Using textual analysis in bankruptcy prediction: Evidence from Indian firms under IBC

  • 498 Views
  • 240 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

Identifying and managing credit risk is vital for all lending institutions. Historically, credit risk is assessed using financial data from published financial statements. However, research indicates that the ability to detect financial hardship may be improved by textual analysis of firms’ disclosed records. This study aims to establish an association between themes and words from Management Discussion and Analysis (MDA) reports of firms and corporate failures. The study took a sample of 57 Indian listed firms declared bankrupt under the Insolvency and Bankruptcy Code (IBC) along with a matched sample of 55 solvent firms (matched by industry and size) for the period of FY2011–2019. The first part of analysis identifies negative words from the published reports and compares them with the negative words of the Loughran-McDonald dictionary. Then a thematic analysis is done to identify the key themes from the MDA reports and the significant themes are validated with their corresponding financial ratios in the third step using a panel logistic regression. Word analysis results show that IBC firms have significantly greater negative tone (2.21 percent) as against 1.30 percent of solvent firms. Thematic analysis results show that manageability, activity and performance are significant themes for predicting financial distress. Financial variables such as ownership pattern, promoters’ shares pledged, return on capital employed, asset utilization are some of the ratios in sync with the key themes. The study recommends that lenders and other stakeholders should look beyond financial statements which may be ‘window dressed’ by firms to qualitative disclosures in annual reports which may forewarn against impending financial distress.

Acknowledgments
The infrastructural support provided by FORE School of Management, New Delhi in completing this paper is gratefully acknowledged.

view full abstract hide full abstract
    • Table 1. Result from the test of proportions
    • Table 2. Themes and their weights
    • Table 3. Results from logistic regression with theme scores
    • Table 4. List of financial variables
    • Table 5. Panel logistic regression results
    • Conceptualization
      Vandana Gupta
    • Data curation
      Vandana Gupta
    • Investigation
      Vandana Gupta, Aditya Banerjee
    • Methodology
      Vandana Gupta, Aditya Banerjee
    • Project administration
      Vandana Gupta, Aditya Banerjee
    • Resources
      Vandana Gupta, Aditya Banerjee
    • Supervision
      Vandana Gupta
    • Visualization
      Vandana Gupta
    • Writing – original draft
      Vandana Gupta
    • Writing – review & editing
      Vandana Gupta
    • Formal Analysis
      Aditya Banerjee
    • Software
      Aditya Banerjee
    • Validation
      Aditya Banerjee