Systematic review and future perspectives of financial distress prediction studies: Back to the principle of finance

ZHU Wuxiang; LIAO Jingqiu; ZHAN Ziliang; TAN Zhijia

doi:10.16511/j.cnki.qhdxxb.2023.21.008

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Journal of Tsinghua University(Science and Technology) ›› 2023, Vol. 63 ›› Issue (9) : 1467-1482. DOI: 10.16511/j.cnki.qhdxxb.2023.21.008

ECONOMIC AND PUBLIC MANAGEMENT

Systematic review and future perspectives of financial distress prediction studies: Back to the principle of finance

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[Significance] Because of multiple factors, such as deleveraging policy, slowing economic growth, trade friction, and the COVID-19 pandemic, debt defaults are occurring with increasing frequency, which could trigger risk contagion and even lead to systemic financial risks. However, some facts indicate that the existing financial distress prediction model is not sufficiently effective; for example, the nonperforming loan ratio of commercial banks shows a rising trend, and the downgrade of ratings usually lags considerably. Thus, government departments and market entities have a strong demand for improving and optimizing the financial distress prediction model, which is necessary to realize risk identification and early warning. An effective prediction model can provide early warnings of investment risks and help financial institutions and investors reduce losses, assist regulators in establishing a multichannel default disposal mechanism, and improve the credit environment of the capital market.[Progress] Based on an extensive literature search in top journals and conferences from 1932 to 2020, this paper reviews four topics, including the financial distress definition, statistical model, variable selection, and model efficiency evaluation method, then further summarizes three research anomalies: 1) Existing financial distress prediction models often focus on the prediction of deep crises, such as insolvency and bankruptcy, which may lead to a delayed warning and market panic. 2) The innovation of financial distress prediction research focuses on applying new computer algorithms and statistical models as well as considering nonfinancial information. One confusing fact is that the judgment of financial distress depends on the selected model, indicators, and sample set rather than the fundamental factors of the enterprise; thus, different prediction models may produce contradictory results on the judgment of the same enterprise. 3) The identification of financial distress relies on comparing an enterprise's future capital cash flow and rigid payment. However, most of the existing financial distress prediction models apply a multivariate weighting method according to common historical financial indicators.[Conclusions and Prospects] This paper proposes a cross-model evaluation framework to compare their financial distress prediction effectiveness and provides improvement suggestions including “one principle, three directions.” The one principle indicates that to accurately assess and manage the absolute risk of financial distress, the study of financial distress prediction should return to the financial principle and pay attention to future capital cash flow. The three directions that need to pay attention include: 1) early financial distress warnings, such as liquidity crisis warnings; 2) steady repayment sources, including operating cash inflows, reliable asset disposal earnings, and refinancing, rather than relying on the total assets of the balance sheet, current assets, and other indicators; 3) financing contracts and full scenario analyses of future capital cash outflows rather than just current ratio, quick ratio, asset-liability ratio, and other liability indicators. In the future, with the development of big data and the improvement in information transmission efficiency, corporate information disclosure will be considerably enhanced, allowing more accurate cash flow and repayment prediction. A prediction model assessing absolute financial distress risk has greater potential.

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financial distress / prediction model / future perspectives

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ZHU Wuxiang, LIAO Jingqiu, ZHAN Ziliang, TAN Zhijia. Systematic review and future perspectives of financial distress prediction studies: Back to the principle of finance[J]. Journal of Tsinghua University(Science and Technology). 2023, 63(9): 1467-1482 https://doi.org/10.16511/j.cnki.qhdxxb.2023.21.008

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