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Do Fire Sales Really Exist? An Empirical Study on Distressed Targets' Premiums


Do Fire Sales Really Exist? An Empirical Study on Distressed Targets' Premiums

Mohammad, Mostafania (2014) Do Fire Sales Really Exist? An Empirical Study on Distressed Targets' Premiums. Masters thesis, Concordia University.

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Firms can be de-listed from public market due to different reasons. They could go bankrupt which would be a negative outcome for their shareholders, they could merge with or acquired by other firms, or they could go private, outcomes that are usually pleasant for their stakeholders. What if a firm becomes an acquisition target in the period right before going bankrupt? Is it still a positive event for shareholders or because of the firm’s distressed situation there will be no positive return for them? We estimate the likelihood of firm failure and examine the premium offered for distressed public firms in both contractions and normal economic periods. We use Survival Analysis and Artificial Neural Networks, both using multi-period inputs, to categorize firms into distressed and not-distressed groups. These models claim to be more successful compared to the single-period static models widely used in the extant literature. Results of analyzing 1378 targets in different market conditions shows that acquirers tend to overpay for distressed targets and even more in contraction periods. on the other hand, we observe a huge discount when we calculate the mean target premium in reference to targets' highest price in the 52-week period before the announcement . It seems that acquirers bid reference is not the current market valuation, but the target’s best position in the one year prior to announcement.

Divisions:Concordia University > John Molson School of Business > Finance
Item Type:Thesis (Masters)
Authors:Mohammad, Mostafania
Institution:Concordia University
Degree Name:M. Sc.
Program:Administration (Finance option)
Date:5 December 2014
Thesis Supervisor(s):Betton, Sandra
Keywords:Survival Analysis, Fire Sale, Artificial Neural Networks, Logistic Regression, Distressed Firms, Bankruptcy
ID Code:979597
Deposited On:13 Jul 2015 16:25
Last Modified:18 Jan 2018 17:49


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