Exploring and weighting features for financially distressed construction companies using Swarm Inspired Projection algorithm. Issue 3 (August 2016)

• Record Type: Journal Article
• Authors: Chen, Jieh-Haur; Su, Mu-Chun; Annuerine Badjie, Bevan
• Is Part Of: Advanced engineering informatics. Volume 30:Issue 3(2016:Aug.)
• Page Start: 376
• Page End: 389
• Publication Date: 2016-08
• Subjects: Financial ratios -- Financial distress -- Construction companies -- Principal component analysis -- Swarm inspired projection algorithm; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2016.05.003
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: 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.