A Comparison of Artificial Neural Network and Multinomial Logit Models in Predicting Mergers

18 Pages Posted: 1 Jul 2010 Last revised: 25 Feb 2011

See all articles by Nilgun Fescioglu-Unver

Nilgun Fescioglu-Unver

TOBB University of Economics and Technology

Başak Tanyeri

Bilkent University

Date Written: February 1, 2011

Abstract

A merger occurs when a bidder firm offers to purchase the control rights in a target firm or when a target firm solicits bids from a bidder firm to purchase the control rights. Predicting merger candidacy is important to measure the price impact of mergers.

This study investigates the performance of artificial neural networks and multinomial logit models in predicting merger candidacy. We use a comprehensive dataset that covers the years 1979 to 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.

Keywords: mergers, artificial neural network models, multinomial logit models

JEL Classification: G34, C45, C25

Suggested Citation

Fescioglu-Unver, Nilgun and Tanyeri, Başak, A Comparison of Artificial Neural Network and Multinomial Logit Models in Predicting Mergers (February 1, 2011). Available at SSRN: https://ssrn.com/abstract=1632864 or http://dx.doi.org/10.2139/ssrn.1632864

Nilgun Fescioglu-Unver

TOBB University of Economics and Technology ( email )

Faculty of Economics and Administrative Sciences
Söğütözü Cad. 43,
Ankara, Cankaya
Turkey

Başak Tanyeri (Contact Author)

Bilkent University ( email )

06533 Bilkent, Ankara
Turkey
903122901871 (Phone)

HOME PAGE: http://www.bilkent.edu.tr/~basak

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