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/08c9191c10c6443e82072896b5966df6
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:08c9191c10c6443e82072896b5966df6
record_format	dspace
spelling	oai:doaj.org-article:08c9191c10c6443e82072896b5966df62021-11-25T06:19:46ZForecasting mergers and acquisitions failure based on partial-sigmoid neural network and feature selection1932-6203https://doaj.org/article/08c9191c10c6443e82072896b5966df62021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8598039/?tool=EBIhttps://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 (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/08c9191c10c6443e82072896b5966df6
work_keys_str_mv	AT wenbinbi forecastingmergersandacquisitionsfailurebasedonpartialsigmoidneuralnetworkandfeatureselection AT qiushengzhang forecastingmergersandacquisitionsfailurebasedonpartialsigmoidneuralnetworkandfeatureselection
_version_	1718413844979646464

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

Ejemplares similares