Credit Grade Research Articles

An Application of Multinomial Mis-Classification Cost Matrix For A P- To – P Lending Credit Score

An emerging new form of online credit for lending, different from traditional sources of finance, such as banks and building societies, where lenders provide loans to borrowers directly is termed as P-P. Many of these credits are unsecured personal loans, thus credit score of loans is vital to regulate the default risk and improve profit for lenders and platforms. Standard two-fold classifiers may not be appropriate in this lending since there are multiple credit classes and misclassification costs vary largely across classes in the lending platforms. Cost Sensitive Classifiers have been studied extensively in this set of lending, but none of them have analyzed this issue from the perspective of multinomial classifications and measured the misclassification costs of different credit grades using actual losses and opportunity costs. The research intends to model credit score in p-p lending as a cost-sensitive multinomial classification problem. A misclassification cost matrix is proposed for credit scoring with a set of equations and models to estimate the costs. A replication study using a publicly available data is conducted to evaluate the performance and validate the usefulness of the proposed misclassification cost matrix with the help of an R statistical package developed to aid the application of the model. The outcomes showed that the cost-sensitive multinomial classifiers can significantly decrease the total cost, which is vital for the p-p survival and profitability.

How Does Optimize Peer to Peer Lending Investment

This research uses general data about loans in 5 Credit Grades A, B, C, D and E which can be obtained from the KoinWorks P2PL factsheet platform. The research results show that there are 4 combinations of funding assets in the calculation of the optimal portfolio of the Markowitz Model with the lowest risk preferences consisting of funding assets in Credit Grades A, B, D and E with an expected portfolio return of 24.29% for the year and 2.02. % for monthly and the best risk level in a year of 1.39% for annual and 0.11% for monthly. Meanwhile, in the optimal portfolio planning of the Markowitz model with sharpe ratio, there are 3 combinations of funding assets consisting of Credit Grades A, B and D which obtain an expected portfolio return of 18.29% in the current year and 1.52% in that month. and the level of risk. best in a year of 1.39% for this year and 0.48% for this month, and portfolio performance of 13.1.

Determinants of firms’ default on unsecured loans in the P2P crowdfunding market

We investigate the payback performance of small businesses’ funded loans, and its determinants, in the Peer-to-Peer (P2P) lending crowdfunding market. We do so by analysing a dataset of 1000 small businesses’ unsecured funded loans, via the American online P2P lending funding website Prosper.com. As these loans are unsecured, interest rates are relatively high (18.5% on average in our data base). As a result, payback is by no means straightforward. Indeed, we find 27% of all funded loans in our sample to default. Hence, it is particularly important in this market to know which factors determine payback performance. A novelty of our paper is that we are able to distinguish empirically between new and established firms in this market. Our results highlight that also in P2P lending, borrower reputation — stipulated by credit grade and repayment history — continues to be the single most dominant determinant of payback over loan default, especially for established firms. However, in contrast to conventional financing theory, we found that borrowers’ credit grade is only weakly relevant in determining risks of default of new businesses and that new firms are not more likely to default when compared to established firms. Selection effects may play a role here, in that only high-risk established firms look for unsecured online P2P finance as they are more likely than lower risk established firms to have been unable to get finance in the more traditional markets. This suggests that the liability of newness does not hold in this market. Nevertheless, among new firms, and in contrast to established firms, inclusion of a picture in the loan application was found to be an additional quality signal, positively associated with payback performance. This suggests the importance of visual information as a means of reducing uncertainty and signalling lower risk to financiers.

The framework of Hammer (CAFÉ) credit rating for capital markets in China with international credit rating standards

AbstractThe goal of this paper is to discuss how we establish the so‐called “Hammer (CAFÉ) Credit System” by applying the Gibbs sampling algorithm under the framework of a big data approach supported by both traditional structured and unstructured data as a breakthrough, in particular, to extract those highly related risk features in depicting default (bad) events and related fraudulent behaviors (action) by following the “five step principle” incorporated with the international credit rating standards in the practice. The analysis shows that our Hammer (CAFÉ) Credit System is able to handle current three issues raised by the credit rating business for capital markets in China, which are as follows: (1) The rating is falsely high, (2) the differentiation of credit rating grades is insufficient, and 3) the performance in predicting early warning and related issues is poor. In addition, the Hammer (CAFÉ) credit discussed in this paper is supported by clearly defining the “BBB” grade rate as the basic investment level associated with the annualized rate of default probability, and the credit transition matrix for “AAA‐A” to “CCC‐C” credit grades in accordance with international standards used in the practice of risk management and decision services.

