scholarly journals IDENTYFIKACJA CZYNNIKÓW WPŁYWAJĄCYCH NA OCENĘ RYZYKA KREDYTOWEGO MIKROPRZEDSIĘBIORSTW DZIAŁAJĄCYCH W REGIONIE ŁÓDZKIM

2021 ◽  
Vol 21 (1) ◽  
pp. 11-19
Author(s):  
Bernard Kokczyński ◽  
Dorota Witkowska
Keyword(s):  
Linear Regression ◽  
Credit Risk ◽  
Risk Evaluation ◽  
Small Firms ◽  
Real Data ◽  
External Financing ◽  
Factors Influencing

Microenterprises have limited sources of external financing that is provided mostly by banks. The aim of our research is to identify factors influencing credit risk evaluation of small firms. Investigation is provided applying linear regression using real data concerning microenterprises functioning in Lodz region which obtained different form of credits in years 2016-2018. The results show that earnings and years of operating on the market are the most important factors which affect the improvement of credibility.

Robust cost-sensitive kernel method with Blinex loss and its applications in credit risk evaluation

2021 ◽  
Author(s):  
Jingjing Tang ◽  
Jiahui Li ◽  
Weiqi Xu ◽  
Yingjie Tian ◽  
Xuchan Ju ◽  
...  
Keyword(s):  
Credit Risk ◽  
Risk Evaluation ◽  
Kernel Method

scholarly journals Financial Credit Risk Evaluation Based on Core Enterprise Supply Chains

2018 ◽  
Vol 10 (10) ◽  
pp. 3699 ◽  
Author(s):  
WeiMing Mou ◽  
Wing-Keung Wong ◽  
Michael McAleer
Keyword(s):  
Supply Chain ◽  
Credit Risk ◽  
Supply Chains ◽  
Risk Evaluation ◽  
Operating Conditions ◽  
The Core ◽  
Supply Chain Finance ◽  
Enterprise Credit ◽  
Financial Credit ◽  
Core Enterprise

Supply chain finance has broken through traditional credit modes and advanced rapidly as a creative financial business discipline. Core enterprises have played a critical role in the credit enhancement of supply chain finance. Through the analysis of core enterprise credit risks in supply chain finance, by means of a ‘fuzzy analytical hierarchy process’ (FAHP), the paper constructs a supply chain financial credit risk evaluation system, making quantitative measurements and evaluation of core enterprise credit risk. This enables enterprises to take measures to control credit risk, thereby promoting the healthy development of supply chain finance. The examination of core enterprise supply chains suggests that a unified information file should be collected based on the core enterprise, including the operating conditions, asset status, industry status, credit record, effective information to the database, collecting related data upstream and downstream of the archives around the core enterprise, developing a data information system, electronic data information, and updating the database accurately using the latest information that might be available. Moreover, supply chain finance and modern information technology should be integrated to establish the sharing of information resources and realize the exchange of information flows, capital flows, and logistics between banks. This should reduce a variety of risks and improve the efficiency and effectiveness of supply chain finance.

MULTILEVEL LINEAR REGRESSION ANALYSIS OF FACTORS INFLUENCING BODY MASS INDEX AMONG BANGLADESHI MARRIED NON-PREGNANT WOMEN

2016 ◽  
Vol 49 (4) ◽  
pp. 498-508 ◽  
Author(s):  
Md Kamruzzaman ◽  
A. S. M. A. Mamun ◽  
Sheikh Muhammad Abu Bakar ◽  
Aik Saw ◽  
T. Kamarul ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Regression Analysis ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Reproductive Age ◽  
Cluster Effect ◽  
Women Of Reproductive Age ◽  
First Marriage ◽  
Factors Influencing ◽  
Age At First Marriage

SummaryThe aim of this study was to investigate the socioeconomic and demographic factors influencing the body mass index (BMI) of non-pregnant married Bangladeshi women of reproductive age. Secondary (Hierarchy) data from the 2011 Bangladesh Demographic and Health Survey, collected using two-stage stratified cluster sampling, were used. Two-level linear regression analysis was performed to remove the cluster effect of the variables. The mean BMI of married non-pregnant Bangladeshi women was 21.60±3.86 kg/m2, and the prevalence of underweight, overweight and obesity was 22.8%, 14.9% and 3.2%, respectively. After removing the cluster effect, age and age at first marriage were found to be positively (p<0.01) related with BMI. Number of children was negatively related with women’s BMI. Lower BMI was especially found among women from rural areas and poor families, with an uneducated husband, with no television at home and who were currently breast-feeding. Age, total children ever born, age at first marriage, type of residence, education level, level of husband’s education, wealth index, having a television at home and practising breast-feeding were found to be important predictors for the BMI of married Bangladeshi non-pregnant women of reproductive age. This information could be used to identify sections of the Bangladeshi population that require special attention, and to develop more effective strategies to resolve the problem of malnutrition.

scholarly journals MODEL JARINGAN SARAF TIRUAN UNTUK EVALUASI RESIKO KREDIT

2018 ◽  
Vol 2 (1) ◽  
pp. 76
Author(s):  
Novan Wijaya
Keyword(s):  
Neural Network ◽  
Credit Risk ◽  
Risk Evaluation ◽  
Evaluation System ◽  
Financial Risk ◽  
Banking Sector ◽  
Major Focus ◽  
Backpropagation Algorithm ◽  
The Neural Network ◽  
Risk Evaluation System

Credit risk evaluation is an importanttopic in financial risk management and become a major focus in the banking sector. This research discusses a credit risk evaluation system using an artificial neural network model based on backpropagation algorithm. This system is to train and test the neural network to determine the predictive value of credit risk, whether high riskorlow risk. This neural network uses 14 input layers, nine hidden layers and an output layer, and the data used comes from the bank that has branches in EastJakarta. The results showed that neural network can be used effectively in the evaluation of credit risk with accuracy of 88% from 100 test data

Credit Risk Evaluation Using Neural Networks

2008 ◽  
pp. 163-179 ◽  
Author(s):  
Z. Yang ◽  
D. Wu ◽  
G. Fu ◽  
C. Luo
Keyword(s):  
Neural Networks ◽  
Credit Risk ◽  
Risk Evaluation

The Distress Anomaly is Deeper than you Think: Evidence from Stocks and Bonds

2021 ◽  
Author(s):  
Doron Avramov ◽  
Tarun Chordia ◽  
Gergana Jostova ◽  
Alexander Philipov
Keyword(s):  
Credit Risk ◽  
Financial Distress ◽  
Corporate Bonds ◽  
Time Varying ◽  
External Financing ◽  
Earnings Expectations ◽  
Limits To Arbitrage

Abstract The distress anomaly reflects the abnormally low returns of high credit risk stocks during financial distress. Evidence from stocks and corporate bonds reinforces the anomaly and challenges rationales based on shareholders’ ability to extract value from bondholders, time-varying betas, lottery-type preferences, biased earnings expectations, and limits-to-arbitrage. Moreover, mispricing of distressed stocks and bonds is associated with excess investment and excess external financing. Potential real distortions are materially understated when assessed based only on equity mispricing. We emphasize the important role of corporate bonds in dissecting the distress anomaly, and show that the anomaly is an unresolved puzzle.

Transparent Decision Support System for Credit Risk Evaluation: An automated credit approval system

2020 ◽  
Author(s):  
Abhinaba Dattachaudhuri ◽  
Saroj Biswas ◽  
Sunita Sarkar ◽  
Arpita Nath Boruah
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Credit Risk ◽  
Support System ◽  
Risk Evaluation
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