In dynamic models of asset markets with asymmetric information and endogenous screening, the anticipation of signaling through delayed sales incentivizes originators to exert greater effort ex ante. A central prediction in those models is a positive relationship between screening effort and the...

In the peer-to-peer (P2P) lending market, current studies focus on two categories of approaches to evaluate the loans, thus providing investment suggestions to the investors: credit scoring (i.e., predicting the credit risk) and profit scoring (i.e., predicting the profitability). However,...

This study addresses the quantification of credit risk in solidarity economy entities, proposing a new methodology to redefine the concept of a "default" in the frequent situations of extreme class imbalances. The objective is to develop and evaluate credit scoring models that enhance risk...

We examine whether loan portfolio sectoral specialization provides informational advantages to banks, enabling better credit risk assessment. Using euro area credit register data, we compare probabilities of default assigned by specialized and non-specialized banks to the same borrowing firm...

Do sovereign credit ratings take into account physical and transition climate risks? This paper empirically addresses this question using a panel dataset that includes a large sample of countries over two decades. The analysis reveals that higher temperature anomalies and more frequent natural...

Prior research has investigated how narcissistic executives affect firm policies and outcomes and how these executives influence colleagues and followers. However, almost no research exists concerning the impact of narcissistic executives on external agents. We examine the case of credit...

Most of indexes regarding Credit Rating of the national debt bonds are associated to Gross National Product, which involves the well-known Keynesian Multiplicator of the IS-LM Equilibrium. Specifically, a common way of Sovereign Debt evaluation is its percentage of the Gross National Product in...

In emerging markets like Vietnam, where student borrowers often lack traditional credit histories, accurately predicting loan eligibility remains a critical yet underexplored challenge. While machine learning and deep learning techniques have shown promise in credit scoring, their comparative...

Background: Corporate bonds are crucial for corporations as they provide a flexible and often less costly alternative to equity financing. However, rising corporate debt levels, along with rating downgrades and economic uncertainty, can cause corporations to face financial distress, exacerbating...

Background: Financial institutions face significant challenges in predicting loan defaults, which directly impact the non-performing loan (NPL) rate. Incorrect predictions can lead to misinformed decisions and substantial financial losses. Objectives: This study aims to enhance default...

The fundamental issue with a credit system for manufacturers and importers of commodities is inefficient credit assessment. Traditional techniques frequently produce inaccurate risk assessments and credit scores, resulting in financial losses for lenders, missing business growth possibilities,...

We investigate non-financial variables for predicting bankruptcy in small and medium-sized enterprises (SMEs). The variables encompass management, board and ownership structures and are sourced from universally accessible information, rendering them available to all stakeholders and allowing for...

Purpose - We examine whether the social capital of the area where a firm's headquarters is located affects that firm's credit rating. Given that credit rating agencies only infrequently visit a firm's headquarters, it is pertinent to investigate whether this soft information is considered....

This staff memo documents a detailed analysis of the payment remark database to better understand whether the recent increase in payment remarks signals an impending wave of bankruptcies. We find that the number of high-bankruptcy-risk firms is growing in proportion to the total number of firms...

This research article presents a comparative analysis between logistic regression as a traditional method, artificial neural networks (ANNs), and decision tree as machine learning techniques for predicting credit risk. It meticulously examines and evaluates these three methods, elucidating their...

We exploit a nation-wide introduction of mandatory disclosure of borrowers' total credit exposures and show that sharing such information increases credit access independent of borrowers' history. Differentiating between borrowers applying to competitor banks and those reapplying to their...

Purpose: This study tries to identify the impact of the COVID-19 pandemic on the similarity of banks' loan portfolios. Design/methodology/approach: Using the entire firm-level bank loan data from 2009 to 2020 in the Korean market, this study analyzes the cosine similarity in terms of credit...

Credit risk is a crucial component of daily financial services operations; it measures the likelihood that a borrower will default on a loan, incurring an economic loss. By analysing historical data for assessment of the creditworthiness of a borrower, lenders can reduce credit risk. Data are...

The increasing population and emerging business opportunities have led to a rise in consumer spending. Consequently, global credit card companies, including banks and financial institutions, face the challenge of managing the associated credit risks. It is crucial for these institutions to...

We study how financing constraints affect the cash holdings of small and mediumsized enterprises. There has been little empirical work on this topic, even though these firms often face financial constraints. We contribute by using detailed data on credit ratings in Sweden as a measure of...