The credit cycle and corporate strategy: challenges and solutions

PurposeThis paper offers a practical overview of the U.S. credit cycle and the challenges it poses, along with a perspective on where we seem to be in the cycle in early 2023. Suggestions are then offered for how corporate executives can address cyclical challenges from a corporate strategy perspective.Design/methodology/approachThe United States credit cycle was out into context by following the trend of Moody’s Baa corporate bond yields from January 1919 to November 2022. Under the Moody’s rating system, Baa is the lowest level of investment grade credit, and as such it possesses speculative characteristics that are sensitive to cyclical dynamics. Another reason for choosing Baa credit patterns for analysis is data availability: over 100-years of continuous Baa data is searchable at the U.S. Federal Reserve.FindingsThe prior credit cycle wave of progressively lower inflation and interest rates began in 1982 and ended in 2020. The current credit cycle of wave of progressively higher inflation and interest rates will present strategic risks and opportunities that executives will increasingly have to deal with.Originality/valueThis is the first corporate strategy paper we are aware that practically addresses the credit cycle change. It is also the first paper we are aware that provides practical suggestions on how to address that change from a corporate strategy perspective.

A study on the evaluation criteria of sports performance in a Chinese university based on Internet technology

The physical education performance of contemporary college students is crucial to every college student's physical health as well as credit grades. And the good or bad sports performance is closely related to the sports evaluation criteria. Relying on Internet technology, this paper takes undergraduates of a domestic university as the research object, adopts a variety of research methods, carries out in-depth research on the evaluation criteria of several items, and develops a more appropriate quantitative evaluation sports scoring standard through analysis and comparison. The experiment shows that the graduation quantitative evaluation sports scoring standards proposed in this paper can meet the graduation assessment needs and better respond to the differences of students' sports quality.

Credit Rating Model of Family Farms and Ranches Based on Dynamic Dichotomous DEA

The key to the sustainable development of family farms and ranches is to solve the problem of financing difficulty, and the construction of the credit rating system of family farms and ranches is the basis of solving the financing difficulty. Therefore, the construction of the credit rating system of family farms and ranches is not only a theoretical problem but also has strong practical significance. On the basis of dichotomous method and improved DEA technology, this paper sets up a credit rating model based on dynamic cycle dichotomous DEA method with the goal of maximizing the efficiency value of credit scoring of family farm and ranch and the constraint of increasing default loss rate. The first feature of this paper is that it constructs a direct mapping from the credit index system to the credit score through the three‐stage DEA model, which avoids the disadvantages of the classical linear weighting formula that relies heavily on the measurement results of different weights, and obtains more accurate and stable credit score results of family farms and pastures. Second, the three‐stage DEA efficiency value is used as the credit score value of the family farm and pasture, and whether it is located in the optimal preset interval is taken as the basis for efficiency dichotomy. The DEA credit score of the family farm and pasture is calculated by dynamic cycle, and the samples belonging to each level of the family farm and pasture are calculated successively according to the order of the credit grade from high to low. Finally, the result of reasonable credit rating of family farm and ranch with high default loss rate and low credit score was obtained. The pyramid test results of matching credit rating and default loss rate show that this research method is effective and has a good pyramid shape.

The Relative Efficiency of Investment Grade Credit and Equity Markets

The Relative Efficiency of Investment Grade Credit and Equity Markets

Identifying the true nature of price discovery and cross-market informational flow in the investment grade CDS and equity markets

We examine the comparative efficiency of systematic investment grade credit default swap (CDS) and equity markets using a time-varying coefficient vector autoregression. This modeling framework enables a view of cross-market informational flow along each point in the time-period under investigation by taking into account parameter instability. We obtain smoothing estimates of parameters capturing such flow between CDS and equity markets using daily data from 2004 to 2019, and measure the strength of flow via relative predictive gains. In contrast to prior studies, we find a two-way interactive effect in which certain types of information are captured more efficiently in prices by each market. We also find that the time-varying coefficient vector autoregression results in superior forecasting gains relative to models not accounting for price discovery. These results have implications for systematic investors, arbitrageurs and stakeholders who monitor systematic markets for their informational content.

The relative efficiency of investment grade credit and equity markets

The relative efficiency of investment grade credit and equity markets

An iterative method for solving a PDE with free boundary arising from pricing corporate bond with credit rating migration

<abstract><p>In this paper an iterative method is proposed to solve a partial differential equation (PDE) with free boundary arising from pricing corporate bond with credit grade migration risk. A iterative algorithm is designed to construct two sequences of fixed internal boundary problems, which produce two weak solution sequences. It is proved that both weak solution sequences are convergent. In each iteration step, an implicit-upwind difference scheme is used to solve the fixed internal boundary problem. It is shown that the scheme is stable and first-order convergent. Numerical experiments verify that the limit of the weak solution sequence is the solution of the free boundary problem. This method simplifies the free boundary problem solving, ensures the stability of the discrete scheme and reduces the amount of calculation.</p></abstract>

