α-threshold networks in credit risk models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14560%2F25%3A00144361" target="_blank" >RIV/00216224:14560/25:00144361 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.tandfonline.com/doi/full/10.1080/14697688.2025.2465697" target="_blank" >https://www.tandfonline.com/doi/full/10.1080/14697688.2025.2465697</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/14697688.2025.2465697" target="_blank" >10.1080/14697688.2025.2465697</a>

Jazyk výsledku

angličtina

Název v původním jazyce

α-threshold networks in credit risk models

Popis výsledku v původním jazyce

Peer-to-peer (P2P) lending markets offer risky investment opportunities, for which accurate credit risk models are in high demand. Loan books offer a broad spectrum of loan and borrower characteristics, making it challenging to construct high-dimensional systems that make the use of traditional credit scoring models. In this study, we propose two network-based feature extraction methods that extract complex relationships between risky assets, namely, loans, which are represented as vertices, and weighted edges, which correspond to the feature-based similarity between loans. Our two methods differ with respect to how similar loans are identified. The traditional approach uses partitioning based on the medoid algorithm to identify similar loans (the k-PAM model). A much faster alternative is to eliminate 100[%](1-α) of the largest distances (the α-threshold model). The resulting network structure is used to extract features that augment profit scoring models. Utilizing P2P loan data, we find that forecasting models that use network-based features consistently outperform the benchmarks in a statistical sense and lead to higher returns and risk-adjusted returns.

Název v anglickém jazyce

α-threshold networks in credit risk models

Popis výsledku anglicky

Peer-to-peer (P2P) lending markets offer risky investment opportunities, for which accurate credit risk models are in high demand. Loan books offer a broad spectrum of loan and borrower characteristics, making it challenging to construct high-dimensional systems that make the use of traditional credit scoring models. In this study, we propose two network-based feature extraction methods that extract complex relationships between risky assets, namely, loans, which are represented as vertices, and weighted edges, which correspond to the feature-based similarity between loans. Our two methods differ with respect to how similar loans are identified. The traditional approach uses partitioning based on the medoid algorithm to identify similar loans (the k-PAM model). A much faster alternative is to eliminate 100[%](1-α) of the largest distances (the α-threshold model). The resulting network structure is used to extract features that augment profit scoring models. Utilizing P2P loan data, we find that forecasting models that use network-based features consistently outperform the benchmarks in a statistical sense and lead to higher returns and risk-adjusted returns.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50206 - Finance

Projekt

<a href="/cs/project/GF22-35130K" target="_blank" >GF22-35130K: Modely úvěrového rizika na P2P trzích využívající teorie grafů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Quantitative Finance

ISSN

1469-7688

e-ISSN

1469-7696

Svazek periodika

25

Číslo periodika v rámci svazku

11

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

23

Strana od-do

1789-1811

Kód UT WoS článku

001435649900001

EID výsledku v databázi Scopus

2-s2.0-86000203939