回归金融原理： 企业财务危机预警研究述评与展望

朱武祥; 廖静秋; 詹子良; 谭智佳

doi:10.16511/j.cnki.qhdxxb.2023.21.008

PDF(2501 KB)

清华大学学报（自然科学版） ›› 2023, Vol. 63 ›› Issue (9) : 1467-1482. DOI: 10.16511/j.cnki.qhdxxb.2023.21.008

经济与公共管理

回归金融原理：企业财务危机预警研究述评与展望

朱武祥¹, 廖静秋¹, 詹子良¹, 谭智佳²

作者信息 +

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

ZHU Wuxiang¹, LIAO Jingqiu¹, ZHAN Ziliang¹, TAN Zhijia²

Author information +

文章历史 +

摘要

财务危机预警一直受到企业、投资者和政府的关注, 但已有预警模型方法的预警能力不能满足市场期许, 甚至引发争议, 政府部门、市场主体对优化债券违约风险识别与预警方法的需求强烈。该文系统梳理了1932至2020年间256篇财务危机预警文献, 从财务危机的概念基础、预警模型的原理及迭代、预警指标选取、预警效率评估等维度进行了述评, 指出了现有财务危机预警模型研究的3个现象、方法论特征及局限性。提出了一个跨模型可比的财务危机预警模型评价框架和“一个原则、三个方向”研究改进展望, 主张回归金融原理, 从而更加精确地进行企业财务危机绝对风险的评估、预警与治理。

Abstract

[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.

导出引用

朱武祥, 廖静秋, 詹子良, 谭智佳. 回归金融原理：企业财务危机预警研究述评与展望[J]. 清华大学学报（自然科学版）. 2023, 63(9): 1467-1482 https://doi.org/10.16511/j.cnki.qhdxxb.2023.21.008

