Mining semantic features in current reports for financial distress prediction: Empirical evidence from unlisted public firms in China

Cuiqing Jiang, Ximei Lyu, Yufei Yuan, Zhao Wang and Yong Ding

International Journal of Forecasting, 2022, vol. 38, issue 3, 1086-1099

Abstract: It is difficult to predict the financial distress of unlisted public firms due to their longer disclosure cycle of accounting information and more inadequate continuity of market trading information compared to listed firms. In this paper, we propose a framework to predict the financial distress of unlisted public firms using current reports. Specifically, to better represent the meaning of current report texts, we propose a semantic feature extraction method based on a word embedding technology. Empirical results show that current reports contain more effective information for predicting the financial distress of unlisted public firms compared with periodic reports. In addition, semantic features extracted using our proposed method significantly improve the predictive performance, and their enhancing effect is superior to that of topic features and sentiment features. Our study also provides implications for stakeholders such as investors and creditors.

Keywords: Financial distress; Current report; Semantic feature; Word embedding; Unlisted public firm (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:38:y:2022:i:3:p:1086-1099

DOI: 10.1016/j.ijforecast.2021.06.011

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