Analysis of factors influencing the bailout of distressed enterprises by local asset management companies - LI, Houwen (Institutional Knowledge at Singapore Management University, 2021)
In recent years, the number of distressed enterprises in China has gradually increased due to radical enterprise strategies, excessive expansion, poor management, and a trend of accelerating growth. Unable to adapt to fierce market competition, a large number of “zombie enterprises” disrupt the market order, reduce the use efficiency of social resources, and aggravate market risks. However, some enterprises aretemporarily caught in financial distress due to a deterioration of their financial situation caused by factors such as debt structure or capital liquidity. If such enterprises can be bailed out in time, they can escape their distress, which not only protects the interests of creditors and the returns for investors, but also improves the efficiency of resource allocation in society as a whole. The key to distinguishing between “zombie enterprises” and enterprises with bailout value is to identify the factors that influence the bailout value of distressed enterprises.After reviewing literature, I find that the existing research focuses mostly on the reasons for the bailout of distressed enterprises and the significance of reorganization and less on how local asset management companies (also known as “local AMCs”) evaluate distressed enterprises. In this thesis, I first analyze the methodology of GOHO Assets Management Co., Ltd. (hereinafter “GOHO Assets”) in the bailout of distressed enterprises, and examine the analytical approach of GOHO Assets to establish a decision-making model for the bailout of distressed enterprises. The model contains control factors, including local government support, the judicial environment, debt status, employee situation, equity structure, and fundamental factors, including macroeconomic industry outlook and other macro-fundamental factors, as well as firm-fundamental factors such as gross margin, total asset turnover ratio, interest ratio,financing rate, equity concentration, corporate structure. Subsequently, I use the decision model to analyze three successful bailout cases of distressed enterprise and two cases without bailout value, and the results show that the decision model has good practical applicability.In the subsequence large sample empirical analysis, I select listed companies that were subject to a risk warning (ST) or delisting warning (*ST) in the Shanghai and Shenzhen Stock Exchanges during the four years from January 2014 to the end of January 2018. Using logit model and probit model, I use the gross profit rate, total asset turnover ratio, leverage ratio, financing rate, equity concentration, and enterprisenature of the listed companies to predict whether the companies could successfully remove their warnings status.The empirical results show that the model yields better predictions in both time fixed effect and industry fixed effect specifications. Two indicators — gross margin and enterprise nature —are significant, and the other indicators are not significant in predicting whether the companies could successfully remove their warnings status.Thus, the key factors proposed in my decision model—gross margin and enterprise nature—should be used as key factors in determining whether a distressed enterprise has bailout value. I also conduct a detailed analysis of the motivations and specific behavioral patterns of local government intervention in distressed enterprises, and find that local governments play an important role in the bailout outcomes of both private and state-owned listed enterprises.Analysis of the indicators that are not significant reveals that their insignificance is mainly due to the fact that distressed enterprises often do not truly disclose their assets, liabilities, or income, leading to distorted indicators. Therefore, when conducting due diligence on the distressed enterprises, it is necessary to makeadjustments to important indicators and use the adjusted indicators as the basis for bail-out decision-making.