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

中图分类号： TP391.1

参考文献

[1] 肖毅, 熊凯伦, 张希. 基于TEI@I方法论的企业财务风险预警模型研究[J]. 管理评论, 2020, 32(7): 226-235. XIAO Y, XIONG K L, ZHANG X. Enterprise financial risk early warning model based on TEI@I methodology[J]. Management Review, 2020, 32(7): 226-235. (in Chinese)
[2] 中国银行保险监督管理委员会. 商业银行不良贷款情况表[EB/OL].[2022-12-19]. http://www.cbirc.gov.cn/cn/view/pages/tongjishuju/tongjishuju.html. China Banking and Insurance Regulatory Commission. Statement of non-performing loans of commercial banks[EB/OL].[2022-12-19]. http://www.cbirc.gov.cn/cn/view/pages/tongjishuju/tongjishuju.html. (in Chinese)
[3] 国家发展改革委政研室. 政策研究室副主任兼委新闻发言人孟玮问答之六: 目前企业债券注册发行的进展?在应对债务风险以及违约处置方面, 将会有哪些新措施?[EB/OL]. (2020-11-18)[2022-12-19].https://www.ndrc.gov.cn/xxgk/jd/jd/202011/t20201118_1250746.html. National Development and Reform Commission. The progress of enterprise bond registration and issuance at present? What new measures will be taken to deal with debt risks and the resolution of defaults?[EB/OL]. (2020-11-18)[2022-12-19].https://www.ndrc.gov.cn/xxgk/jd/jd/202011/t20201118_1250746.html. (in Chinese)
[4] 中国银保监会党委. 持之以恒防范化解重大金融风险[N]. 中国银行保险报, 2022-05-17(001) China Banking and Insurance Regulatory Commission. Statement of non-performing loans of commercial banks[N]. China Banking and Insurance News, 2022-05-17(001). (in Chinese)
[5] 防范化解重大风险战略解读编写组. 防范化解重大风险战略解读[M]. 北京: 中共中央党校出版社, 2019. Interpretation and Compilation Group of Strategies for Preventing and Defusing Major Risks. Interpretation of strategies for preventing and defusing major risks[M]. Beijing: Publication of Party School of the CPC Central Committee, 2019. (in Chinese)
[6] ALTMAN E I. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy[J]. The Journal of Finance, 1968, 23(4): 589-609.
[7] BEAVER W H. Financial ratios as predictors of failure[J]. Journal of Accounting Research, 1966, 4: 71-111.
[8] ALTMAN E I, HALDEMAN R G, NARAYANAN P. ZETATM analysis a new model to identify bankruptcy risk of corporations[J]. Journal of Banking & Finance, 1977, 1(1): 29-54.
[9] OHLSON J A. Financial ratios and the probabilistic prediction of bankruptcy[J]. Journal of Accounting Research, 1980, 18(1): 109-131.
[10] ODOM M D, SHARDA R. A neural network model for bankruptcy prediction[C]// 1990 IJCNN International Joint Conference on Neural Networks. San Diego, USA: IEEE, 1990: 163-168.
[11] COATS P K, FANT L F. Recognizing financial distress patterns using a neural network tool[J]. Financial Management, 1993, 22(3): 142-155.
[12] 李帆, 杜志涛, 李玲娟. 企业财务预警模型: 理论回顾及其评论[J]. 管理评论, 2011, 23(9): 144-151. LI F, DU Z T, LI L J. The model of predicting finance distress of enterprise: Theory evolution and its review[J]. Management Review, 2011, 23(9): 144-151. (in Chinese)
[13] LEE S, CHOI W S. A multi-industry bankruptcy prediction model using back-propagation neural network and multivariate discriminant analysis[J]. Expert Systems with Applications, 2013, 40(8): 2941-2946.
[14] CHEN N, RIBEIRO B, CHEN A. Financial credit risk assessment: A recent review[J]. Artificial Intelligence Review, 2016, 45(1): 1-23.
[15] WANG C L, CHUGH H. Entrepreneurial learning: Past research and future challenges[J]. International Journal of Management Reviews, 2014, 16(1): 24-61.
[16] 国际货币基金组织. 2020年4月《世界经济展望》[EB/OL]. (2020-04-14)[2022-12-19]. https://www.imf.org/zh/Publications/WEO/Issues/2020/04/14/weo-april-2020. International Monetary Fund. World economic outlook, April 2020: The great lockdown[EB/OL]. (2020-04-14)[2022-12-19]. https://www.imf.org/zh/Publications/WEO/Issues/2020/04/14/weo-april-2020. (in Chinese)
