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This paper aims to review prior studies and presents a synthesis of the takeover prediction literature spanning the period 1968–2018.

The paper adopts a narrative review approach. It explores prior studies on takeover target prediction from a historical perspective, focusing on the evolution and development of the literature over the 50-year period.

From a historical development perspective, prior studies in the area can be partitioned into four distinct eras. Studies in the first era (1968–1985) mainly established that takeover targets share common characteristics which can be captured with financial ratios. Studies in the second era (1986–2002) developed and extended formal target prediction hypotheses. These studies concluded that it was impossible to build a successful investment strategy around takeover target prediction. Studies in the third era (2003–2009) explored similar questions using alternative modelling techniques but arrive at similar results – targets can be predicted with limited accuracy and target prediction is unlikely to lead to abnormal returns. Studies in the fourth era (2010–2018) explore implications of M&A predictability on share valuation, governance and bond prices (amongst others), but most importantly, provide some evidence that takeover prediction can lead to abnormal returns when combined with appropriate screening strategies.

This presents the first in-depth review of the literature on takeover target prediction. It highlights the development of the literature over four distinct eras and identifies several limitations, research gaps and opportunities for future research. Given the recent decline in the literature (i.e. fourth era), this study may stimulate new research in the area.

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 paper is to propose various toehold indicators and analyse whether the models incorporating these indicators can be used to establish investment strategies.

Logistic regression is used to test toehold indicator significance.

The results reflect that the designed measures are positively correlated to the likelihood of launching a takeover, although the power of the models to predict out‐sample takeovers is moderate, between 60.71 percent and 71.59 percent. The indicators allow us to design strategies which offer positive abnormal returns. In particular, abnormal return over the Fama‐French factors is 0.5 percent.

Toeholds are used to initiate takeover processes. As previous studies have indicated, a toehold increases the likelihood of success in a tender offer. Nevertheless, the studies on takeover prediction do not include the toehold since it is a variable which is unobservable prior to the announcement of a takeover bid.

This study provides valuable insights to managers aiming to increase the effectiveness of their diversification and growth portfolios. The purpose of this paper is to examine the value of utilizing a neural networks (NNs) approach using mergers and acquisition (M&A) data confined in the US technology domain.

Using data from Bloomberg for the period 2000–2016, the results confirm that an NN approach provides more explanation between financial variables in the model than a traditional regression model where the NN approach of this study is then compared with linear classifier, logistic regression. The empirical results show that NN is a promising method of evaluating M&A takeover targets in terms of their predictive accuracy and adaptability.

The findings emphasize the value alternative methodologies provide in high-technology industries in order to achieve the screening and explorative performance objectives, given the technological complexity, market uncertainty and the divergent skill sets required for breakthrough innovations in these sectors.

NN methods do not provide for a fuller analysis of significance for each of the autonomous variables in the model as traditional regression methods do. The generalization breadth of this study is limited within a specific sector (technology) in a specific country (USA) covering a specific period (2000–2016).

Investors value firms before investing in them to identify their true stock price; yet, technology firms pose a great valuation challenge to investors and analysts alike as the latest information technology stock price bubbles, Silicon Valley and as the recent stratospheric rise of financial technology companies have also demonstrated.

Numerous studies have shown that M&As are more often than not destroy value rather than create it. More than 50 percent of all M&As lead to a decline in relative total shareholder return after one year. Hence, effective target identification must be built on the foundation of a credible strategy that identifies the most promising market segments for growth, assesses whether organic or acquisitive growth is the best way forward and defines the commercial and financial hurdles for potential deals.

Technology firm value is directly dependent on growth, consequently most of the value will originate from future customers or products not from current assets that makes it challenging for investors to measure a firm’s beta (risk) where the value of a technology is only known after its commercialization to the market. A differentiated methodological approach used is the use of NNs, machine learning and data mining to predict bankruptcy or takeover targets.

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.

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.

The purpose of this paper is to examine the existence of a diversification discount in the Australian takeover market. A sample of 446 Australian publicly‐listed firms involved in the market for corporate control was observed between 2000 and 2007. The authors examined two pre‐announcement and four post‐announcement periods, predominantly around the immediate event date, but also examined activity out to one year following the announcement.Design/methodology/approach – An event study, in this case, is used to examine abnormal returns around the announcement of a merger or acquisition. The timeframe this study intends to focus on is the period from announcement date to a time one year down the track which, although some studies may deem it “long‐term”, is still a relatively short‐term measure of performance. While many variables in acquisitions have been looked at in depth over the years, such as outcome, nature, payment method and size of deal, one area which has had considerably less attention is the area of specialisation and diversification. That is, do focus increasing (or non‐diversification) deals have different return patterns relative to focus decreasing (or diversification) deals?

The overall findings of this paper are fairly mixed, barring a few exceptions, and there does not appear to be a great deal of variation in return patterns based purely on whether the announced acquisition is non‐diversifying or diversifying in nature.

