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Exploring Trends and Advancements in Financial Distress Prediction Research: A Bibliometric Study

Author

Listed:
  • Soumya Ranjan Sethi

    (Research Scholar, School of Management, National Institute of Technology, Rourkela, India)

  • Dushyant Ashok Mahadik

    (School of Management, National Institute of Technology, Rourkela, India.)

  • Rajkiran V. Bilolikar

    (Centre for Energy Studies, Administrative Staff College of India, Hyderabad, India)

Abstract

Due to the growing complexity and unpredictability of contemporary markets as evidenced by the financial crisis of the past ten years, the field of financial distress prediction (FDP) research is receiving more attention. For creditors, investors, and other stakeholders to make well-informed decisions on their financial relationships with a given entity, financial distress prediction is essential. This paper identified the risk indicators that are the cause of financial distress and latest tools and methods to predict financial distress with identifying the risk management strategies for eradicate the distress condition. In this context, this study explores the landscape of the literature published in this area. We have used systematic and bibliometric approach for studying the existing literature. For the study, we have collected articles from the Scopus database for the period 1985 to 2022. Science mapping technique has been used for the analysis data has been conducted with the help of Vosviewer and Biblioshiny software. Various important component of a literature review like most relevant authors, most relevant sources, keywords co-occurrence network, thematic analysis and others have been explored. The study will help the scholars and future researchers in getting a comprehensive understanding and insights in the concerned field and add to the existing body of literature.

Suggested Citation

  • Soumya Ranjan Sethi & Dushyant Ashok Mahadik & Rajkiran V. Bilolikar, 2024. "Exploring Trends and Advancements in Financial Distress Prediction Research: A Bibliometric Study," International Journal of Economics and Financial Issues, Econjournals, vol. 14(1), pages 164-179, January.
    • Handle: RePEc:eco:journ1:2024-01-16
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    References listed on IDEAS

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    1. Cummins, J David & Mahul, Olivier, 2003. "Optimal Insurance with Divergent Beliefs about Insurer Total Default Risk," Journal of Risk and Uncertainty, Springer, vol. 27(2), pages 121-138, October.
    2. M.J. Cobo & A.G. López-Herrera & E. Herrera-Viedma & F. Herrera, 2011. "Science mapping software tools: Review, analysis, and cooperative study among tools," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(7), pages 1382-1402, July.
    3. Balcaen, Sofie & Ooghe, Hubert, 2006. "35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems," The British Accounting Review, Elsevier, vol. 38(1), pages 63-93.
    4. Mian, Shehzad L & Smith, Clifford W, Jr, 1992. "Accounts Receivable Management Policy: Theory and Evidence," Journal of Finance, American Finance Association, vol. 47(1), pages 169-200, March.
    5. Zhao, Shuping & Xu, Kai & Wang, Zhao & Liang, Changyong & Lu, Wenxing & Chen, Bo, 2022. "Financial distress prediction by combining sentiment tone features," Economic Modelling, Elsevier, vol. 106(C).
    6. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    7. Amal Al Ali & Ahmed M. Khedr & Magdi El Bannany & Sakeena Kanakkayil, 2023. "GALSTM-FDP: A Time-Series Modeling Approach Using Hybrid GA and LSTM for Financial Distress Prediction," IJFS, MDPI, vol. 11(1), pages 1-15, February.
    8. Jiang, Cuiqing & Lyu, Ximei & Yuan, Yufei & Wang, Zhao & Ding, Yong, 2022. "Mining semantic features in current reports for financial distress prediction: Empirical evidence from unlisted public firms in China," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1086-1099.
    9. J. David Cummins & Mary A. Weiss, 2009. "Convergence of Insurance and Financial Markets: Hybrid and Securitized Risk‐Transfer Solutions," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 76(3), pages 493-545, September.
    10. John David Cummins & Olivier Mahul, 2003. "Optimal insurance with divergent beliefs about total default risk," Post-Print hal-01952121, HAL.
    11. M.J. Cobo & A.G. López‐Herrera & E. Herrera‐Viedma & F. Herrera, 2011. "Science mapping software tools: Review, analysis, and cooperative study among tools," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(7), pages 1382-1402, July.
    12. Zhao, Qi & Xu, Weijun & Ji, Yucheng, 2023. "Predicting financial distress of Chinese listed companies using machine learning: To what extent does textual disclosure matter?," International Review of Financial Analysis, Elsevier, vol. 89(C).
    13. Nees Jan Eck & Ludo Waltman, 2010. "Software survey: VOSviewer, a computer program for bibliometric mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(2), pages 523-538, August.
    14. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    15. Danielsson, Jon & Jorgensen, Bjorn N. & de Vries, Casper G., 2002. "Incentives for effective risk management," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1407-1425, July.
    16. E.C.M. Noyons & H.F. Moed & M. Luwel, 1999. "Combining mapping and citation analysis for evaluative bibliometric purposes: A bibliometric study," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 50(2), pages 115-131.
    17. Hamid Waqas & Rohani Md-Rus, 2018. "Predicting financial distress: Importance of accounting and firm-specific market variables for Pakistan’s listed firms," Cogent Economics & Finance, Taylor & Francis Journals, vol. 6(1), pages 1545739-154, January.
    18. Hernandez Tinoco, Mario & Wilson, Nick, 2013. "Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 394-419.
    19. Kim Long Tran & Hoang Anh Le & Thanh Hien Nguyen & Duc Trung Nguyen, 2022. "Explainable Machine Learning for Financial Distress Prediction: Evidence from Vietnam," Data, MDPI, vol. 7(11), pages 1-12, November.
    20. Altman, Edward I. & Marco, Giancarlo & Varetto, Franco, 1994. "Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience)," Journal of Banking & Finance, Elsevier, vol. 18(3), pages 505-529, May.
    21. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    22. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    23. Zmijewski, Me, 1984. "Methodological Issues Related To The Estimation Of Financial Distress Prediction Models," Journal of Accounting Research, Wiley Blackwell, vol. 22, pages 59-82.
    24. Chien-Min Kang & Ming-Chieh Wang & Lin Lin, 2022. "Financial Distress Prediction of Cooperative Financial Institutions—Evidence for Taiwan Credit Unions," IJFS, MDPI, vol. 10(2), pages 1-25, April.
    25. Huchang Liao & Ming Tang & Li Luo & Chunyang Li & Francisco Chiclana & Xiao-Jun Zeng, 2018. "A Bibliometric Analysis and Visualization of Medical Big Data Research," Sustainability, MDPI, vol. 10(1), pages 1-18, January.
    26. Zhitao Wang & Qiuyan Wang & Zi Nie & Bingcheng Li, 2022. "Corporate financial distress prediction based on controlling shareholder’s equity pledge," Applied Economics Letters, Taylor & Francis Journals, vol. 29(15), pages 1365-1368, September.
    27. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    28. Lagasio, Valentina & Brogi, Marina & Gallucci, Carmen & Santulli, Rosalia, 2023. "May board committees reduce the probability of financial distress? A survival analysis on Italian listed companies," International Review of Financial Analysis, Elsevier, vol. 87(C).
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    More about this item

    Keywords

    Financial Distress Prediction; Bibliometric Analysis; Forecasting; Financial Distress; Risk;
    All these keywords.

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G40 - Financial Economics - - Behavioral Finance - - - General

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