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The paper examines the impact of credit rating on capital adequacy ratios of Indian state-owned banks using quarterly data for the period 1997:1 to 2002:4. To this end, a multinomial logit model with multi credit rating indicators as dependent variable is estimated. The variables that can impinge upon capital adequacy ratio have been used as explanatory variables. Two separate models — one for long-term credit rating and another for short-term credit rating — have been estimated. The paper concludes that, both for short-term as well as for long-term ratings, capital adequacy ratios are an important factor impinging on credit rating of Indian state-owned banks.

Some qualitative studies have mentioned the impact of trading networks on the credit levels of countries or businesses. However, only a few studies quantified network information on simulated or actual data. This research studied the relationship between network characteristics and sovereign credit levels. Using a data set for 114 countries, we developed and compared the performance of five credit classification models with and without integrating network information.

We used several measures to quantify the trading network information, such as Trading Weight, Closeness, Betweenness, PageRank, and Modularity. The analysis showed that they were closely related to credit levels. In particular, the Trading Weight usually belonged to the top five critical features for almost all classifiers. Overall, we saw that the correct prediction of the network models was consistently higher than those without a network. It meant we could improve the sovereign credit classifications with available trading network information.

In this paper, we present a model to describe the evolution of the yield spread by considering the rating evaluation as the determinant of credit spreads. The underlying rating migration process is assumed to be a non-homogeneous discrete time semi-Markov process. We calculate the total sum of mean basis points paid within any given time interval. From this information we show how it is possible to extract the time evolution of expected interest rates and discount factors.

We examine the impact of Level 3 assets held by nonfinancial companies on credit risk. Specifically, we investigate how the pricing uncertainty of Level 3 assets is reflected in credit ratings, corporate bond yield spreads, and incidences of bond covenants. We find that higher holdings of Level 3 assets are associated with lower credit ratings, higher yield spreads, especially for Level 3 assets sample, and incidences of bondholder-friendly covenants in the bond issues. Our findings are robust to the treatment of sample selection bias and the influence of macroeconomic factors. In addition, our direct test on the relation between the holdings of Level 3 assets and a firm’s distance-to-default shows that higher holdings of Level 3 assets reduce a firm’s distance-to-default. Overall, our findings support the view that Level 3 assets are perceived as increasing credit risk in the bond market.

Credit rating models are widely used by banking institutions to assess the creditworthiness of credit applicants and to estimate the probability of default. Several pattern classification algorithms are used for the development of such models. In contrast to other pattern classification tasks, however, credit rating models are not only expected to provide accurate predictions, but also to make clear economic sense. Within this context, the estimated probability of default is often required to be a monotone function of the independent variables. Most machine learning techniques do not take this requirement into account. In this paper, monotonicity hints are used to address this issue within the modeling framework of support vector machines (SVM), which have become increasingly popular in this field. Non-linear SVM credit rating models are developed with linear programming, taking into account the monotonicity requirement. The obtained results indicate that the introduction of monotonicity hints improves the predictive ability of the models.

This paper investigates the relationship between CEO visibility and corporate risk-taking. The empirical results show that more visible CEOs tend to take more risk. A one-standard-deviation shock in the CEOs media exposure results in a 6.53% rise in total risk. We further investigate the channels of risk-taking activities and find that more visible CEOs seek more R&D investments. The positive effect of CEO visibility on firm risk policies is clearly of concern to bondholders. Consistent with this view, we report that CEO visibility has a significant negative effect on firm credit ratings. Our results highlight the importance of CEO visibility on a crucial corporate outcome — the extent of corporate risk-taking.

The default of corporate bonds can result in large financial losses as well as irreparable harm to investors’ trust and the economy as a whole, which implies that the identification of corporate bond default must be done promptly and properly. Current studies mainly rely on accounting and/or macroeconomic data and use the credit rank (CR) to disclose the credit status of corporate bonds in the default prediction task. However, the textual data of credit rating reports (CRRs) contain richer and more comprehensive information and are neglected in related work. In this paper, we propose a novel framework that draws on the unstructured data in CRR to predict the default of corporate bonds. We extract the rating opinion sentences (categorized as positive and negative) from the collected CRR files and use latent Dirichlet allocation (LDA) models to mine topic information. The bilateral topic information of positive and negative opinions can reflect the anti-risk ability and potential risk of corporate bonds, respectively, based on which the constructed topic features are used for default prediction. Results on real-world Chinese corporate bonds dataset show that the bilateral topic information of CRR can significantly improve the predicting power of models (LR, SVM, KNN and MLP) under three performance metrics (AUC, KS and H-measure). By analyzing the ranking of topic features using SHAP value, the proposed framework can explain the factors that affect bond defaults, which can provide a basis for the decision-making of investment behavior.

Risk and risk management are the most important concerns for insurance companies and financial institutions. In this paper, we will overview some of our research work on this area. In the insurance risk case, we will use ruin probability as a risk measure and discuss the issue of how to estimate it. We will also post some research problems on this subject. For finance risk, we will briefly discuss the risk measures in literature and summarize some of our recent results on coherent risk measures for derivatives. Then we will illustrate how to use some actuarial science techniques to measure financial risk, in particular, credit risk. In this paper, we will focus on the interplay between finance and actuarial science.

Credit rating is the basis for determining the degree of credit risk and credit risk management. Enterprises as the main unit of economic activities, and banks have a close relationship with the credit, bank credit is directly related to the use of bank credit funds and benefits. This requires banks required to enterprise’s business activities, operating results, profitability, solvency and give a scientific evaluation to determine the loss of credit assets does not determine the extent to which the maximum to guard against the risk of loans.