Role of Comprehensive Income in Predicting Bankruptcy

Asyrofa Rahmi (), Hung-Yuan Lu (), Deron Liang (), Dinda Novitasari () and Chih-Fong Tsai ()
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Asyrofa Rahmi: National Central University
Hung-Yuan Lu: California State University
Deron Liang: National Central University
Dinda Novitasari: National Central University
Chih-Fong Tsai: National Central University

Computational Economics, 2023, vol. 62, issue 2, No 9, 689-720

Abstract: Abstract This study examines the role of comprehensive income and its components, in addition to net income, as inputs to forecast bankruptcy. Using a matched sample of 466 (233 pairs) U.S. bankrupt and non-bankrupt firms from 1993 to 2014, we build a bankruptcy prediction model using random forest classification. Compared with the benchmark model, our proposed model’s accuracy increases by 1.5% and the Type I error decreases by up to 3%. A variable importance analysis reveals that comprehensive income is consistently the most useful variable for bankruptcy prediction. A variable interaction analysis shows that the top interaction pair includes one Altman variable and comprehensive income. Finally, we analyze bankrupt firms that our model identifies but the benchmark model misclassifies; we find that such firm’ other comprehensive income is consistently negative, suggesting that firms’ macroeconomic risk exposure plays a key role in bankruptcy prediction.

Keywords: Forecasting; Bankruptcy; Comprehensive income; Altman variables (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10614-022-10328-5

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