[17] 汤铎铎, 刘学良, 倪红福, 等. 全球经济大变局、中国潜在增长率与后疫情时期高质量发展[J]. 经济研究, 2020, 55(8): 4-23. TANG D D, LIU X L, NI H F, et al. The changing global economic landscape and China's potential growth rate and high-quality development in the post-epidemic era[J]. Economic Research Journal, 2020, 55(8): 4-23. (in Chinese)
[18] KETZ J E. The effect of general price-level adjustments on the predictive ability of financial ratios[J]. Journal of Accounting Research, 1978, 16: 273-284.
[19] ZMIJEWSKI M E. Methodological issues related to the estimation of financial distress prediction models[J]. Journal of Accounting Research, 1984, 22: 59-82.
[20] MENSAH Y M. An examination of the stationarity of multivariate bankruptcy prediction models: A methodological study[J]. Journal of accounting research, 1984, 22(1): 380-395.
[21] WOOD D, PIESSE J. The information value of failure predictions in credit assessment[J]. Journal of Banking & Finance, 1988, 12(2): 275-292.
[22] PLATT H D, PLATT M B. Development of a class of stable predictive variables: The case of bankruptcy prediction[J]. Journal of Business Finance & Accounting, 1990, 17(1): 31-51.
[23] PLATT H D, PLATT M B, PEDERSEN J G. Bankruptcy discrimination with real variables[J]. Journal of Business Finance & Accounting, 1994, 21(4): 491-510.
[24] LIN S W, YING K C, CHEN S C, et al. Particle swarm optimization for parameter determination and feature selection of support vector machines[J]. Expert Systems with Applications, 2008, 35(4): 1817-1824.
[25] LI H, SUN J. Empirical research of hybridizing principal component analysis with multivariate discriminant analysis and logistic regression for business failure prediction[J]. Expert Systems with Applications, 2011, 38(5): 6244-6253.
[26] SHUMWAY T. Forecasting bankruptcy more accurately: A simple hazard model[J]. The Journal of Business, 2001, 74(1): 101-124.
[27] CAMPBELL J Y, HILSCHER J, SZILAGYI J. In search of distress risk[J]. The Journal of Finance, 2008, 63(6): 2899-2939.
[28] MERTON R C. On the pricing of corporate debt: The risk structure of interest rates[J]. The Journal of Finance, 1974, 29(2): 449-470.
[29] 谷祺, 刘淑莲. 财务危机企业投资行为分析与对策[J]. 会计研究, 1999(10): 28-31. GU Q, LIU S L. Analysis and countermeasures of investment behavior of enterprises in financial crisis[J]. Accounting Research, 1999(10): 28-31. (in Chinese)
[30] CARMICHAEL D R. The auditor's reporting obligation: The meaning and implementation of the fourth standard of reporting[M]. New York: American Institute of Certified Public Accountants, 1972.
[31] 吕长江, 韩慧博. 财务困境、财务困境间接成本与公司业绩[J]. 南开管理评论, 2004, 7(3): 80-85. LV C J, HAN H B. Corporate financial distress, financial distress indirect cost and performance[J]. Nankai Business Review, 2004, 7(3): 80-85. (in Chinese)
[32] 吕长江, 徐丽莉, 周琳. 上市公司财务困境与财务破产的比较分析[J]. 经济研究, 2004, 39(8): 64-73. LV C J, XU L L, ZHOU L. Comparative analysis of corporate financial distress and financial bankruptcy[J]. Economic Research Journal, 2004, 39(8): 64-73. (in Chinese)
[33] 刘翰林. 我国上市公司财务危机预警模型实证研究[J]. 数量经济技术经济研究, 2002, 19(7): 115-118. LIU H L. The empirical study on prewarning model of financial crisis of China's listed companies[J]. The Journal of Quantitative & Technical Economics, 2002, 19(7): 115-118. (in Chinese)
[34] 李秉成, 梁慧, 刘芬芳. 上市公司财务困境"A记分法"预测模型研究[J]. 管理评论, 2005, 17(9): 15-19. LI B C, LIANG H, LIU F F. Research on the A-scoring analytical model for listed Companies' financial distress[J]. Management Review, 2005, 17(9): 15-19. (in Chinese)
