The role of social and psychological related soft information in credit analysis: evidence from a Fintech company
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985998%3A_____%2F22%3A00556737" target="_blank" >RIV/67985998:_____/22:00556737 - isvavai.cz</a>
Nalezeny alternativní kódy
RIV/00216208:11230/22:10435284
Výsledek na webu
<a href="https://doi.org/10.1016/j.socec.2021.101806" target="_blank" >https://doi.org/10.1016/j.socec.2021.101806</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.socec.2021.101806" target="_blank" >10.1016/j.socec.2021.101806</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
The role of social and psychological related soft information in credit analysis: evidence from a Fintech company
Popis výsledku v původním jazyce
Improvements in the quality of information in credit appraisal are paramount to the greater efficiency of credit markets. The existing research to assess the role of soft information in credit markets has so far been very limited and inconclusive due to differences in approaches and methodological limitations. The aim of this paper is to discuss the role of social and psychological related soft information in predicting defaults in the P2P lending market and to assess the importance of such information in Fintech credit analysis. Using a unique dataset from the pioneer P2P lending platform RRDai.com and alternative models of testing, we compared the predictive performance of soft information, hard information and combined hard and soft information on defaults. The results show that soft information can provide valuable input into credit appraisals. Soft information shows high predictive power in our test, and combined with hard information, it increases the power of our model to predict defaults.
Název v anglickém jazyce
The role of social and psychological related soft information in credit analysis: evidence from a Fintech company
Popis výsledku anglicky
Improvements in the quality of information in credit appraisal are paramount to the greater efficiency of credit markets. The existing research to assess the role of soft information in credit markets has so far been very limited and inconclusive due to differences in approaches and methodological limitations. The aim of this paper is to discuss the role of social and psychological related soft information in predicting defaults in the P2P lending market and to assess the importance of such information in Fintech credit analysis. Using a unique dataset from the pioneer P2P lending platform RRDai.com and alternative models of testing, we compared the predictive performance of soft information, hard information and combined hard and soft information on defaults. The results show that soft information can provide valuable input into credit appraisals. Soft information shows high predictive power in our test, and combined with hard information, it increases the power of our model to predict defaults.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
50202 - Applied Economics, Econometrics
Návaznosti výsledku
Projekt
<a href="/cs/project/GA18-04630S" target="_blank" >GA18-04630S: Ekonomický dopad investičních sporů</a><br>
Návaznosti
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Ostatní
Rok uplatnění
2022
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Journal of Behavioral and Experimental Economics
ISSN
2214-8043
e-ISSN
2214-8051
Svazek periodika
96
Číslo periodika v rámci svazku
February
Stát vydavatele periodika
US - Spojené státy americké
Počet stran výsledku
19
Strana od-do
101806
Kód UT WoS článku
000731074200003
EID výsledku v databázi Scopus
2-s2.0-85120869761