Exploring and weighting features for financially distressed construction companies using Swarm Inspired Projection algorithm

NCUIR > Engineering College > Department of Civil Engineering > journal & Dissertation > Item 987654321/100526

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/100526

Title:	Exploring and weighting features for financially distressed construction companies using Swarm Inspired Projection algorithm
Authors:	陳介豪;Chen, Jieh-Haur;Su, Mu-Chun;Annuerine Badjie, Bevan
Contributors:	工學院土木工程學系
Keywords:	Algorithms;Bankruptcies;Business;Construction companies;Construction costs;Economics;Failure;Financial distress;Financial ratios;Principal component analysis;Projection;Swarm inspired projection algorithm
Date:	2016-08-01
Issue Date:	2026-04-21 14:05:18 (UTC+8)
Publisher:	Elsevier Ltd.;Elsevier Ltd
Abstract:	摘要： Financial crisis has raised concerns for years and its effect on companies influence economies globally. The ability to accurately identify the features responsible for business failure is an important issue in financial decision-making. There is clear need for accurate decision support for both credit granting and monitoring of ongoing health of credit customers. The financial ratios involved provide useful quantitative financial information to both investors and analysts so that they can evaluate the operation of a firm and analyze its position within a sector. This research brings awareness to managers as to which features they have to focus on. All the ratios involved each play a crucial role. In this paper, the Swarm Inspired Projection (SIP) algorithm as a new analysis tool is combined with the Principal Component Analysis (PCA) to determine the weights of the features and to adjust these weights to suit the profitability of these construction companies. The study made use of 1615 effective financial reports from 55 construction companies over the last decade. Based on the 25 ratios used, the PCA incorporating the SIP algorithm gives us an average accuracy rate of 90%. This method provides better reliability in the identification of the principal features in bankruptcy analysis. Corporate financial distress is a major concern to business sectors worldwide; therefore using both clustering and statistical techniques is a better basis in mitigating bankruptcy to both practitioners and researchers. 出版者： Elsevier Ltd 出版日期： 2016-08-01 出處： Advanced engineering informatics, 2016-08, Vol.30 (3), p.376-389 版權： 2016 Elsevier Ltd 識別號： ISSN: 1474-0346 識別號： DOI: 10.1016/j.aei.2016.05.003
Appears in Collections:	[Department of Civil Engineering] journal & Dissertation

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