{"title":"Restructuring cost and its prediction analysis","authors":"Yi Ren, Dong Xiao","doi":"10.1002/jcaf.22659","DOIUrl":null,"url":null,"abstract":"<p>Given the ever-increasing occurrence of corporate restructuring, the relevance of accounting information related to restructuring became more important in financial reporting. We first examine how restructuring cost affects stock returns and find firms with significant restructuring costs have worse stock performance compared with firms without significant restructuring costs. We then establish prediction model to predict the incurrence of future restructuring cost. The performance of the prediction model achieves an AUC (the area under curve) of .84 on the training data and an AUC of .77 when using out-of-sample validation.</p>","PeriodicalId":44561,"journal":{"name":"Journal of Corporate Accounting and Finance","volume":null,"pages":null},"PeriodicalIF":0.9000,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Corporate Accounting and Finance","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jcaf.22659","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
引用次数: 0
Abstract
Given the ever-increasing occurrence of corporate restructuring, the relevance of accounting information related to restructuring became more important in financial reporting. We first examine how restructuring cost affects stock returns and find firms with significant restructuring costs have worse stock performance compared with firms without significant restructuring costs. We then establish prediction model to predict the incurrence of future restructuring cost. The performance of the prediction model achieves an AUC (the area under curve) of .84 on the training data and an AUC of .77 when using out-of-sample validation.