scholarly journals Evaluating borrowers’ default risk with a spatial probit model reflecting the distance in their relational network

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261737
Author(s):  
Jong Wook Lee ◽  
So Young Sohn
Keyword(s):  
Spatial Autocorrelation ◽  
Default Risk ◽  
Predictive Power ◽  
Credit Scoring ◽  
Probit Model ◽  
Peer To Peer ◽  
Empirical Results ◽  
Default Prediction ◽  
Spatial Probit ◽  
Relational Network

Potential relationship among loan applicants can provide valuable information for evaluating default risk. However, most of the existing credit scoring models either ignore this relationship or consider a simple connection information. This study assesses the applicants’ relation in terms of their distance estimated based on their characteristics. This information is then utilized in a proposed spatial probit model to reflect the different degree of borrowers’ relation on the default prediction of loan applicant. We apply this method to peer-to-peer Lending Club Loan data. Empirical results show that the consideration of information on the spatial autocorrelation among loan applicants can provide high predictive power for defaults.

scholarly journals Comparing Performance of Machine Learning Algorithms for Default Risk Prediction in Peer to Peer Lending

TEM Journal ◽  
2021 ◽  
pp. 133-143
Author(s):  
Yanka Aleksandrova
Keyword(s):  
Machine Learning ◽  
Default Risk ◽  
Credit Scoring ◽  
Learning Algorithms ◽  
Peer To Peer ◽  
Machine Learning Algorithms ◽  
Ensemble Classifiers ◽  
Peer Lending ◽  
Official Site ◽  
Heterogeneous Ensemble

The purpose of this research is to evaluate several popular machine learning algorithms for credit scoring for peer to peer lending. The dataset to fit the models is extracted from the official site of Lending Club. Several models have been implemented, including single classifiers (logistic regression, decision tree, multilayer perceptron), homogeneous ensembles (XGBoost, GBM, Random Forest) and heterogeneous ensemble classifiers like Stacked Ensembles. Results show that ensemble classifiers outperform single ones with Stacked Ensemble and XGBoost being the leaders.

scholarly journals Gender effect on the default risk in peer-to-peer lending markets: The case of the largest Chinese platform

2019 ◽  
Vol 9 (3) ◽  
pp. 8-22
Author(s):  
Lin Lingnan
Keyword(s):  
Credit Risk ◽  
Gender Discrimination ◽  
Default Risk ◽  
Probit Model ◽  
Peer To Peer ◽  
Gender Effect ◽  
Individual Characteristics ◽  
Model Estimate ◽  
P2p Lending ◽  
Peer Lending

Research of gender effect on funding success in peer-to-peer lending markets demonstrates that gender discrimination is a platform-specific phenomenon rather than a common feature. Can we get a similar conclusion about the relationship between gender and credit risk? How do gender differences affect default risk? We try to answer this question using the data of the largest peer-to-peer lending platform RenRenDai spanning from March 2016 to September 2016. In order to avoid the endogeneity problem, this paper first uses the instrumental variable method to conduct a baseline Probit model estimate connecting gender difference to the default rate with several borrowers’ individual characteristics under control. Then the original Probit model and a propensity score matching method aiming to eliminate the effects of divergent observable characteristics are applied to test the robustness of the outcome. Both the baseline estimation and the robustness test show that there is no significant gender effect on the probability of default, ceteris paribus. Therefore, borrowers’ gender is not a good screening device for the P2P lending platform to control the credit risk; other factors should be taken into account to reduce the non-performing loan rate. However, since this paper only investigates the situation of RenRenDai and the data we use is limited, we should be very careful to generalize our findings to other P2P lending platforms. More research on different P2P lending platforms in different regulatory regimes is in necessity

A deep metric learning approach for weakly supervised loan default prediction1

2021 ◽  
pp. 1-13
Author(s):  
Kai Zhuang ◽  
Sen Wu ◽  
Xiaonan Gao
Keyword(s):  
Default Risk ◽  
Metric Learning ◽  
Real Life ◽  
Risk Scores ◽  
Loan Default ◽  
Default Prediction ◽  
Heterogeneous Features ◽  
Deep Metric Learning ◽  
Weakly Supervised ◽  
Decision Boundaries

To deal with the systematic risk of financial institutions and the rapid increasing of loan applications, it is becoming extremely important to automatically predict the default probability of a loan. However, this task is non-trivial due to the insufficient default samples, hard decision boundaries and numerous heterogeneous features. To the best of our knowledge, existing related researches fail in handling these three difficulties simultaneously. In this paper, we propose a weakly supervised loan default prediction model WEAKLOAN that systematically solves all these challenges based on deep metric learning. WEAKLOAN is composed of three key modules which are used for encoding loan features, learning evaluation metrics and calculating default risk scores. By doing so, WEAKLOAN can not only extract the features of a loan itself, but also model the hidden relationships in loan pairs. Extensive experiments on real-life datasets show that WEAKLOAN significantly outperforms all compared baselines even though the default loans for training are limited.

scholarly journals Measuring and Managing Credit Risk for Chinese Microfinance Institutions

