Loan pricing model for SMEs based on credit risk adjustment

2011 ◽  
Author(s):  
Qian Rao ◽  
Yan Zhang ◽  
Peng Yang
Keyword(s):  
Credit Risk ◽  
Risk Adjustment ◽  
Pricing Model ◽  
Loan Pricing

Internet financial supervision based on machine learning and improved neural network

2020 ◽  
pp. 1-12
Author(s):  
Cao Yanli
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Credit Risk ◽  
Machine Learning Algorithms ◽  
Financial Supervision ◽  
Loan Pricing ◽  
Comprehensive Management ◽  
Risk Pricing ◽  
Basel Agreement ◽  
Relevant Factors

The research on the risk pricing of Internet finance online loans not only enriches the theory and methods of online loan pricing, but also helps to improve the level of online loan risk pricing. In order to improve the efficiency of Internet financial supervision, this article builds an Internet financial supervision system based on machine learning algorithms and improved neural network algorithms. Moreover, on the basis of factor analysis and discretization of loan data, this paper selects the relatively mature Logistic regression model to evaluate the credit risk of the borrower and considers the comprehensive management of credit risk and the matching with income. In addition, according to the relevant provisions of the New Basel Agreement on expected losses and economic capital, starting from the relevant factors, this article combines the credit risk assessment results to obtain relevant factors through regional research and conduct empirical analysis. The research results show that the model constructed in this paper has certain reliability.

scholarly journals A multicriteria credit scoring model for SMEs using hybrid BWM and TOPSIS

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Pranith Kumar Roy ◽  
Krishnendu Shaw
Keyword(s):  
Credit Risk ◽  
Financial Institutions ◽  
Credit Scoring ◽  
Real Life ◽  
Financial Data ◽  
Loan Pricing ◽  
Scoring Model ◽  
Record Keeping ◽  
Return On Capital ◽  
Credit Scoring Model

AbstractSmall- and medium-sized enterprises (SMEs) have a crucial influence on the economic development of every nation, but access to formal finance remains a barrier. Similarly, financial institutions encounter challenges in the assessment of SMEs’ creditworthiness for the provision of financing. Financial institutions employ credit scoring models to identify potential borrowers and to determine loan pricing and collateral requirements. SMEs are perceived as unorganized in terms of financial data management compared to large corporations, making the assessment of credit risk based on inadequate financial data a cause for financial institutions’ concern. The majority of existing models are data-driven and have faced criticism for failing to meet their assumptions. To address the issue of limited financial record keeping, this study developed and validated a system to predict SMEs’ credit risk by introducing a multicriteria credit scoring model. The model was constructed using a hybrid best–worst method (BWM) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Initially, the BWM determines the weight criteria, and TOPSIS is applied to score SMEs. A real-life case study was examined to demonstrate the effectiveness of the proposed model, and a sensitivity analysis varying the weight of the criteria was performed to assess robustness against unpredictable financial situations. The findings indicated that SMEs’ credit history, cash liquidity, and repayment period are the most crucial factors in lending, followed by return on capital, financial flexibility, and integrity. The proposed credit scoring model outperformed the existing commercial model in terms of its accuracy in predicting defaults. This model could assist financial institutions, providing a simple means for identifying potential SMEs to grant credit, and advance further research using alternative approaches.

Fact, Fiction, and the Size Effect

2018 ◽  
Keyword(s):  
Asset Pricing ◽  
Size Effect ◽  
Financial Markets ◽  
Market Efficiency ◽  
Risk Adjustment ◽  
Empirical Work ◽  
Pricing Model ◽  
Asset Pricing Model ◽  
Data Errors ◽  
Small Stock

In the earliest days of empirical work in academic finance, the size effect was the first market anomaly to challenge the standard asset pricing model and prompt debates about market efficiency. The notion that small stocks have higher average returns than large stocks, even after risk adjustment, was a path-breaking discovery, and for decades it has been taken as an unwavering fact of financial markets. In practice, the discovery of the size effect fueled a crowd of small-cap indexes and active funds to the point that the investment landscape is now segmented into large and small stock universes. However, despite its long and illustrious history in academia and its commonplace acceptance in practice, there is still confusion and debate about the size effect. We examine many claims about the size effect and aim to clarify some of the misunderstanding surrounding it by performing simple tests using publicly available data. For one, using 90+ years of U.S. data, there is no evidence of a pure size effect; moreover, it may not have existed in the first place, if not for data errors and insufficient adjustments for risk and liquidity.