Credit Rating Model of Small Enterprises Based on Optimal Discriminant Ability and Its Empirical Study

As an important part of the national economy, small enterprises are now facing the problem of financing difficulties, so a scientific and reasonable credit rating method for small enterprises is very important. This paper proposes a credit rating model of small enterprises based on optimal discriminant ability; the credit score gap of small enterprises within the same credit rating is the smallest, and the credit score gap of small enterprises between different credit ratings is the largest, which is the dividing principle of credit rating of small enterprises based on the optimal discriminant ability. Based on this principle, a nonlinear optimization model for credit rating division of small enterprises is built, and the approximate solution of the model is solved by a recursive algorithm with strong reproducibility and clear structure. The small enterprise credit rating division not only satisfies the principle that the higher the credit grade, the lower the default loss rate, but also satisfies the principle that the credit group of small enterprises matches the credit grade, with credit data of 3111 small enterprises from a commercial bank for empirical analysis. The innovation of this study is the maximum ratio of the sum of the dispersions of credit scores between different credit ratings and the sum of the dispersions of credit scores within the same credit rating as the objective function, as well as the default loss rate of the next credit grade strictly larger than the default loss rate of the previous credit grade as the inequality constraint; a nonlinear credit rating optimal partition model is constructed. It ensures that the small enterprises with small credit score gap are of the same credit grade, while the small enterprises with large credit score gap are of different credit grades, overcoming the disadvantages of the existing research that only considers the small enterprises with large credit score gap and ignores the small enterprises with small credit score gap. The empirical results show that the credit rating of small enterprises in this study not only matches the reasonable default loss rate but also matches the credit status of small enterprises. The test and comparative analysis with the existing research based on customer number distribution, K-means clustering, and default pyramid division show that the credit rating model in this study is reasonable and the distribution of credit score interval is more stable.

Vertical integration, peaking plant commitments and the role of credit quality in energy-only markets

Resource adequacy in energy-only markets is of continual interest to policymakers and in Australia has historically been navigated via energy retailer investment commitments in peaking plant capacity. This in turn has been driven by the National Electricity Market's very high Market Price Cap (AUD $15,000/MWh). The market is rapidly transitioning with rising levels of renewables and coal plant closures. Ironically however, investment commitments in peaking plant capacity by major energy retailers has stalled. This raises the question as to whether the model of vertical integration, a pattern which dominated energy-only markets, has somehow broken down. In this article, peaking plant dynamics are tested using historic data – first as a stand-alone generator, then as part of a merged vertical entity over 16 years of trade. Results reveal the canonical merchant peaking plant remains too risky as a stand-alone project financing, but vertical integration and energy retailer incentives to commit to on-balance sheet peaking plant remains strong, with transaction cost synergies of 13% and investment grade credit quality being contingent on integration. Any lack of commitment must be explained by other variables.

Fintech platforms: Lax or careful borrowers\u2019 screening?

Can peer-to-peer lending platforms mitigate fraudulent behaviors? Or have lending players been acting similar to free-riders? This paper constructs a new proxy to investigate lending platform misconduct and compares the FICO score and the LendingClub credit grade. To examine whether the lack of verification by the Fintech platform affects lenders’ collection performance, I explore the recovery rate (RR) of non-performing loans through a mixed-continuous model. The regression results show that the degree of prudence taken by the lending platform in the pre-screening activity negatively affects the detection of some misreporting borrowers. I also find that the Fintech platform’s missing verification information (e.g., annual income and employment length) affects the RR of non-performing loans, thereby hampering lenders’ collection performance.

A Multi-platform Study of Crowd Signals Associated with Successful Online Fundraising

The growing popularity of online fundraising (aka "crowdfunding") has attracted significant research on the subject. In contrast to previous studies that attempt to predict the success of crowdfunded projects based on specific characteristics of the projects and their creators, we present a more general approach that focuses on crowd dynamics and is robust to the particularities of different crowdfunding platforms. We rely on a multi-method analysis to investigate the correlates, predictive importance, and quasi-causal effects of features that describe crowd dynamics in determining the success of crowdfunded projects. By applying a multi-method analysis to a study of fundraising in three different online markets, we uncover universal crowd dynamics that ultimately decide which projects will succeed. In all analyses and across three markets, we consistently find that funders' behavioural signals (1) are significantly correlated with fundraising success; (2) approximate fundraising outcomes better than the characteristics of projects and their creators such as credit grade, company valuation, and subject domain; and (3) have significant quasi-causal effects on fundraising outcomes while controlling for potentially confounding project variables. By showing that universal features deduced from crowd behaviour are predictive of fundraising success on different crowdfunding platforms, our work provides design-relevant insights about novel types of collective decision-making online. This research inspires thus potential ways to leverage cues from the crowd and catalyses research into crowd-aware system design.