2018 ◽  
Vol 10 (7) ◽  
pp. 56
Author(s):  
Jie Li ◽  
Zhenyu Sheng
Keyword(s):  
Credit Risk ◽  
Predictive Power ◽  
Direct Method ◽  
Credit Scoring ◽  
Microfinance Institutions ◽  
Linear Discriminant ◽  
Management Level ◽  
Scoring Model ◽  
Subjective Appraisals ◽  
Credit Scoring Model

Chinese microfinance institutions need to measure and manage credit risk in a quantitative way in order to improve competitiveness. To establish a credit scoring model (CSM) with sound predictive power, they should examine various models carefully, identify variables, assign values to variables and reduce variable dimensions in an appropriate way. Microfinance institutions could employ both CSM and loan officer’s subjective appraisals to improve risk management level gradually. The paper sets up a CSM based on the data of a microfinance company running from October 2009 to June 2014 in Jiangsu province. As for establishing the model, the paper uses Linear Discriminant Analysis (LDA) method, selects 16 initial variables, employs direct method to assign variables and adopts all the variables into the model. Ten samples are constructed by randomly selecting records. Based on the samples, the coefficients are determined and the final none-standardized discriminant function is established. It is found that Bank credit, Education, Old client and Rate variables have the greatest impact on the discriminant effect. Compared with the same international models, this model’s classification effect is fine. The paper displays the key technical points to build a credit scoring model based on a practical application, which provides help and references for Chinese microfinance institutions to measure and manage credit risk quantitatively.

scholarly journals Forecasting Large Price Declines of the Nikkei Using the S&P 500 Implied Volatility

2016 ◽  
Vol 8 (1) ◽  
pp. 58
Author(s):  
Chikashi Tsuji
Keyword(s):  
Quantile Regression ◽  
Regression Models ◽  
Implied Volatility ◽  
Predictive Power ◽  
Downside Risk ◽  
Price Changes ◽  
Empirical Results ◽  
Left Tail

This paper empirically examines the forecast power of the previous day’s US implied volatility for large declines of the Nikkei by using several versions of quantile regression models. All our empirical results suggest that the previous day’s US S&P 500 implied volatility has forecast power for large price drops of the Nikkei 225 in Japan. Since we repeatedly and carefully tested the several left tail risks in price changes of the Nikkei and we also tested by using some different versions of quantile regression models, our evidence of the predictive power of the S&P 500 implied volatility for downside risk of the Nikkei is very robust.

scholarly journals Does Banking Management Affect Credit Risk? Evidence from the Indian Banking System

2018 ◽  
Vol 6 (3) ◽  
pp. 67 ◽  
Author(s):  
Laxmi Koju ◽  
Ram Koju ◽  
Shouyang Wang
Keyword(s):  
Credit Risk ◽  
Default Risk ◽  
Banking System ◽  
Capital Requirements ◽  
Operating Efficiency ◽  
Investment Strategies ◽  
Empirical Results ◽  
Bank Managers ◽  
Credit Standards ◽  
The Impact

This study investigated the impact of banking management on credit risk using a sample of Indian commercial banks. The study employed dynamic panel estimations to evaluate the link between banking management variables and credit risk. The empirical results show that an increase in loan portion over total assets does not necessarily increase problem loans. The findings suggest that high capital requirements and large bank size do not reduce default risk, whereas high profitability and strong income diversification policies lower the likelihood of default risk. The overall empirical results supported the “operating efficiency”, “diversification” and “too big to fail” hypotheses, confirming that credit quality in the banking industry is mainly driven by profitability, banking supervision, high credit standards and strong investment strategies. The findings are relevant to bank managers, investors and bank regulators, in formulating effective credit policies and investment strategies.

scholarly journals Forecasting Macedonian Business Cycle Turning Points Using Qual Var Model

2016 ◽  
Vol 5 (3) ◽  
pp. 61-78
Author(s):  
Magdalena Petrovska ◽  
Aneta Krstevska ◽  
Nikola Naumovski
Keyword(s):  
Business Cycle ◽  
Predictive Power ◽  
Turning Points ◽  
Probit Model ◽  
Economic Conditions ◽  
Var Model ◽  
Leading Indicators ◽  
Out Of Sample ◽  
The Business Cycle

Abstract This paper aims at assessing the usefulness of leading indicators in business cycle research and forecast. Initially we test the predictive power of the economic sentiment indicator (ESI) within a static probit model as a leading indicator, commonly perceived to be able to provide a reliable summary of the current economic conditions. We further proceed analyzing how well an extended set of indicators performs in forecasting turning points of the Macedonian business cycle by employing the Qual VAR approach of Dueker (2005). In continuation, we evaluate the quality of the selected indicators in pseudo-out-of-sample context. The results show that the use of survey-based indicators as a complement to macroeconomic data work satisfactory well in capturing the business cycle developments in Macedonia.

Secured Lending and Default Risk: Equilibrium Analysis, Policy Implications and Empirical Results

1991 ◽  
Vol 101 (406) ◽  
pp. 458 ◽  
Author(s):  
Arnoud W. A. Boot ◽  
Anjan V. Thakor ◽  
Gregory F. Udell
Keyword(s):  
Default Risk ◽  
Policy Implications ◽  
Equilibrium Analysis ◽  
Empirical Results
Sign in / Sign up

Export Citation Format

Share Document