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

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.