Begoña Gutiérrez-Nieto

Partial Least Square Discriminant Analysis for bankruptcy prediction

This paper uses Partial Least Square Discriminant Analysis (PLS-DA) for the prediction of the 2008 USA banking crisis. PLS regression transforms a set of correlated explanatory variables into a new set of uncorrelated variables, which is appropriate in the presence of multicollinearity. PLS-DA performs a PLS regression with a dichotomous dependent variable. The performance of this technique is compared to the performance of 8 algorithms widely used in bankruptcy prediction. In terms of accuracy, precision, F-score, Type I error and Type II error, results are similar; no algorithm outperforms the others. Behind performance, each algorithm assigns a score to each bank and classifies it as solvent or failed. These results have been analyzed by means of contingency tables, correlations, cluster analysis and reduction dimensionality techniques. PLS-DA results are very close to those obtained by Linear Discriminant Analysis and Support Vector Machine.

Determinants of Default in P2P Lending.

This paper studies P2P lending and the factors explaining loan default. This is an important issue because in P2P lending individual investors bear the credit risk, instead of financial institutions, which are experts in dealing with this risk. P2P lenders suffer a severe problem of information asymmetry, because they are at a disadvantage facing the borrower. For this reason, P2P lending sites provide potential lenders with information about borrowers and their loan purpose. They also assign a grade to each loan. The empirical study is based on loans’ data collected from Lending Club (N = 24,449) from 2008 to 2014 that are first analyzed by using univariate means tests and survival analysis. Factors explaining default are loan purpose, annual income, current housing situation, credit history and indebtedness. Secondly, a logistic regression model is developed to predict defaults. The grade assigned by the P2P lending site is the most predictive factor of default, but the accuracy of the model is improved by adding other information, especially the borrower’s debt level.

Factors Influencing Funder Loyalty to Microfinance Institutions

This study unveils which factors affect decisions to fund Microfinance Institutions (MFIs). A quality-loyalty model is proposed to explain funder—MFI relationships. The role played by outreach and sustainability as perceived quality antecedents within a MFI has been studied. The model includes MFI transparency as an antecedent of trust. The proposed model is tested using a survey of 116 managers of MFI funding bodies. The analytical technique used to test the model is Partial Least Squares (PLS). The results suggest that both outreach and sustainability are important for MFI funders. The managerial implications for MFIs are discussed.

A decision support system for financial and social investment

This article proposes a decision-making model that assesses the different aspects associated to Social Venture Capital (SVC) investment decisions. SVC companies buy shares of investee companies, valued according to financial and social aspects. The proposed model includes three main factors: the previous experience with the company (the past); its financial information and intangible assets (the present); and the proposed project, considering financial and social criteria (the future). The model has 26 criteria and 160 indicators, prioritized by means of Analytic Hierarchy Process (AHP). AHP simplifies a complex problem using a hierarchical analysis methodology, which enables subjective judgements among different criteria. The model has been tested in a given SVC company. Its development is explained in this article.

Determinants of Margin in Microfinance Institutions

ABSTRACT Microfinance institutions (MFIs) lend to the poor. However, microfinance clients suffer from high interest rates, a type of poverty penalty. This article analyses the margin determinants in MFIs. A banking model has been adapted to microfinance. This model has been tested using 9-year panel data. Some factors explaining bank margin also explain MFI margin, with operating costs being the most important factor. Specific microfinance factors are donations and legal status, as regulated MFIs can collect deposits. It has also been found that MFIs operating in countries with a high level of financial inclusion have low margins.

Path modelling to bankruptcy: causes and symptoms of the banking crisis

This article studies the bankruptcy of US banks since 2009. It first analyses the financial symptoms that precede bankruptcy, such as low profitability, insufficient revenue or low solvency ratios. It also goes into the causes of these symptoms. It poses several hypotheses on causes of failure, such as loan growth (some of them risky), specialization (in this case concentration in real estate) and the pursuit of a turnover-driven strategy neglecting margin. It presents and tests a structural equation modelling based on partial least squares path modelling (PLS-PM) and logistic regression. Results show that, 5 years before the crisis, failed banks had, compared to solvent banks, the following: higher loan growth, higher concentration on real estate loans, higher risk ratios, higher turnover, but lower margins. A relationship is found between symptoms and causes. Failed banks present a significant relationship between the percentage of real estate loans and risk. This relationship is negative in excellent banks, confirming that they allocated less real estate loans with a high quality. Nonfailed banks compensated increases in risk by strengthening their core capital.