The prediction algorithm of credit risk of science and technology finance based on cloud computing

2021 ◽  
pp. 1-17
Author(s):  
Guiping Li
Keyword(s):  
Cloud Computing ◽  
Logistic Regression ◽  
Credit Risk ◽  
Regression Model ◽  
Risk Prediction ◽  
Logistic Regression Model ◽  
Science And Technology ◽  
Principal Component ◽  
Prediction Algorithm ◽  
Financial Indicators

In order to effectively guarantee the effect of credit risk prediction of science and technology finance and improve the ability of risk prediction, a credit risk prediction algorithm of science and technology finance based on cloud computing is proposed. The logistic regression model is used to predict, and the financial indicators of science and technology credit are selected as the model covariates. According to the characteristics and strong correlation of many financial indicators of science and technology credit, this paper constructs the final index system of online supply chain technology credit risk evaluation based on SMEs. Then the principal component analysis method is used to select the principal component. Combined with the penalty method, the data space dimension of financial indicators is further reduced, and the unrelated principal components are obtained. On this basis, a logistic regression model is established to predict the credit risk by taking the selected main components as covariates. The experimental results show that the algorithm has a good fit to the credit risk of 16 science and technology credit enterprises, and the risk prediction ability is significantly improved, which can effectively guarantee the effect of science and technology credit risk prediction.

scholarly journals Credit Scoring for Financial Services Institution using Ant Colony Optimization Algorithm under Logistic Regression Model

2019 ◽  
Vol 8 (4) ◽  
pp. 5957-5961
Keyword(s):  
Logistic Regression ◽  
Credit Risk ◽  
Regression Model ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Logistic Regression Model ◽  
Financial Institution ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Credit Analysis

Economic and trade activities are important in a country. All these activities are regulated by financial institutions, such as banks. The process of channeling funds to the public or known as credit is one of the tasks of the banking sector which aims to improve the people's economy. Credit granting is required for credit analysis, which is useful to determine the level of eligibility of a debtor to receive credit. The function of the credit analysis is to reduce the credit risk of prospective debtors who have failed to pay as well as to avoid financial institution losses or charges. The method used to analyze credit risk in this study is the Ant Colony Optimization algorithm in the Logistic Regression model. Past data held by each prospective debtor obtained from one financial institution in Indonesia is used as a feasibility parameter in this analysis. The results of the study showed that eight variables analyzed were five variables including the significant influence (age of debt ( 1 X ), family dependents ( 2 X ), value of the collection ( 4 X ), the number of credit limits ( 6 X ), and the term of the loan ( 8 X ) while the other three variables (the amount of savings ( 3 X ), income per month ( 5 X ), net income ( 7 X ) are not significant to the risk of default.

scholarly journals Comparison between normal weights and conditional Bays weights in Iterative principal component estimators

2018 ◽  
Vol 24 (109) ◽  
pp. 535
Author(s):  
اياد حبيب شمال
Keyword(s):  
Logistic Regression ◽  
Renal Disease ◽  
Regression Model ◽  
Nonlinear Regression ◽  
Logistic Regression Model ◽  
Principal Component ◽  
Binary Response ◽  
Nonlinear Regression Model ◽  
Qualitative Variable ◽  
Mean Error

Abstract: This paper discusses the problem of semi maulticollinearity in the nonlinear regression model (the multi-logistic regression model) When the dependent variable is a qualitative variable, the binary response is either equal to one for a response or zero for no response, Through the use of Iterative principal component estimatorsWhich are based on the normal weights and conditional Bays weights . If the appliede Estimates this model Through the use of two types of drugs concentrations thy concentration of ciprodar (variable X1) On a number of people with Patients with renal disease represent the dependent variable (The person heals from the disease  , The person has not recovered from the disease )from through Mean Error Squares (MSE) The results were indicative of Iterative principal component estemaite   Depending on the conditional Bays weights prefer the Iterative principal component estimators Depending on the the normal weights.

scholarly journals Credit risk assessment of agricultural enterprises in the Republic of Serbia: Logistic regression vs discriminant analysis

2021 ◽  
Vol 68 (4) ◽  
pp. 881-894
Author(s):  
Dragana Tekić ◽  
Beba Mutavdžić ◽  
Dragan Milić ◽  
Nebojša Novković ◽  
Vladislav Zekić ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Logistic Regression ◽  
Discriminant Analysis ◽  
Credit Risk ◽  
Regression Model ◽  
Financial Stability ◽  
Logistic Regression Model ◽  
Agricultural Enterprises ◽  
Credit Risk Assessment ◽  
The Republic

Credit risk assessment of agricultural enterprises in the Republic of Serbia was analyzed in this research by applying discriminant analysis and logistic regressions. The aim of the research is to determine the financial indicators which financial analysts consider when analyzing a loan application that have the most influence on the decision to approve or reject a loan application. The internal determinants of credit risk of agricultural enterprises are analyzed, i.e., indicators of financial leverage, profitability, liquidity, solvency, financial stability and effectiveness. The analyzed models gave different results in significance of the observed indicators. The indicators that stood out as significant in both models are only indicators of profitability and solvency. The model of discriminant analysis has successfully classified rate 81.0%, while the logistic regression model has successfully classifies rate 89.8%. In modeling the credit risk of agricultural enterprises in the Republic of Serbia, the logistic regression model gives better results.

scholarly journals Does Good ESG Lead to Better Financial Performances by Firms? Machine Learning and Logistic Regression Models of Public Enterprises in Europe

2020 ◽  
Vol 12 (13) ◽  
pp. 5317 ◽  
Author(s):  
Caterina De Lucia ◽  
Pasquale Pazienza ◽  
Mark Bartlett
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Investment Decisions ◽  
Current Debate ◽  
Financial Indicators ◽  
Public Enterprises ◽  
Responsible Investment ◽  
Ordered Logistic Regression

The increasing awareness of climate change and human capital issues is shifting companies towards aspects other than traditional financial earnings. In particular, the changing behaviors towards sustainability issues of the global community and the availability of environmental, social and governance (ESG) indicators are attracting investors to socially responsible investment decisions. Furthermore, whereas the strategic importance of ESG metrics has been particularly studied for private enterprises, little attention have received public companies. To address this gap, the present work has three aims—1. To predict the accuracy of main financial indicators such as the expected Return of Equity (ROE) and Return of Assets (ROA) of public enterprises in Europe based on ESG indicators and other economic metrics; 2. To identify whether ESG initiatives affect the financial performance of public European enterprises; and 3. To discuss how ESG factors, based on the findings of aims #1 and #2, can contribute to the advancements of the current debate on Corporate Social Responsibility (CSR) policies and practices in public enterprises in Europe. To fulfil the above aims, we use a combined approach of machine learning (ML) techniques and inferential (i.e., ordered logistic regression) model. The former predicts the accuracy of ROE and ROA on several ESG and other economic metrics and fulfils aim #1. The latter is used to test whether any causal relationships between ESG investment decisions and ROA and ROE exist and, whether these relationships exist, to assess their magnitude. The inferential analysis fulfils aim #2. Main findings suggest that ML accurately predicts ROA and ROE and indicate, through the ordered logistic regression model, the existence of a positive relationship between ESG practices and the financial indicators. In addition, the existing relationship appears more evident when companies invest in environmental innovation, employment productivity and diversity and equal opportunity policies. As a result, to fulfil aim #3 useful policy insights are advised on these issues to strengthen CSR strategies and sustainable development practices in European public enterprises.

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