Takeovers and the Cross-Section of Returns

Cremers, K. J. Martijn; Nair, Vinay B.; John, Kose

doi:10.1093/rfs/hhn032

Cited by 278 publications

(135 citation statements)

References 49 publications

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“…First approach identifies a single firm characteristic that is used for classification of anticipated and unanticipated bidder firms [5,16,24,32,34,42,44]. Second approach develops predictive models of bidder candidacy that use multiple firm characteristics to classify anticipated versus unanticipated bidders [2,3,9,10,12,13,39,41]. The contribution of this paper is three-fold.…”

Section: Introductionmentioning

confidence: 99%

A comparison of artificial neural network and multinomial logit models in predicting mergers

Fescioglu-Ünver

Tanyeri

2013

Journal of Applied Statistics

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A merger proposal discloses a bidder firm's desire to purchase the control rights in a target firm. Predicting who will propose (bidder candidacy) and who will receive (target candidacy) merger bids is important to investigate why firms merge and to measure the price impact of mergers. This study investigates the performance of artificial neural networks and multinomial logit models in predicting bidder and target candidacy. We use a comprehensive data set that covers the years 1979-2004 and includes all deals with publicly listed bidders and targets. We find that both models perform similarly while predicting target and non-merger firms. The multinomial logit model performs slightly better in predicting bidder firms.

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Section: Introductionmentioning

confidence: 99%

A comparison of artificial neural network and multinomial logit models in predicting mergers

Fescioglu-Ünver

Tanyeri

2013

Journal of Applied Statistics

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“…In Panel C, we augment the Carhart four-factor model with the liquidity factor of Pastor and Stambaugh (2003) and estimate this five-factor model. In Panel D, we extend the Carhart four-factor model by the takeover factor of Cremers, Nair, and John (2009) and estimate this five-factor model. In all panels, we use the principal component of the three transparency proxies as a measure of transparency.…”

Section: Alternative Asset Pricing Modelsmentioning

confidence: 99%

“…which allows us to control simultaneously for various variables used in prior research, such as competition, institutional ownership, etc. Finally, we experiment with alternative asset pricing models that include, e.g., the liquidity factor of Pastor and Stambaugh (2003) or the takeover factor of Cremers, Nair, and John (2009) as a 5th factor. Therefore, we conclude that a firm's information environment is an independently important dimension for the way in which corporate governance affects firm performance.…”

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confidence: 99%

Governance and Equity Prices: Does Transparency Matter?*

Hackbarth

2013

Review of Finance

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Abstract. This article examines how accounting transparency and corporate governance interact. Firms with better governance are associated with higher abnormal returns, but even more so if they also have higher transparency. The effect is largely monotonic-it is small and insignificant for opaque firms and large and significant for transparent firms-and survives numerous robustness tests. We find supportive evidence for firm value and operating performance. Hence, governance and transparency are complements. This complementarity effect is consistent with the view that more transparent firms are more likely takeover targets, because acquirers can bid more effectively and identify synergies more precisely.

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“…In doing so, it provides a framework that reconciles the market timing literature (e.g., Rhodes-Kropf et al, 2005, Dong et al, 2006, Shleifer and Vishny, 2003 with the non-behavioral side of the literature. Finally, our methodology builds on the literature on expectations in merger announcements (Malatesta and Thomson, 1985, Eckbo et al, 1990, Cremers et al, 2009, and Cornett et al, 2011. These papers predict merger candidacy and link subsequent returns to the surprise effect of the announcement.…”

Section: Introductionmentioning

confidence: 99%

Acquisition Finance and Market Timing

Vermaelen

Groen-Xu

2013

SSRN Journal

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Bidders have an incentive to pay with stock when their shares are overvalued, but target firms should be reluctant to accept such overvalued payment. In a sample of 2,978 acquisitions, we find that stock payment is readily accepted only when the bidder can justify the financing decision in terms of such economic fundamentals as optimal capital structure. Yet even when the fundamentals justify stock payment, paying with cash is common. In that way, firms can preclude paying with undervalued stock and are more likely to experience positive long-term excess returns. JEL: G14, G34

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Takeovers and the Cross-Section of Returns

Cited by 278 publications

References 49 publications

A comparison of artificial neural network and multinomial logit models in predicting mergers

A comparison of artificial neural network and multinomial logit models in predicting mergers

Governance and Equity Prices: Does Transparency Matter?*

Acquisition Finance and Market Timing

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