[35] 刘京军, 秦宛顺. 上市公司陷入财务困境可能性研究[J]. 金融研究, 2006(11): 44-52. LIU J J, QIN W S. The possibility of financial distress of listed company[J]. Journal of Financial Research, 2006(11): 44-52. (in Chinese)
[36] 吴世农, 卢贤义. 我国上市公司财务困境的预测模型研究[J]. 经济研究, 2001(6): 46-55, 96. WU S N, LU X Y. A study of models for predicting financial distress in China's listed companies[J]. Economic Research Journal, 2001(6): 46-55, 96. (in Chinese)
[37] ROSS S, WESTERFIELD R, JAFFE J F. Corporate finance[M]. 10th ed. New York: McGraw-Hill Education, 2012.
[38] 吴超鹏, 吴世农. 基于价值创造和公司治理的财务状态分析与预测模型研究[J]. 经济研究, 2005, 40(11): 99-110. WU C P, WU S N. A study on prediction model for changes of financial status based on value-creation and corporate governance[J]. Economic Research Journal, 2005, 40(11): 99-110. (in Chinese)
[39] LAITINEN E K. Predicting a corporate credit analyst's risk estimate by logistic and linear models[J]. International Review of Financial Analysis, 1999, 8(2): 97-121.
[40] 王今, 韩文秀, 侯岚. 西方企业财务危机预测方法评析[J]. 中国软科学, 2002(6): 109-112. WANG J, HAN W X, HOU L. Predicting method of financial distress in the developed world: A review[J]. China Soft Science, 2002(6): 109-112. (in Chinese)
[41] LAHSASNA A, AINON R N, WAH T Y. Credit risk evaluation decision modeling through optimized fuzzy classifier[C]// 2008 International Symposium on Information Technology. Kuala Lumpur, Malaysia: IEEE, 2008: 1-8.
[42] CHEN X H, ZHONG Q. On consumer credit scoring based on multi-criteria fuzzy logic[C]// 2009 International Conference on Business Intelligence and Financial Engineering. Beijing, China: IEEE, 2009: 765-768.
[43] ZHAO H M. A multi-objective genetic programming approach to developing Pareto optimal decision trees[J]. Decision Support Systems, 2007, 43(3): 809-826.
[44] LI H, SUN J, WU J. Predicting business failure using classification and regression tree: An empirical comparison with popular classical statistical methods and top classification mining methods[J]. Expert Systems with Applications, 2010, 37(8): 5895-5904.
[45] ZHOU L G, LAI K K, YU L A. Least squares support vector machines ensemble models for credit scoring[J]. Expert Systems with Applications, 2010, 37(1): 127-133.
[46] 方匡南, 杨阳. SGL-SVM方法研究及其在财务困境预测中的应用[J]. 统计研究, 2018, 35(8): 104-115. FANG K N, YANG Y. SGL-SVM with its application in forecasting corporate financial distress[J]. Statistical Research, 2018, 35(8): 104-115. (in Chinese)
[47] PARK C S, HAN I. A case-based reasoning with the feature weights derived by analytic hierarchy process for bankruptcy prediction[J]. Expert Systems with Applications, 2002, 23(3): 255-264.
[48] SARKAR S, SRIRAM R S. Bayesian models for early warning of bank failures[J]. Management Science, 2001, 47(11): 1457-1475.
[49] KADAM A, LENK P. Bayesian inference for issuer heterogeneity in credit ratings migration[J]. Journal of Banking & Finance, 2008, 32(10): 2267-2274.
[50] CHEN N, RIBEIRO B, VIEIRA A S, et al. A genetic algorithm-based approach to cost-sensitive bankruptcy prediction[J]. Expert Systems with Applications, 2011, 38(10): 12939-12945.
[51] CARLING K, JACOBSON T, LINDÉ J, et al. Corporate credit risk modeling and the macroeconomy[J]. Journal of Banking & Finance, 2007, 31(3): 845-868.
[52] KUMAR P R, RAVI V. Bankruptcy prediction in banks and firms via statistical and intelligent techniques: A review[J]. European Journal of Operational Research, 2007, 180(1): 1-28.
[53] BLANCO A, PINO-MEJÍAS R, LARA J, et al. Credit scoring models for the microfinance industry using neural networks: Evidence from Peru[J]. Expert Systems with Applications, 2013, 40(1): 356-364.
