scholarly journals Portfolio recommendations to improve risk of default in microfinance

2020 ◽  
Vol 28 (1) ◽  
pp. 1-7
Author(s):  
Irving Simonin ◽  
Marc Brooks ◽  
Luis Enrique Nieto Barajas
Keyword(s):  
Machine Learning ◽  
Financial Institutions ◽  
Statistical Models ◽  
Clustering Analysis ◽  
Default Risk ◽  
Regression Tree ◽  
Principal Component ◽  
Credit Default ◽  
Credit History ◽  
Exciting Application

This article presents an exciting application of machine learning for loan origination in microfinance. Microfinance targets people who cannot build a credit history and therefore cannot access loans from banks or other financial institutions. We use data from a Mexican microfinance company that operates in several regions throughout the country. The objective is to guide intermediate lenders to choose their clients and achieve a lowerr credit default risk. We use several statistical models such as principal component analysis, clustering analysis and a regression tree. We obtain, as a result, a series of recommendations based on the characteristics of the clients.

scholarly journals The Determinants of Credit Default Swap Premia

2009 ◽  
Vol 44 (1) ◽  
pp. 109-132 ◽  
Author(s):  
Jan Ericsson ◽  
Kris Jacobs ◽  
Rodolfo Oviedo
Keyword(s):  
Default Risk ◽  
Common Factor ◽  
Explanatory Power ◽  
Empirical Work ◽  
Credit Default Swap ◽  
Principal Component ◽  
Corporate Bond ◽  
Credit Default ◽  
Default Swap ◽  
Swap Spreads

AbstractVariables that in theory determine credit spreads have limited explanatory power in existing empirical work on corporate bond data. We investigate the linear relationship between theoretical determinants of default risk and default swap spreads. We find that estimated coefficients for a minimal set of theoretical determinants of default risk are consistent with theory and are significant statistically and economically. Volatility and leverage have substantial explanatory power in univariate and multivariate regressions. A principal component analysis of residuals and spreads indicates limited evidence for a residual common factor, confirming that the theoretical variables explain a significant amount of the variation in the data.

scholarly journals Study of the Insolvency and Bankruptcy Code 2016

2019 ◽  
pp. 221-233
Author(s):  
Vritti Bang ◽  
Shreyansh Bhansali ◽  
Devansh Doshi ◽  
Asawari Vedak
Keyword(s):  
Financial Institutions ◽  
Default Risk ◽  
Entire System ◽  
Public Sector Banks ◽  
Long Run ◽  
Credit Default ◽  
Bankruptcy Code ◽  
The Impact ◽  
Regulation Changes ◽  
Transparency Method

India has been riddling since decades with the problem of insolvency and bankruptcy issues. Several public sector banks, financial institutions and operational creditors were facing severe credit default risk. Various laws and codes have been passed as a corrective measure, but have proved to be inefficient and failed to provide any kind of relief to the creditors. There was thus a need for reform in insolvency and bankruptcy laws. The Insolvency and Bankruptcy code 2016 (IBC) has been instrumental in creating a shift in the way the bankruptcy process of defaulting firms has been dealt with. The IBC 2016 promises to bring about transparency, method and infrastructure in the entire system of liquidation. Changing up core aspects of the insolvency process, it gives companies a well-deserved chance at revival. Despite the recent amendments to the code and regulation changes by the Insolvency and Bankruptcy Board of India, there are still few grey areas in the code. This paper aims to thus test the effectiveness of the IBC 2016 since its introduction in 2016 and whether it resolves lags in the previous system. Hence, the paper dwells into the various components of IBC to critically analyse its sustainability and scalability. The research paper is purely based on secondary research through different news articles and reports from reliable sources. Though it is too early to comment on the impact of the IBC 2016, the researchers have tried to study the code and conclude whether it will be successful in fixing the problems and will keep up to its promise in the long run.

scholarly journals Machine learning applied to the prediction of citrus production