Vulnerabilities and resilience in insurance investing: studying the COVID-19 pandemic.

The COVID-19 crisis has major impacted the insurance industry in three dimensions: business operations, underwriting and claims and insurance investing. This paper will analyse the implications for insurance investing. We start by showing the impact of the severe drawdown in the equity markets during the initial phase of the crisis in March/April 2020 on a typical insurer’s balance sheet. We then look at the effects of the dislocations in fixed income, which make up the largest share of exposures in insurance companies’ portfolios. We track the performance of investment grade credit during the year while paying special attention to the impact of downgrades on insurers’ solvency capital. We finally study alternative investments with particular focus on private markets. These investments are a faster-growing part of insurance companies’ exposures and pose specific challenges as they are complex, more difficult to access, have limited liquidity and are often harder to price, especially during times of high market volatility. Yet, compared to their equivalent public market exposures, private investments provide additional income, which allows insurers to charge lower rates on their products to policyholders. As the sophistication and complexity of investments keep growing, companies as well as regulators need to find a good balance between policyholder protection and market efficiency.

Covid-19 and College Academic Performance: A Longitudinal Analysis

Using an unbalanced panel of close to 12,000 academic records, and difference-in-differences models and event study analyses with individual fixed effects, we evaluate the impact of the COVID-19 pandemic on lower-income students' academic performance during the spring 2020 semester relative to their higher-income peers. We find a differential effect by students' pre-COVID-19 academic performance. Top-performing lower-income students experienced a decrease in both grades (5% lower) and earned credits (11% fewer) during the spring 2020 semester relative to their higher-income peers. In contrast, lower-income students in the bottom quartile of the fall 2019 cumulative GPA distribution outperformed their higher-income peers with a 9% higher spring 2020 GPA. After ruling out alternative mechanisms, we find suggestive evidence from survey data that top-performing lower-income students' lower relative performance may be driven by greater challenges with online learning and a disproportionate intake of incomplete courses relative to their higher-income peers. Among bottom-performing lower-income students, greater concerns with maintaining financial aid than their higher income peers may have led them to a higher use of the credit/no credit grade option instead of a letter grade.

The Bloomberg Corporate Default Risk Model (DRSK) for Public Firms

The DRSK public model estimates forward-looking real-world default probabilities for publicly traded firms. The model also assigns credit grades based on the estimated default probabilities. The product covers firms in all regions and sectors of operation for which the necessary data is available. The DRSK public model was last updated in 2015. This year we are releasing an updated model which improves on the previous model's performance in a variety of ways. The new model's accuracy ratio is above 92%, adjusted pseudo R-squareds have improved, and performance is more in line with observed historical default rates. We describe the new model, analyze its performance in various ways and compare it to the previous model.

A dynamic Bayesian approach for probability of default and stress test

Obligor defaults are cross-sectionally correlated as obligors share common economic conditions; in addition obligors are longitudinally correlated so that an economic shock like the IMF crisis in 1998 lasts for a period of time. A longitudinal correlation should be used to construct statistical scenarios of stress test with which we replace a type of artificial scenario that the banks have used. We propose a Bayesian model to accommodate such correlation structures. Using 402 obligors to a domestic bank in Korea, our model with a dynamic correlation is compared to a Bayesian model with a stationary longitudinal correlation and the classical logistic regression model. Our model generates statistical financial statement under a stress situation on individual obligor basis so that the genearted financial statement produces a similar distribution of credit grades to when the IMF crisis occurred and complies with Basel IV (Basel Committee on Banking Supervision, 2017) requirement that the credit grades under a stress situation are not sensitive to the business cycle.

Risk evaluation for C2C E‐commerce via an improved credit counting method

E‐commerce has become a popular way for shopping, especially during the Covid‐19 pandemic. The key of the E‐commerce is the credit evaluation, for instance the client to client (C2C) mode E‐commerce. In this paper, an improved credit scoring method is proposed. The improved credit scoring method uses the interval distribution of commodity prices to add points for successful transactions and deduct points for failed transactions by using credit grade deduction coefficients and credit level, which can solve credit hype and periodic deception. Through the analysis of credit risk, a risk calculation method for online transactions is designed by using the improved credit scoring model. The proposed risk calculation method can evaluate the risk of current transactions based on historical transaction conditions and current transaction prices. The simulated experiments show that the improved credit scoring method has better anti‐credit speculation and periodic deception effects, and the risk assessment method is more accurate and effective for risk analysis and prediction in C2C E‐commerce transactions.