[54] HUNG C, CHEN J H. A selective ensemble based on expected probabilities for bankruptcy prediction[J]. Expert Systems with Applications, 2009, 36(3): 5297-5303.
[55] BELLOVARY J L, GIACOMINO D E, AKERS M D. A review of bankruptcy prediction studies: 1930 to present[J]. Journal of Financial Education, 2007, 33: 1-42.
[56] LIN W Y, HU Y H, TSAI C F. Machine learning in financial crisis prediction: A survey[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 2012, 42(2): 421-436.
[57] CROSBIE P, BOHN J. Modeling default risk[Z/OL]. (2003-12-18)[2022-12-19]. https://www.moodysanalytics.com/-/media/whitepaper/before-2011/12-18-03-modeling-default-risk.pdf.
[58] YEH C C, LIN F Y, HSU C Y. A hybrid KMV model, random forests and rough set theory approach for credit rating[J]. Knowledge-Based Systems, 2012, 33: 166-172.
[59] LEE W C. Redefinition of the KMV model's optimal default point based on genetic algorithms: Evidence from Taiwan[J]. Expert Systems with Applications, 2011, 38(8): 10107-10113.
[60] ALTMAN E I, BRENNER M. Information effects and stock market response to signs of firm deterioration[J]. The Journal of Financial and Quantitative Analysis, 1981, 16(1): 35-51.
[61] JOHNSON C G. Ratio analysis and the prediction of firm failure[J]. The Journal of Finance, 1970, 25(5): 1166-1168.
[62] 钱爱民, 张淑君, 程幸. 基于自由现金流量的财务预警指标体系的构建与检验——来自中国机械制造业A股上市公司的经验数据[J]. 中国软科学, 2008(9): 148-155. QIAN A M, ZHANG S J, CHENG X. Construction and examination of financial early-warning system based on free cash flow: Evidence from China's a share listed companies in manufacturing industry[J]. China Soft Science, 2008(9): 148-155. (in Chinese)
[63] 周首华, 杨济华, 王平. 论财务危机的预警分析——F分数模式[J]. 会计研究, 1996(8): 8-11. ZHOU S H, YANG J H, WANG P. On the early warning analysis of financial crisis: F-score model[J]. Accounting Research, 1996(8): 8-11. (in Chinese)
[64] 杨淑娥, 徐伟刚. 上市公司财务预警模型——Y分数模型的实证研究[J]. 中国软科学, 2003(1): 56-60. YANG S E, XU W G. Financial affairs in early warning model for listed companies-an empirical study on Y market's model[J]. China Soft Science, 2003(1): 56-60. (in Chinese)
[65] ASQUITH P, GERTNER R, SCHARFSTEIN D. Anatomy of financial distress: An examination of junk-bond issuers[J]. The Quarterly Journal of Economics, 1994, 109(3): 625-658.
[66] 王昱, 杨珊珊. 考虑多维效率的上市公司财务困境预警研究[J]. 中国管理科学, 2021, 29(2): 32-41. WANG Y, YANG S S. Corporate financial distress prediction based on multi-dimensional efficiency indicators[J]. Chinese Journal of Management Science, 2021, 29(2): 32-41. (in Chinese)
[67] 王竹泉, 宋晓缤, 王苑琢. 我国实体经济短期金融风险的评价与研判——存量与流量兼顾的短期财务风险综合评估与预警[J]. 管理世界, 2020, 36(10): 156-169. WANG Z Q, SONG X B, WANG Y Z. Objective evaluation and rational judgment of short-term financial risk in China's real economy: Comprehensive assessment and early warning of short-term financial risk considering stock and flow[J]. Management World, 2020, 36(10): 156-169. (in Chinese)
[68] 郭斌, 戴小敏, 曾勇, 等. 我国企业危机预警模型研究——以财务与非财务因素构建[J]. 金融研究, 2006(2): 78-87. GUO B, DAI X M, ZENG Y, et al. On the financial distress warning model[J]. Journal of Financial Research, 2006(2): 78-87. (in Chinese)
[69] BELKAOUI A. Industrial bond ratings: A new look[J]. Financial Management, 1980, 9(3): 44-51.
[70] ROSE P S, ANDREWS W T, GIROUX G A. Predicting business failure: A macroeconomic perspective[J]. Journal of Accounting, Auditing and Finance, 1982, 6(1): 20-31.
[71] CHAVA S, JARROW R A. Bankruptcy prediction with industry effects[J]. European Finance Review, 2004, 8(4): 537-569.
[72] GRUNERT J, NORDEN L, WEBER M. The role of non-financial factors in internal credit ratings[J]. Journal of Banking & Finance, 2005, 29(2): 509-531.
