Forecasting the Price of the Candidate in M&A Based on Multiple-Kernel SVMR

Author

Hongjiu Liu ; Yanrong Hu ; Weimin Ma

Author_Institution

Sch. of Manage., Changshu Inst. of Technol., Changshu, China

fYear

2011

fDate

12-14 Aug. 2011

Firstpage

1

Lastpage

4

Abstract

In Mergers and Acquisitions, forecasting the price of the candidate is a very important step, which decides whether an acquisition continues to advance. In this paper, multiple-kernel SVMR is applied to predict the price of candidates in mergers and acquisitions. In the model, we adopt a two-stage multiple-kernel learning algorithm by incorporating sequential minimal optimization and the gradient projection method. By this algorithm, advantages from different hyperparameter settings can be combined and overall system performance can be improved. Experimental results show that SVMR performs better than other methods which a strong tool for M&A decision-making.

Keywords

corporate acquisitions; gradient methods; optimisation; pricing; regression analysis; support vector machines; gradient projection method; mergers and acquisitions; multiple-kernel SVMR; multiple-kernel learning algorithm; price forecasting; sequential minimal optimization; support vector machine regression; Educational institutions; Forecasting; Kernel; Mathematical model; Optimization; Predictive models; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Management and Service Science (MASS), 2011 International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-6579-8

Type

conf

DOI

10.1109/ICMSS.2011.5998121

Filename

5998121