2017 ◽  
Vol 15 (2) ◽  
pp. e0205 ◽  
Author(s):  
Irene Díaz ◽  
Silvia M. Mazza ◽  
Elías F. Combarro ◽  
Laura I. Giménez ◽  
José E. Gaiad
Keyword(s):  
Machine Learning ◽  
Precision Agriculture ◽  
Piecewise Linear ◽  
Regression Tree ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Absolute Error ◽  
Linear Functions ◽  
Machine Learning Techniques ◽  
Citrus Production

An in-depth knowledge about variables affecting production is required in order to predict global production and take decisions in agriculture. Machine learning is a technique used in agricultural planning and precision agriculture. This work (i) studies the effectiveness of machine learning techniques for predicting orchards production; and (ii) variables affecting this production were also identified. Data from 964 orchards of lemon, mandarin, and orange in Corrientes, Argentina are analysed. Graphic and analytical descriptive statistics, correlation coefficients, principal component analysis and Biplot were performed. Production was predicted via M5-Prime, a model regression tree constructor which produces a classification based on piecewise linear functions. For all the species studied, the most informative variable was the trees’ age; in mandarin and orange orchards, age was followed by between and within row distances; irrigation also affected mandarin production. Also, the performance of M5-Prime in the prediction of production is adequate, as shown when measured with correlation coefficients (~0.8) and relative mean absolute error (~0.1). These results show that M5-Prime is an appropriate method to classify citrus orchards according to production and, in addition, it allows for identifying the most informative variables affecting production by tree.

scholarly journals Countries are Clustered but Number of Tests is not Vital to Predict Global COVID-19 Confirmed Cases: A Machine Learning Approach

2020 ◽  
Author(s):  
Md Hasinur Rahaman Khan ◽  
Ahmed Hossain
Keyword(s):  
Machine Learning ◽  
Regression Tree ◽  
Principal Component ◽  
Vital Role ◽  
Machine Learning Techniques ◽  
Cumulative Number ◽  
Individual Country ◽  
Country Level ◽  
Global Pandemic ◽  
Factor Regression

AbstractCOVID-19 disease is a global pandemic and it appears as pandemic for each and every nation and territory in the earth.This paper focusses on analysing the global COVID-19 data by popular machine learning techniques to know which covariates are importantly associated with the cumulative number of confirmed cases, whether the countries are clustered with respect to the covariates considered, whether the variation in the covariates are explained by any latent factor. Regression tree, cluster analysis and principal component analysis are implemented to global COVID-19 data of 133 countries obtained from the worldometer website as reported as on April 17, 2020. Our results suggest that there are four major clusters among the countries. First cluster consists of 8 countries where cumulative infected cases and deaths are highest. It is also revealed that there are two principal components. The countries which play vital role to explain the 60% variation of the total variations by the first component characterized by all variables except the rate variables include USA, Spain, Italy, France, Germany, UK, and Iran. Remaining countries contribute to explaining 20% variation of the total variations by the second component characterized by only three rate variables. We also found that the number of tests by the country variable among other variables country, number of active cases, number of deaths, number of recovered patients, number of serious cases, and number of new cases is an unimportant variable to predict cumulative number of confirmed cases. Hence, the number of tests might play vital role to individual country level who are in the primary level of virus spread but not to the global level.

scholarly journals Estimating credit default probabilities using stochastic optimisation

2021 ◽  
Vol 1 (3) ◽  
pp. 253-271
Author(s):  
Dominic Joseph ◽  
Keyword(s):  
Credit Risk ◽  
Financial Institutions ◽  
Default Risk ◽  
Credit Ratings ◽  
Mathematical Framework ◽  
Probability Of Default ◽  
Credit Risk Management ◽  
Stochastic Optimisation ◽  
Credit Default ◽  
The World

<abstract> <p>Banks and financial institutions all over the world manage portfolios containing tens of thousands of customers. Not all customers are high credit-worthy, and many possess varying degrees of risk to the Bank or financial institutions that lend money to these customers. Hence assessment of default risk that is calibrated and reflective of actual credit risk is paramount in the field of credit risk management. This paper provides a detailed mathematical framework using the concepts of Binomial distribution and stochastic optimisation, in order to estimate the Probability of Default for credit ratings. The empirical results obtained from the study have been illustrated to have potential application value and perform better compared to other estimation models currently in practise.</p> </abstract>

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

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