In an era marked by multi-crisis environments, the significance of corporate finance and credit ratings amplifies, especially for German small- and medium-sized family firms, often constrained in accessing capital markets. This thesis investigates how family firms employ impression management...

This research explores the impact of financial indicators on the credit ratings of companies listed on the S&P 500, employing a Sys-GMM model to address endogeneity concerns. Three independent variables categorized as market and survival factors alongside seven control variables sourced from...

There has been heated debate regarding credit-rating agencies' (CRAs') reporting accuracy of corporate credit ratings, which is essential for investors because they rely on those crediting ratings to make investment decisions. We estimate the reporting accuracy using the data on corporate...

Purpose: The purpose of this paper is to shed the light on board evaluation for following analyses that will explore whether and how does credit rating agencies react to board performance evaluation. Design/methodology/approach: To test our research questions, we hand-collect board evaluation...

This paper examines how the corporate life cycle affects credit scoring. Previous empirical studies have shown that the life cycle has an impact on financial policies, creditworthiness, risk and performance. This study utilizes panel data of U.S. listed companies for the period 1985-2017. The...

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...

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...

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,...

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...

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...

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...

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...

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...

This paper assesses whether fintech mortgage lenders align pricing with borrower risk using conforming 30-year mortgages (2012-2020). We estimate default probabilities using machine learning (logit, random forest, gradient boosting, LightGBM, XGBoost), finding that non-fintech lenders achieve...

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,...

Accurate corporate credit ratings are essential for financial risk assessment; yet, traditional methodologies relying on manual evaluation and basic statistical models often fall short in dynamic economic conditions. This study investigated the potential of machine-learning (ML) algorithms as a...