scholarly journals Ethnic Fractionalization, Governance and Loan Defaults in Africa

2017 ◽  
Vol 79 (4) ◽  
pp. 435-462 ◽  
Author(s):  
Svetlana Andrianova ◽  
Badi H. Baltagi ◽  
Panicos Demetriades ◽  
David Fielding
Keyword(s):  
Ethnic Fractionalization ◽  
Loan Defaults

scholarly journals Ethnic Fractionalization, Governance and Loan Defaults in Africa

2014 ◽  
Author(s):  
Svetlana Andrianova ◽  
Badi H. Baltagi ◽  
Panicos O. Demetriades ◽  
David Fielding
Keyword(s):  
Ethnic Fractionalization ◽  
Loan Defaults

LOGISTIC REGRESSIONMODELS FOR PREDICTION LOAN DEFAULTS-QUALTITATIVE DATA ANALYSIS

2020 ◽  
Vol 9 (8) ◽  
pp. 6027-6034
Author(s):  
E. Elakkiya ◽  
K. Radhaiah ◽  
G. Mokesh Rayalu
Keyword(s):  
Data Analysis ◽  
Loan Defaults

Do Lenders Affect Firms' Disclosure Decisions? Evidence from Lender-Side Loan Defaults

2016 ◽  
Author(s):  
Janet Gao ◽  
Chuchu Liang ◽  
Kenneth J. Merkley ◽  
Joseph M. Pacelli
Keyword(s):  
Disclosure Decisions ◽  
Loan Defaults

scholarly journals New evidence on the link between ethnic fractionalization and economic freedom

2021 ◽  
Author(s):  
Marta Marson ◽  
Matteo Migheli ◽  
Donatella Saccone
Keyword(s):  
Developing Countries ◽  
Ethnic Groups ◽  
Economic Freedom ◽  
Conclusive Evidence ◽  
Empirical Literature ◽  
Ethnic Fractionalization ◽  
Economic Freedom Index ◽  
New Evidence

AbstractAmong the determinants of economic freedom, the presence of different ethnic groups within a country has sometimes been explored by the empirical literature, without conclusive evidence on the sign of the relation, its drivers, and the conditions under which it holds. This paper offers new evidence by empirically modelling how ethnic fragmentation is related to economic freedom, as measured by the Economic Freedom Index and by each of its numerous areas, components and sub-components. The results provide insights on the components driving the effect and, interestingly, detect notable differences between developed and developing countries.

scholarly journals An Analysis of the Unpredictable Factors Influencing Loan Default in Small Scale Enterprises in the Port Loko Municipality in Sierra Leone using the Fish Bone Diagram

2021 ◽  
Vol 7 (2) ◽  
pp. 119-128
Author(s):  
Edmund Benedict Amara
Keyword(s):  
Sierra Leone ◽  
Research Question ◽  
Fish Bone ◽  
Small Scale ◽  
Microfinance Institutions ◽  
Random Sample Size ◽  
Loan Default ◽  
Factors Influencing ◽  
Fishbone Diagram ◽  
Loan Defaults

The study shows that there are unpredictable factors influencing loan default in small-scale enterprises in Port Loko Municipality. A fishbone diagram which is a cause an effect tool was used to determine these factors. A brainstorming activity was used to get the views of participants with regard to the Research Question. The research question was to respond to a research objective which was “Are there unpredicted factors influencing loan default in small scale enterprises in Port Loko Municipality in Sierra Leone?”. Reviews of necessary literature were done to aid the study. In the review, matters relating to loan default and possible causes were addressed. It is unfolded that there are some loan defaults that are as a result of the borrowers’ lapses and others that are lender-oriented causes. The population size of one hundred and a random sample size of sixty people were used as participants to carry out the brainstorming activity. The population is comprised of small-scale enterprise owners and workers of credit or Microfinance institutions in the Municipality. Brainstorming participants proved that the death of clients or borrowers, internal insecurity, outbreak of diseases (Pandemic), Natural Disasters, and accident all significantly influence loan default of small-scale enterprises.

Why segmentation matters: a Machine Learning approach for predicting loan defaults in the Peer-to-Peer (P2P) Financial Ecosystem

2021 ◽  
Vol 16 (2) ◽  
pp. 35-49
Author(s):  
Adamaria Perrotta ◽  
◽  
Georgios Bliatsios ◽  
Keyword(s):  
Machine Learning ◽  
Area Under The Curve ◽  
Peer To Peer ◽  
Weight Of Evidence ◽  
K Nearest Neighbors ◽  
Default Prediction ◽  
Sequential Feature Selection ◽  
Loan Defaults ◽  
The One ◽  
Online Lending

Peer-to-Peer (P2P) lending is an online lending process allowing individuals to obtain or concede loans without the interference of traditional financial intermediaries. It has grown quickly the last years, with some platforms reaching billions of dollars of loans in principal in a short amount of time. Since each loan is associated with the probability of loss due to a borrower's failure, this paper addresses the borrower's default prediction problem in the P2P financial ecosystem. The main assumption, which makes this study different from the available literature, is that borrowers sharing the same homeownership status display similar risk profile, thus a model per segment should be developed. We estimate the Probability of Default (PD) of a borrower by using Logistic Regression (LR) coupled with Weight of Evidence encoding. The features set is identified via the Sequential Feature Selection (SFS). We compare the forward against the backward SFS, in terms of the Area Under the Curve (AUC), and we choose the one that maximizes this statistic. Finally, we compare the results of the chosen LR approach against two other popular Machine Learning (ML) techniques: the k Nearest Neighbors (k-NN) and the Random Forest (RF).

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