scholarly journals Pricing Convertible Bonds with Credit Risk under Regime Switching and Numerical Solutions

2014 ◽  
Vol 2014 ◽  
pp. 1-13
Author(s):  
Wei-Guo Zhang ◽  
Ping-Kang Liao
Keyword(s):  
Credit Risk ◽  
Regime Switching ◽  
Numerical Solutions ◽  
Dilution Effect ◽  
Convertible Bonds ◽  
Bond Pricing ◽  
Convertible Bond ◽  
Pricing Model ◽  
Black Scholes ◽  
The Impact

This paper discusses the convertible bonds pricing problem with regime switching and credit risk in the convertible bond market. We derive a Black-Scholes-type partial differential equation of convertible bonds and propose a convertible bond pricing model with boundary conditions. We explore the impact of dilution effect and debt leverage on the value of the convertible bond and also give an adjustment method. Furthermore, we present two numerical solutions for the convertible bond pricing model and prove their consistency. Finally, the pricing results by comparing the finite difference method with the trinomial tree show that the strength of the effect of regime switching on the convertible bond depends on the generator matrix or the regime switching strength.

scholarly journals THE IMPACT OF INFORMATION ASYMMETRY, MORAL HAZARD AND THE STRUCTURE OF FUNDING ON CORPORATE U.S. DOLLARS LOAN PRICING: THE EMPIRICAL STUDY IN INDONESIA THE PERIOD 1990-1997

2017 ◽  
Vol 11 (1) ◽  
Author(s):  
Deddy Marciano ◽  
Suad Husnan
Keyword(s):  
Moral Hazard ◽  
Information Asymmetry ◽  
Ordinary Least Squares ◽  
Regression Coefficients ◽  
Special Relationship ◽  
Pricing Model ◽  
Loan Pricing ◽  
Capital Costs ◽  
Increased Risk ◽  
The Impact

This study aims to answer the question: "What factors that influence the price of corporate loans in Indonesia?" And "Are there some differences in loan pricing between several types of creditors?". Furthermore, this research is to develop and test the loan pricing model that was developed in America and Europe to the context or setting in Asia, especially Indonesia. Different conditions and settings of the financial system between America/Europe and Asia, especially Indonesia, causing the loan pricing model that was developed in America/Europe can not be fully implemented for Indonesia. Key issues in this study consisted of: information asymmetry, moral hazard and funding structure. The first issue, information asymmetry consists of the type of creditors, foreign and domestic ownership, public and non-public ownership. The second issue, moral hazard problem consists of variables governmental and non-government ownership, and the special relationship between creditors and debtors. The last issue, creditors’ structure of funding is proxied by the ratio of CD / ML. In addition, this study also adobt the loan pricing models that are developed in America / Europe as control variables. This study also examines the argument of Strahan (1999) whether the loan fees also reflected the condition of the loan as well as loan spreads. The OLS regression (Ordinary Least Squares) with white correction method (White heteroskedasticity correction) for heteroscedasticity problem is conducted to test the model. Various samples and sub samples are prepared to answer various research questions and hypotheses. Testing between regression coefficients are conducted to examine differences in loan pricing between different types of creditors for each variable in the model. The test results generally show that only two new variables suggested by the study, namely: ownership and structure of funding have a significant contribution to the loan pricing model. For variable type of institution consisting of investment banks and commercial banks indicate that generally there is no difference in loan pricing between the two, only in some models of these variables are not significant with signs consistent.Ownership variable show results consistent with the hypothesis and significant effect on loan prices. While the variable special relationship between creditors and debtors have no effect on loan prices, it is due to inter-group loans made by conglomerates. For the case of capital costs of the creditor shows that the variable has a positive effect on lending rates set by creditors. Testing different regression coefficients lead to the conclusion that domestic creditors succeeded in detecting an increased risk of the debtor before the economic crisis of 1997 compared with foreign creditors.

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