scholarly journals Evaluating Default Risk and Loan Performance in UK Peer-to-Peer Lending: Evidence from Funding Circle

2021 ◽  
Vol 25 (5) ◽  
pp. 530-538
Author(s):  
Boyu Xu ◽  
◽  
Zhifang Su ◽  
Jan Celler
Keyword(s):  
Interest Rate ◽  
Default Risk ◽  
Binary Logistic Regression ◽  
The United States ◽  
Peer To Peer ◽  
Loan Default ◽  
P2p Lending ◽  
Loan Defaults ◽  
The Interest Rate ◽  
The Uk

The United Kingdom is the third-largest peer-to-peer (P2P) lending market in the world, which is surpassed only by the two dominant forces in P2P investing, China and the United States of America. As an innovative financial market in the UK, P2P lending brings not only many opportunities but also many risks, especially the loan default risk. In this context, this paper uses binary logistic regression and survival analysis to evaluate default risk and loan performance in UK P2P lending. The empirical results indicate that credit group, loan purpose for capital needs, sector type, loan amount, interest rate, loan term, and the age of the company all have a significant impact on the probability of loan default. Among them, the interest rate, loan term, and loan purpose for capital needs are the three most important determinants of the probability of loan defaults and survival time of loans.

An Evolutionary Behavior Forecasting Model for Online Lenders and Borrowers in Peer-to-Peer Lending

2017 ◽  
Vol 34 (01) ◽  
pp. 1740008 ◽  
Author(s):  
Wei Liu ◽  
Li-Qiu Xia
Keyword(s):  
Interest Rate ◽  
Evolutionary Game ◽  
Mutual Effect ◽  
Peer To Peer ◽  
Forecasting Model ◽  
Private Lending ◽  
P2p Lending ◽  
Numeric Experiment ◽  
The Interest Rate ◽  
Borrowing Cost

Online peer-to-peer (P2P) lending is an emerging financial mode that combines the Internet with private lending to provide unsecured lending among individuals. The interest rate and risk depend on online lenders and borrowers’ behavior choices and game in the context of P2P lending. In this paper, we propose an evolutionary behavior forecasting model for online participants based on the risk preference behavior of lenders and the credit choice of borrowers. We highlight four evolutionary equilibrium states of online lenders and borrowers’ behavior and their effects on the risk of online P2P lending platforms. We run a numeric experiment using the Paipaidai platform in China as a case and find that the evolutionary behavior of online lenders and borrowers is determined by the mutual effect of the interest rate, information gathering cost, borrowing cost, and yield rate. This paper uses evolutionary game methodology to analyze online P2P lending behavior in China and explores P2P fund success from the dual perspective of lenders and borrowers.

The Mechanism of Shifting From Direct Monetary Policies to Indirect Monetary Policies

2021 ◽  
Vol 39 (11) ◽  
Author(s):  
Saad Khalaf ◽  
Abdul Rahman Abdul Ridha ◽  
Hussein Habeeb
Keyword(s):  
Interest Rate ◽  
Main Tool ◽  
The United States ◽  
The European Union ◽  
Monetary Policies ◽  
Policy Makers ◽  
The United Kingdom ◽  
The World ◽  
New Policies ◽  
The Interest Rate

After 2008, a new term appeared on monetary policies after the direct monetary policies failed to reach a solution to the economic deficit that occurred in the economies of many countries, especially after the mortgage crisis that plagued the financial markets in most countries of the world, as these countries tried to reduce the interest rate to Zero or close to it in order to move the economy, but it did not respond despite the fact that the interest rate is the main tool and is considered the control stick in direct monetary policies.  Thus, it became imperative for those countries to use new tools in order to get out of that crisis. Japan is considered the first to use these new policies and solutions before that period, and he is the first to call them indirect monetary policies. These tools were called by many names, including quantitative easing, credit facilitation and others. Many names, but it was the best solution by monetary policy makers for many countries, including the United States of America, the United Kingdom of Britain and the European Union, which represent the most powerful economies in the world,

scholarly journals Monetary Policy Effects on Energy Sector Bubbles

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 472
Author(s):  
Petre Caraiani ◽  
Adrian Călin
Keyword(s):  
Monetary Policy ◽  
Interest Rate ◽  
Asset Prices ◽  
The United States ◽  
Energy Sector ◽  
Monetary Policy Shocks ◽  
Policy Shocks ◽  
The Difference ◽  
The Interest Rate ◽  
The Impact

We investigate the effects of monetary policy shocks, including unconventional policy measures, on the bubbles of the energy sector, for the case of the United States. We estimate a time-varying Bayesian VAR model that allows for quantifying the impact of monetary policy shocks on asset prices and bubbles. The energy sector is measured through the S&P Energy Index, while bubbles are measured through the difference between asset prices and the corresponding dividends for the energy sector. We find significant differences in the impact of monetary policy shocks for the aggregate economy and for the energy sector. The findings seem sensitive to the interest rate use, i.e., whether one uses the shadow interest rate or the long-term interest rate.

