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The main purpose of this study is to examine the possibility of prediction of Greek takeover targets that belong to the industrial sector, emphasizing the econometric methodology and the prediction test.

The study uses a sample of 51 targets and 290 non‐targets exclusively from Greek industry over the period 1997‐2005. In order to achieve a better predictive accuracy the paper uses a new econometric methodology, the bootstrap mixed logit and different (more advanced) techniques of prediction test and choice of cutoff values.

The results exhibit that bootstrap mixed logit has significant and valuable predictive ability with respect to the classical conditional logit model. Furthermore, the predictive accuracy is higher than the results of other studies (e.g Palepu and Espahbodi and Espahbodi).

The main contribution of this study is the application of the bootstrap mixed logit in analyzing Greek takeovers. The results change the prediction variables as well as the determinants of the takeover target characteristics for the Greek industry. This is meaningful, not only for the investors that seek to increase the value of their fortune through acquisitions, but also for the managers that can detect if their firm might be considered a takeover target.

The purpose of this research is to highlight the financial characteristics of large French targets which were subject to takeovers during the period 2001–2007 and thereafter deduct the implicit motivations of acquirers.

Using a global sample of 128 French listed companies (64 targets and 64 non-targets), the authors carried out Wilcoxon–Mann–Whitney testing and logistic regression in order to test nine hypotheses likely to discriminate between the two categories of companies (targets and non-targets).

According to the results, target firms are more unbalanced in terms of growth resources and less rich in liquidity than their peers. They have unused debt capacity, offer greater opportunities for growth than firms in the control group and present low levels of value creation.

The main limitation of this study is regarding the sample size, limited by the exclusive use of large firms (deals of over $100m). The scope of this research could be broadened in future by including medium-sized companies.

The authors believe that their results have two major implications. First, they enable market investors to achieve abnormal returns by investing in predicted targets through a portfolio of high takeover probability firms. Second, CEO of companies that are potentially targeted can assess their takeover likelihood in order to act and to manage such a situation for the benefit of their shareholders.

This research concerns the last wave of takeover prior to the subprime-mortgage financial crisis (2001–2007), a period that has not been sufficiently covered in empirical studies. This research contributes to the existing literature in two main respects. First, the results of this study improve our understanding of motivations for takeovers, particularly in the French context. Second, the introduction of new accounting and financial variables, not previously tested in the literature, enriches the available information concerning the profile of takeover targets.

ASEAN region has emerged as a major hotspot for banking mergers and acquisitions (M&A) in Asia. This paper aims to examine the determinants of acquisitions for 47 acquired banks and 33 acquiring banks in ASEAN from 2003 to 2011 by applying matching strategy.

Three binary logistic regressions are estimated in the study to identify the determinants of acquisitions in the ASEAN banking industry. Furthermore, the paper examines the ex ante bank-specific and country-specific characteristics of acquiring and acquired banks which motivate bank acquisitions.

The division of the sample into sub-samples reflects significant changes in the determinants of the likelihood of being acquired over different time periods. In the normal period prior to the financial crises, acquired banks are also found to have greater loan activities. Asset quality and liquidity played important roles in determining the likelihood of being acquired in the period after the onset of the 2007 global financial crisis and the European sovereign debt crisis. Larger banks with higher growth and greater profitability are more likely to engage in acquisitions as acquiring banks rather than as acquired banks. The study indicates that financial crises bring about a change in the determinants of bank acquisitions.

The results for the bank-specific determinants are consistent with the growth-resource and inefficient management hypotheses. It is obvious that the involvement of ASEAN banks in acquisitions is strongly motivated by the pursuit of growth, consistent with the rapid economic growth in the region.

The study identifies the bank-specific and country-specific characteristics of acquiring and acquired banks which influence their involvement in M&A. The uniqueness of this paper lies in the applied methodology on matching strategy.

This study proposes a qualitative analysis to identify takeover target criteria according to French Mergers and Acquisitions (M&A) practitioners.

A principal component factor analysis, applied to responses from 42 French M&A practitioners, highlighted four factors that summarize information about predictive variables and which explain the occurrence of takeover.

According to the surveyed practitioners, four main axes explain 83% of the occurrence of takeover. These axes reflect motivations related to the undervalued target theory, synergy theory and agency theory. The first factor defined by the size of the company, its rate of return and turbulence in the sector. A second factor opposed market value and dividend payout ratio to the liquidity variable. The last two factors are called the debt factor, structured by the debt variable and the value creation factor, which opposed the value creation variable and transaction volume to the growth opportunities variable. The results therefore confirmed the importance of some predictor variables tested in previous studies and showed different results.

This study was limited in terms of sample size. The low number of responses obtained reflects the sensitivity of the subject, insofar as it highlights the predictive model used by M&A practitioners (professional secrecy). Future investigations will involve in extending the questionnaire approach to a larger sample of continental European M&A practitioners.

Predicting takeover targets has been the subject of abundant literature. The results do not converge and are sometimes contradictory. This paper undertakes a field study conducted using a questionnaire survey to detect predictive variables used by M&A practitioners in their identification of a target firm. The authors aim to identify a relevant indicators favorable to the occurrence of a takeover bid and which are/or not handled by the literature.

Development of new cultivars requires extensive genetic knowledge, trained personnel, and significant financial resources, so it is crucial for breeders to focus on the attributes most preferred by the key supply chain stakeholders such as consumers and producers. The purpose of this paper is to identify which attributes generate the highest total revenue or social surplus, information that breeders can take into account as they allocate resources to focus on attributes in their breeding programs.

