IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/112699.html
   My bibliography  Save this paper

Fundamental Credit Analysis through Dynamical Modeling and Simulation of the Balance Sheet: Applications to Chinese Real Estate Developers

Author

Listed:
  • Xu, Jack

Abstract

Fundamental credit analysis is widely performed by fixed income analysts and financial institutions to assess the credit risk of individual companies based on their financial data, notably the financial statements reported by the companies. Yet, the conventional analysis has not developed a computational method to forecast, directly from a company’s financial statements, the default probability, the recovery rate, and ultimately the fundamental valuation of a company’s credit risk in terms of credit spreads to risk-free rate. This paper introduces a generalizable approach to achieve these goals by implementing fundamental credit analysis in dynamical models. When combined with Monte-Carlo simulation, the current methodology naturally combines several novel features in the same forecast algorithm: 1. integrating default (defined as the state of negative cash) and recovery rate (under liquidation scenario) through the same defaulted balance sheet, 2. valuing the corporate real options manifested as planning in the amount of borrowing and expenditure, 3. embedding macro-economic and macro-financing conditions, and 4. forecasting the joint default risk of multiple companies. The method is applied to the Chinese real estate industry to forecast for several listed developers their forward default probabilities and associated recovery rates, and the fair-value par coupon curves of senior unsecured debt, using as inputs 6-8 years of their annual financial statements with 2020 as the latest. The results show both agreements and disagreements with the market-traded credit spreads at early April 2021, the time of these forecasts. The models forecasted much wider than market spreads on the big three developers, particularly pricing Evergrande in distressed levels. After setting up additional generic industry models, the current methodology is capable of computing default risk and debt valuation on large-scale of companies based on their historical financial statements.

Suggested Citation

  • Xu, Jack, 2022. "Fundamental Credit Analysis through Dynamical Modeling and Simulation of the Balance Sheet: Applications to Chinese Real Estate Developers," MPRA Paper 112699, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:112699
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/112699/1/MPRA_paper_112699.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    2. Chunsheng Zhou, 1997. "A jump-diffusion approach to modeling credit risk and valuing defaultable securities," Finance and Economics Discussion Series 1997-15, Board of Governors of the Federal Reserve System (U.S.).
    3. 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.
    4. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    5. Afik, Zvika & Arad, Ohad & Galil, Koresh, 2016. "Using Merton model for default prediction: An empirical assessment of selected alternatives," Journal of Empirical Finance, Elsevier, vol. 35(C), pages 43-67.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Koresh Galil & Neta Gilat, 2019. "Predicting Default More Accurately: To Proxy or Not to Proxy for Default?," International Review of Finance, International Review of Finance Ltd., vol. 19(4), pages 731-758, December.
    2. Mirza, Nawazish & Rahat, Birjees & Naqvi, Bushra & Rizvi, Syed Kumail Abbas, 2023. "Impact of Covid-19 on corporate solvency and possible policy responses in the EU," The Quarterly Review of Economics and Finance, Elsevier, vol. 87(C), pages 181-190.
    3. Trueck, Stefan & Rachev, Svetlozar T., 2008. "Rating Based Modeling of Credit Risk," Elsevier Monographs, Elsevier, edition 1, number 9780123736833.
    4. Giordani, Paolo & Jacobson, Tor & Schedvin, Erik von & Villani, Mattias, 2014. "Taking the Twists into Account: Predicting Firm Bankruptcy Risk with Splines of Financial Ratios," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 49(4), pages 1071-1099, August.
    5. Lin, Hsiou-Wei William & Lo, Huai-Chun & Wu, Ruei-Shian, 2016. "Modeling default prediction with earnings management," Pacific-Basin Finance Journal, Elsevier, vol. 40(PB), pages 306-322.
    6. Ahsan Habib & Mabel D' Costa & Hedy Jiaying Huang & Md. Borhan Uddin Bhuiyan & Li Sun, 2020. "Determinants and consequences of financial distress: review of the empirical literature," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(S1), pages 1023-1075, April.
    7. Huang, Hsing-Hua & Lee, Han-Hsing, 2013. "Product market competition and credit risk," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 324-340.
    8. Giesecke, Kay & Longstaff, Francis A. & Schaefer, Stephen & Strebulaev, Ilya, 2011. "Corporate bond default risk: A 150-year perspective," Journal of Financial Economics, Elsevier, vol. 102(2), pages 233-250.
    9. Ferreira Filipe, Sara & Grammatikos, Theoharry & Michala, Dimitra, 2016. "Pricing default risk: The good, the bad, and the anomaly," Journal of Financial Stability, Elsevier, vol. 26(C), pages 190-213.
    10. Xu, Si & He, Xiaoyi & Cao, Chunfang, 2023. "Struggle for survival in credit crunch: The effect of interest rate deregulation in China," China Economic Review, Elsevier, vol. 77(C).
    11. John Y. Campbell & Jens Hilscher & Jan Szilagyi, 2008. "In Search of Distress Risk," Journal of Finance, American Finance Association, vol. 63(6), pages 2899-2939, December.
    12. Tao, Qizhi & Chen, Carl & Lu, Rui & Zhang, Ting, 2017. "Underfunding or distress? An analysis of corporate pension underfunding and the cross-section of expected stock returns," International Review of Economics & Finance, Elsevier, vol. 48(C), pages 116-133.
    13. Wolfgang Härdle & Yuh-Jye Lee & Dorothea Schäfer & Yi-Ren Yeh, 2009. "Variable selection and oversampling in the use of smooth support vector machines for predicting the default risk of companies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(6), pages 512-534.
    14. Mark Clintworth & Dimitrios Lyridis & Evangelos Boulougouris, 2023. "Financial risk assessment in shipping: a holistic machine learning based methodology," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 25(1), pages 90-121, March.
    15. Chengru Hu & Wei Jiang, 2019. "Managerial risk incentives and accounting conservatism," Review of Quantitative Finance and Accounting, Springer, vol. 52(3), pages 781-813, April.
    16. Hamadi Matoussi & Aida Abdelmoula, 2008. "Using A Neural Network-Based Methodology for Credit–Risk Evaluation of A Tunisian Bank," Working Papers 408, Economic Research Forum, revised 06 Jan 2008.
    17. En-Der Su & Shih-Ming Huang, 2010. "Comparing Firm Failure Predictions Between Logit, KMV, and ZPP Models: Evidence from Taiwan’s Electronics Industry," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 17(3), pages 209-239, September.
    18. Charitou, Andreas & Dionysiou, Dionysia & Lambertides, Neophytos & Trigeorgis, Lenos, 2013. "Alternative bankruptcy prediction models using option-pricing theory," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2329-2341.
    19. Anand Deo & Sandeep Juneja, 2021. "Credit Risk: Simple Closed-Form Approximate Maximum Likelihood Estimator," Operations Research, INFORMS, vol. 69(2), pages 361-379, March.
    20. Barinov, Alexander, 2023. "Profitability anomaly and aggregate volatility risk," Journal of Financial Markets, Elsevier, vol. 64(C).

    More about this item

    Keywords

    fundamental credit analysis; financial statement analysis; default forecasting; bond valuation; debt valuation; dynamical models; joint default; corporate real options;
    All these keywords.

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:112699. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.