Forecasting mergers and acquisitions failure based on partial-sigmoid neural network and feature selection.

Traditional forecasting methods in mergers and acquisitions (M&A) data have two limitations that significantly reduce forecasting accuracy: (1) the imbalance of data, that is, the failure cases of M&A are far fewer than the successful cases (82%/18% of our sample), and (2) both the bidder an...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Wenbin Bi, Qiusheng Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e3da5f56e8ad4728ac17c186247e1f44
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:e3da5f56e8ad4728ac17c186247e1f44
record_format	dspace
spelling	oai:doaj.org-article:e3da5f56e8ad4728ac17c186247e1f442021-12-02T20:12:54ZForecasting mergers and acquisitions failure based on partial-sigmoid neural network and feature selection.1932-620310.1371/journal.pone.0259575https://doaj.org/article/e3da5f56e8ad4728ac17c186247e1f442021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0259575https://doaj.org/toc/1932-6203Traditional forecasting methods in mergers and acquisitions (M&A) data have two limitations that significantly reduce forecasting accuracy: (1) the imbalance of data, that is, the failure cases of M&A are far fewer than the successful cases (82%/18% of our sample), and (2) both the bidder and the target of the merger have numerous descriptive features, making it difficult to choose which ones to forecast. This study proposes a neural network using partial-sigmoid (i.e., partial-sigmoid neural network [PSNN]) as the activation function of the output layer and compares three feature selection methods, namely, chi-square (chi2) test, information gain and gradient boosting decision tree (GBDT). Experimental results prove that our PSNN (improved up to 0.37 precision, 0.49 recall, 0.41 G-Mean and 0.23 F1-measure) and feature selection (improved 1.83%-13.16% accuracy) method can effectively improve the adverse effects of the defects of the above two merger data on forecasting. Scholars who studied the forecast of merger failure have overlooked three important features: assets of the previous year, market value and capital expenditure. The chi2 test feature selection method is the best among the three feature selection methods.Wenbin BiQiusheng ZhangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11, p e0259575 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Wenbin Bi Qiusheng Zhang Forecasting mergers and acquisitions failure based on partial-sigmoid neural network and feature selection.
description	Traditional forecasting methods in mergers and acquisitions (M&A) data have two limitations that significantly reduce forecasting accuracy: (1) the imbalance of data, that is, the failure cases of M&A are far fewer than the successful cases (82%/18% of our sample), and (2) both the bidder and the target of the merger have numerous descriptive features, making it difficult to choose which ones to forecast. This study proposes a neural network using partial-sigmoid (i.e., partial-sigmoid neural network [PSNN]) as the activation function of the output layer and compares three feature selection methods, namely, chi-square (chi2) test, information gain and gradient boosting decision tree (GBDT). Experimental results prove that our PSNN (improved up to 0.37 precision, 0.49 recall, 0.41 G-Mean and 0.23 F1-measure) and feature selection (improved 1.83%-13.16% accuracy) method can effectively improve the adverse effects of the defects of the above two merger data on forecasting. Scholars who studied the forecast of merger failure have overlooked three important features: assets of the previous year, market value and capital expenditure. The chi2 test feature selection method is the best among the three feature selection methods.
format	article
author	Wenbin Bi Qiusheng Zhang
author_facet	Wenbin Bi Qiusheng Zhang
author_sort	Wenbin Bi
title	Forecasting mergers and acquisitions failure based on partial-sigmoid neural network and feature selection.
title_short	Forecasting mergers and acquisitions failure based on partial-sigmoid neural network and feature selection.
title_full	Forecasting mergers and acquisitions failure based on partial-sigmoid neural network and feature selection.
title_fullStr	Forecasting mergers and acquisitions failure based on partial-sigmoid neural network and feature selection.
title_full_unstemmed	Forecasting mergers and acquisitions failure based on partial-sigmoid neural network and feature selection.
title_sort	forecasting mergers and acquisitions failure based on partial-sigmoid neural network and feature selection.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/e3da5f56e8ad4728ac17c186247e1f44
work_keys_str_mv	AT wenbinbi forecastingmergersandacquisitionsfailurebasedonpartialsigmoidneuralnetworkandfeatureselection AT qiushengzhang forecastingmergersandacquisitionsfailurebasedonpartialsigmoidneuralnetworkandfeatureselection
_version_	1718374854802014208

Forecasting mergers and acquisitions failure based on partial-sigmoid neural network and feature selection.

Ejemplares similares