The paper is of particular value in Australia. Most of the research of diversification to date has taken place in the USA. Australia is similar to the USA in that it has a well‐developed economy based on common law principles and an active equity market, however, the existence of institutional and regulatory differences suggests that US results may not hold in Australia.

The firm size hypothesis – takeover likelihood (TALI) decreases with target firm size (SIZE) – has enjoyed little traction in the TALI modelling literature; hence, this paper aims to redevelop this hypothesis while taking account of prevailing market conditions – capital liquidity and market performance.

The study uses a logit modelling framework to model TALI. Model performance is assessed using receiver operating characteristic (ROC) curve analysis. The empirical analysis is based on a UK sample of 34,661 firm-year observations drawn from 3,105 firms and 1,396 M&A deals over a 30-year period (1987-2016).

While acquirers generally seek smaller targets because of transaction cost constraints, the paper shows that the documented negative relation between SIZE and TALI arises from sampling bias. Over a full sample, mid-sized firms are most at risk of takeovers. Additionally, market conditions moderate the SIZE–TALI relationship, with acquirers more inclined to pursue comparatively larger targets when financing costs are low and market growth or sentiment is high. The results are generally robust to endogeneity.

Sample truncation on the basis of SIZE leads to empirical misspecification of the TALI–SIZE relation. In an unbiased sample, an inverse U-shaped specification between TALI and SIZE sufficiently models the underlying relation and leads to improvements in the predictive ability of TALI models.

This study advances a new firm size hypothesis which is consistent with classic M&A theories. The study also evidences market conditions as a moderator of the acquirer’s choice of target SIZE. A new model specification which recognises the non-linear relation between TALI and SIZE and accounts for the moderating effect of market conditions on the SIZE-TALI relationship leads to improvements in the performance of TALI prediction models.

This paper aims to review the relevant literature on mergers and acquisitions in an attempt to provide a comprehensive account of what we know about mergers and which parts of the puzzle are still incomplete.

This literature review consists of three key sections. The first part of this paper summarises the literature on the cyclical nature of mergers referred to in the literature as merger waves. The second section reviews the causes and consequences of takeovers; it first reviews the causes, or drivers, of acquisitions, while focusing on the fact that acquisitions happen in waves and then reviews the consequences of takeovers, with a predominant focus on the impacts of mergers on the economic performance of acquirers. The third part of the review summarises the theories as well as previous empirical studies on determinants of announcement returns and post-acquisition performance of combined firms.

Merger activity demonstrates a wavy pattern, i.e. mergers are clustered in industries through time. The causes suggested for this fluctuating pattern include industry and economy-level shocks, mis-valuation and managerial herding. Market reaction to announcement of acquisitions is, on average, slightly negative for acquirer stocks and significantly positive for target stocks. The combined abnormal return is positive. These findings have been consistent over several decades of investigation. The prior research also identifies a number of factors that are related to performance of acquisitions. These factors are categorised and reviewed in five different groups: acquirer characteristics, target characteristics, bid characteristics, industry characteristics and macro-environment characteristics.

This review illustrates a number of issues. Prior research is heavily biased towards gains to acquirers and factors that affect these gains. It is also biased towards finding sources of value creation through mergers, despite the fact that several theories suggest that mergers can be value-destroying. In fact, value destruction is often attributed to managers’ self-interest (agency problem) and mistakes (hubris). However, the mechanisms through which mergers destroy value are rarely addressed. Aside from that, the possibility of simultaneous creation and destruction of value in acquisitions is not often considered. Finally, after several decades of investigation, a key question is not completely answered yet: “What are the sources of value in mergers and acquisitions?”

This study aims to investigate the role of the market for corporate control as an external governance mechanism and its effect on executive risk-taking incentives. Managers tend to be risk-averse as they are more exposed to idiosyncratic risk, resulting in sub-optimal risk-taking that does not maximize shareholders’ wealth. The takeover market alleviates this problem, inducing managers to take more risk. Therefore, risk-taking incentives inside the firm are less powerful when the outside takeover market is more active.

Exploiting a novel measure of takeover vulnerability recently constructed by Cain et al. (2017), the authors explore how takeover vulnerability influences executive risk-taking incentives. Using a large sample of US firms, the authors use fixed-effects regressions, propensity score matching and instrumental variable analysis.

Consistent with this study’s hypothesis, a more active takeover market results in less powerful risk-taking incentives. Specifically, a rise in takeover vulnerability by one standard deviation diminishes executive risk-taking incentives by 22.39%, which is an economically meaningful magnitude.

To the best of the authors’ knowledge, this study is the first to explore the effect of the takeover market on managerial risk-taking incentives, using a novel measure of takeover susceptibility. The authors’ measure of takeover vulnerability is considerably less susceptible to endogeneity, enabling the authors to draw causal inferences with more confidence.