The Inflation Forecast

2001 ◽  
Vol 175 ◽  
pp. 59-66 ◽  
Author(s):  
C. A. E. Goodhart
Keyword(s):  
Monetary Policy ◽  
Interest Rate ◽  
Time Lags ◽  
Variable Time ◽  
Monetary Policy Committee ◽  
Alternative Procedures ◽  
The Interest Rate ◽  
The Uk ◽  
Policy Committee

Given the long and variable time lags between interest rate changes and responses in output and inflation, an inflation forecast must lie at the heart of monetary policy. In the UK the Bank's inflation forecast and Report were developed when the interest rate decision still lay with the Chancellor. Its, largely unchanged, continuation has led to certain tensions once that decision was delegated to a Monetary Policy Committee of independently responsible experts. In this paper the question is raised whether such a Committee should be jointly and individually responsible for the inflation forecast, and what might be considered as alternative procedures.

scholarly journals Predicting Loan Defaults Using Logistic Regression

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Selena Zhao ◽  
Jiying Zou
Keyword(s):  
Logistic Regression ◽  
Exploratory Data Analysis ◽  
Predictor Variables ◽  
Loan Default ◽  
Stratified Sample ◽  
Anonymized Data ◽  
Exploratory Data ◽  
Loan Defaults ◽  
The Interest Rate ◽  
Made In

We used anonymized data from a loan company to analyze correlations between loan defaults and other characteristics of loans or borrowers of loans. We performed an exploratory data analysis of the different factors and how they correlated with loan defaults. Using observations made in the EDA, we proceeded to use logistic regression to predict the odds of loan defaults with several loan characteristics as predictor variables. Different models were evaluated and cross-validated using AIC, AUC, and predicted accuracy. Weighted accuracy was also measured because the loan dataset was a stratified sample. We concluded that the interest rate most accurately predicted the odds of a loan default and that the most useful model was both simplistic and accurate. Research was limited by the variables that were not analyzed during EDA, the limited variables the loan dataset contained, and the modeling technique used.

The interest-rate management and sterilization hypotheses for the UK: under fixed exchange rates

1991 ◽  
Vol 23 (1) ◽  
pp. 821-838
Author(s):  
Daniel Wai-Wah Cheung ◽  
Subhash Sharma ◽  
Paul Trescott
Keyword(s):  
Interest Rate ◽  
Exchange Rates ◽  
Fixed Exchange Rates ◽  
The Interest Rate ◽  
The Uk

The interest-rate management and sterilization hypotheses for the UK: under fixed exchange rates

1991 ◽  
Vol 23 (4) ◽  
pp. 821-838 ◽  
Author(s):  
Daniel Wai-Wah Cheung ◽  
Subhash C. Sharma ◽  
Paul B. Trescott
Keyword(s):  
Interest Rate ◽  
Exchange Rates ◽  
Fixed Exchange Rates ◽  
The Interest Rate ◽  
The Uk

Non-performance of financial contracts in agricultural lending

2016 ◽  
Vol 76 (3) ◽  
pp. 362-377 ◽  
Author(s):  
Basri Savitha ◽  
Naveen Kumar K.
Keyword(s):  
Default Risk ◽  
Secondary Data ◽  
Binary Logistic Regression ◽  
Farm Size ◽  
Farm Income ◽  
Multinomial Regression ◽  
Agricultural Credit ◽  
Family Responsibilities ◽  
Content Type ◽  
Loan Defaults

Purpose Evaluating a portfolio of agricultural loans has become an important issue in recent years primarily due to a large number of loan defaults. The purpose of this paper is to investigate the factors influencing credit repayment behavior of farmers in Karnataka. Design/methodology/approach The study is based on secondary data of 590 farmers collected from a private bank in the state of Karnataka, India. Binary logistic regression and multinomial regression analysis was carried out to estimate the probability of non-payment of a loan. Findings The results of the regression confirm a significant relationship between non-repayment of agricultural credit and characteristics of borrowers such as the age, years of banking relationship, yield of the crop, distance to bank branch, size and tenure of the loan, farm size and leverage and efficiency ratio. Practical implications The factors predicted by the model do certainly help in improving the decision-making process in agricultural lending. A rigorous assessment of family responsibilities, farm size, credit-to-asset ratio, interest burden on the farmers and farm income is suggested to reduce the probability of doubtful assets. Originality/value The studies that predict default risk in agricultural loan are limited in India. This is one of the few studies that estimate the determinants of substandard and doubtful categories of credit in a private sector bank.

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