[73] TOBBACK E, BELLOTTI T, MOEYERSOMS J, et al. Bankruptcy prediction for SMES using relational data[J]. Decision Support Systems, 2017, 102: 69-81.
[74] KOU G, XU Y, PENG Y, et al. Bankruptcy prediction for SMEs using transactional data and two-stage multiobjective feature selection[J]. Decision Support Systems, 2021, 140: 113429.
[75] HAN S, ZHOU X. Informed bond trading, corporate yield spreads, and corporate default prediction[J]. Management Science, 2014, 60(3): 675-694.
[76] YUAN G, WANG H Q, ZENG T, et al. The dynamical mechanism for SMEs evolution under the hologram approach[J]. SSRN Electron Journal, 2019.
[77] DAILY C M, DALTON D R. Corporate governance and the bankrupt firm: An empirical assessment[J]. Strategic Management Journal, 1994, 15(8): 643-654.
[78] LIANG D, LU C C, TSAI C F, et al. Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study[J]. European Journal of Operational Research, 2016, 252(2): 561-572.
[79] DONKER H, SANTEN B, ZAHIR S. Ownership structure and the likelihood of financial distress in the Netherlands[J]. Applied Financial Economics, 2009, 19(21): 1687-1696.
[80] CIAMPI F. Corporate governance characteristics and default prediction modeling for small enterprises. An empirical analysis of Italian firms[J]. Journal of Business Research, 2015, 68(5): 1012-1025.
[81] GUPTA M C, HUEFNER R J. A cluster analysis study of financial ratios and industry characteristics[J]. Journal of Accounting Research, 1972, 10(1): 77-95.
[82] KLUGER B D, SHIELDS D. Auditor changes, information quality and bankruptcy prediction[J]. Managerial and Decision Economics, 1989, 10(4): 275-282.
[83] TENNYSON B M, INGRAM R W, DUGAN M T. Assessing the information content of narrative disclosures in explaining bankruptcy[J]. Journal of Business Finance & Accounting, 1990, 17(3): 391-410.
[84] CECCHINI M, AYTUG H, KOEHLER G J, et al. Making words work: Using financial text as a predictor of financial events[J]. Decision Support Systems, 2010, 50(1): 164-175.
[85] 陈艺云. 基于信息披露文本的上市公司财务困境预测: 以中文年报管理层讨论与分析为样本的研究[J]. 中国管理科学, 2019, 27(7): 23-34. CHEN Y Y. Forecasting financial distress of listed companies with textual content of the information disclosure: A study based MD&A in Chinese annual reports[J]. Chinese Journal of Management Science, 2019, 27(7): 23-34. (in Chinese)
[86] HAFIZ A, LUKUMON O, MUHAMMAD B, et al. Bankruptcy prediction of construction businesses: Towards a big data analytics approach[C]//2015 IEEE First International Conference on Big Data Computing Service and Applications. Redwood City, USA: IEEE, 2015: 347-352.
[87] RAJAN U, SERU A, VIG V. The failure of models that predict failure: Distance, incentives, and defaults[J]. Journal of Financial Economics, 2015, 115(2): 237-260.
[88] GIAMMARINO R M. The resolution of financial distress[J]. The Review of Financial Studies, 1989, 2(1): 25-47.
[89] HOSHI T, KASHYAP A, SCHARFSTEIN D. The role of banks in reducing the costs of financial distress in Japan[J]. Journal of Financial Economics, 1990, 27(1): 67-88.
[90] OPLER T C, TITMAN S. Financial distress and corporate performance[J]. The Journal of Finance, 1994, 49(3): 1015-1040.

PDF(2501 KB)

Accesses

Citation

Detail

段落导航

收稿日期	出版日期
2023-03-20	2023-09-15
发布日期
2023-08-19

选择文件类型/文献管理软件名称

选择包含的内容

摘要

Abstract

关键词

Key words

引用本文

{{custom_sec.title}}

{{custom_sec.title}}

参考文献

访问统计

模态框（Modal）标题

选择文件类型/文献管理软件名称

选择包含的内容

摘要

Abstract

关键词

Key words

引用本文

{{custom_sec.title}}

{{custom_sec.title}}

参考文献

访问统计