasis is on papers that appeared after 1991, but there is an overlap with Section 2 of the most recent review paper by Cho and Parlar (1991). We distinguish between stationary models, where a long-term stable situation is assumed, and dynamic models, which can take information into account that becomes available only on the short term. Within the stationary models we choose a classification scheme that is primarily based on the various options of grouping maintenance activities: grouping either corrective or preventive maintenance, or combining preventive-maintenance actions with corrective actions. As such, this classification links up with the possibilities for grouped maintenance activities that exist in practice.

Representing text chunks

Abstract: Dividing sentences in chunks of words is a useful preprocessing step for parsing, information extraction and information retrieval. (Ramshaw and Marcus, 1995) have introduced a "convenient" data representation for chunking by converting it to a tagging task. In this paper we will examine seven different data representations for the problem of recognizing noun phrase chunks. We will show that the the data representation choice has a minor influence on chunking performance. However, equipped with the most suitable data representation, our memory-based learning chunker was able to improve the best published chunking results for a standard data set.