This study used mail-in and online surveys to collect consumer and producer choice experiment data, and then employed mixed logit models to analyze and simulate individual producer and consumer willingness to pay (WTP) for the apple attributes.

Based on the simulation results, this study derived the supply and demand curves and the market equilibrium prices and quantities for each apple attribute. Based on the WTP analysis for both consumer and producer, this paper found the highest equilibrium price and welfare for apples come from crispness, followed by flavor.

The authors propose a framework to estimate the equilibrium prices and quantities of a product based on the results of choice experiments. The framework can be easily adapted to understand any countries’ producer and consumer preferences for certain products.

Currently, the state of practice in experimental design centres on orthogonal designs (Alpizar et al., 2003), which are suitable when applied to surveys with a large sample size. In a stated choice experiment involving interdependent freight stakeholders in Sydney (see Hensher & Puckett, 2007; Puckett et al., 2007; Puckett & Hensher, 2008), one significant empirical constraint was difficult in recruiting unique decision-making groups to participate. The expected relatively small sample size led us to seek an alternative experimental design. That is, we decided to construct an optimal design that utilised extant information regarding the preferences and experiences of respondents, to achieve statistically significant parameter estimates under a relatively low sample size (see Bliemer & Rose, 2006).

The D-efficient experimental design developed for the study is unique, in that it centred on the choices of interdependent respondents. Hence, the generation of the design had to account for the preferences of two distinct classes of decision makers: buyers and sellers of road freight transport. This paper discusses the process by which these (non-coincident) preferences were used to seed the generation of the experimental design, and then examines the relative power of the design through an extensive bootstrap analysis of increasingly restricted sample sizes for both decision-making classes in the sample. We demonstrate the strong potential for efficient designs to achieve empirical goals under sampling constraints, whilst identifying limitations to their power as sample size decreases.

Patients and health professionals often make decisions which involve a choice between discrete alternatives. This chapter reviews the econometric methods which have been developed for modelling discrete choices and their application in the health economics literature. We start by reviewing the multinomial and mixed logit models and then consider issues such as scale heterogeneity, estimation in willingness to pay space and attribute non-attendance.

This study updates the literature review of Jones (1987) published in this journal. The study pays particular attention to two important themes that have shaped the field over the past 35 years: (1) the development of a range of innovative new statistical learning methods, particularly advanced machine learning methods such as stochastic gradient boosting, adaptive boosting, random forests and deep learning, and (2) the emergence of a wide variety of bankruptcy predictor variables extending beyond traditional financial ratios, including market-based variables, earnings management proxies, auditor going concern opinions (GCOs) and corporate governance attributes. Several directions for future research are discussed.

This study provides a systematic review of the corporate failure literature over the past 35 years with a particular focus on the emergence of new statistical learning methodologies and predictor variables. This synthesis of the literature evaluates the strength and limitations of different modelling approaches under different circumstances and provides an overall evaluation the relative contribution of alternative predictor variables. The study aims to provide a transparent, reproducible and interpretable review of the literature. The literature review also takes a theme-centric rather than author-centric approach and focuses on structured themes that have dominated the literature since 1987.

There are several major findings of this study. First, advanced machine learning methods appear to have the most promise for future firm failure research. Not only do these methods predict significantly better than conventional models, but they also possess many appealing statistical properties. Second, there are now a much wider range of variables being used to model and predict firm failure. However, the literature needs to be interpreted with some caution given the many mixed findings. Finally, there are still a number of unresolved methodological issues arising from the Jones (1987) study that still requiring research attention.

The study explains the connections and derivations between a wide range of firm failure models, from simpler linear models to advanced machine learning methods such as gradient boosting, random forests, adaptive boosting and deep learning. The paper highlights the most promising models for future research, particularly in terms of their predictive power, underlying statistical properties and issues of practical implementation. The study also draws together an extensive literature on alternative predictor variables and provides insights into the role and behaviour of alternative predictor variables in firm failure research.

To measure adolescent girls’ preferences over features of human papillomavirus (HPV) vaccines in order to provide quantitative estimates of the perceived benefits of vaccination and potential vaccine uptake.

A discrete choice experiment (DCE) survey was developed to measure adolescent girls’ preferences over features of HPV vaccines. The survey was fielded to a U.S. sample of 307 girls aged 13–17 years who had not yet received an HPV vaccine in June 2008.

In a latent class logit model, two distinct groups were identified – one with strong preferences against vaccination which largely did not differentiate between vaccine features, and another that was receptive to vaccination and had well-defined preferences over vaccine features. Based on the mean estimates over the entire sample, we estimate that girls’ valuation of bivalent and quadrivalent HPV vaccines ranged between $400 and $460 in 2008, measured as willingness-to-pay (WTP). The additional value of genital warts protection was $145, although cervical cancer efficacy was the most preferred feature. We estimate maximum uptake of 54–65%, close to the 53% reported for one dose in 2011 surveillance data, but higher than the 35% for three doses in surveillance data.

We conclude that adolescent girls do form clear opinions and some place significant value on HPV vaccination, making research on their preferences vital to understanding the determinants of HPV vaccine demand.

DCE studies may be used to design more effective vaccine-promotion programs and for reassessing public health recommendations and guidelines as new vaccines are made available.