Support Vector Machines for Credit Scoring and discovery of significant features

The assessment of risk of default on credit is important for financial institutions. Logistic regression and discriminant analysis are techniques traditionally used in credit scoring for determining likelihood to default based on consumer application and credit reference agency data. We test support vector machines against these traditional methods on a large credit card database. We find that they are competitive and can be used as the basis of a feature selection method to discover those features that are most significant in determining risk of default. 1

EU banks rating assignments: Is there heterogeneity between new and old member countries?

We model EU countries’ bank ratings using financial variables and allowing for intercept and slope heterogeneity. We find that country-specific factors (in the form of heterogeneous intercepts) are a crucial determinant of ratings. Whilst “new” EU countries typically have lower ratings than “old” EU countries, after ontrolling for financial variables, all countries are found to have significantly different intercepts, which confirms our hypothesis. This intercept heterogeneity may reflect differences in country risk and the legal and regulatory framework that banks face (such as foreclosure laws). In addition, ratings may respond differently to the liquidity and operating expenses to operating income variables across countries: typically ratings are more responsive to the former and less sensitive to the latter for “new” EU countries compared with “old” EU countries

Equity Forecast: Predicting Long Term Stock Price Movement using Machine Learning

Abstract. Long term investment is one of the major investment strategies. However, calculating intrinsic value of some company and evaluating shares for long term investment is not easy, since analyst have to care about a large number of financial indicators and evaluate them in a right manner. So far, little help in predicting the direction of the company value over the longer period of time has been provided from the machines. In this paper we present a machine learning aided approach to evaluate the equity’s future price over the long time. Our method is able to correctly predict whether some company’s value will be 10% higher or not over the period of one year in 76.5% of cases.Keywords. Machine learning, Long term investment, Equity, Stock price prediction.JEL. H54, D92, E20

Rating assignments: Lessons from international banks

This paper estimates ordered logit and probit regression models for bank ratings which also include a country index to capture country-specific variation. The empirical findings provide support to the hypothesis that the individual international bank ratings assigned by Fitch Ratings are underpinned by fundamental quantitative financial analyses. Also, there is strong evidence of a country effect. Our model is shown to provide accurate predictions of bank ratings for the period prior to the 2007 – 2008 banking crisis based upon publicly available information. However, our results also suggest that quantitative models are not likely to be able to predict ratings with complete accuracy. Furthermore, we find that both quantitative models and rating agencies are likely to produce highly inaccurate predictions of ratings during periods of financial instability

The Impacts of Machine Learning in Financial Crisis Prediction

The most complicated and expected issue to be handled in corporate firms, small-scale businesses, and investors’ even governments are financial crisis prediction. To this effect, it was of interest to us to investigate the current impact of the newly employed technique that is machine learning (ML) to handle this menace in all spheres of business both private and public. The study uses systematic literature assessment to study the impact of ML in financial crisis prediction. From the selected works of literature, we have been able to establish the important role play by this method in the prediction of bankruptcy and creditworthiness that was not handled appropriately by others method. Also, machine learning helps in data handling, data privacy, and confidentiality. This study presents a leading approach to achieving financial growth and plasticity in corporate organizations. We, therefore, recommend a real-time study to investigate the impact of ML in FCP. &nbsp

Rating Assignments: Lessons from International Banks

This paper estimates ordered logit and probit regression models for bank ratings which also include a country index to capture country-specific variation. The empirical findings provide support to the hypothesis that the individual international bank ratings assigned by Fitch Ratings are underpinned by fundamental quantitative financial analyses. Also, there is strong evidence of a country effect. Our model is shown to provide accurate predictions of bank ratings for the period prior to the 2007 - 2008 banking crisis based upon publicly available information. However, our results also suggest that quantitative models are not likely to be able to predict ratings with complete accuracy. Furthermore, we find that both quantitative models and rating agencies are likely to produce highly inaccurate predictions of ratings during periods of financial instability.International banks, ratings, ordered choice models, country index

Neural Networks, Ordered Probit Models and Multiple Discriminants. Evaluating Risk Rating Forecasts of Local Governments in Mexico.

Credit risk ratings have become an important input in the process of improving transparency of public finances in local governments and also in the evaluation of credit quality of state and municipal governments in Mexico. Although rating agencies have recently been subjected to heavy criticism, credit ratings are indicators still widely used as a benchmark by analysts, regulators and banks monitoring financial performance of local governments in stable and volatile periods. In this work we compare and evaluate the performance of three forecasting methods frequently used in the literature estimating credit ratings: Artificial Neural Networks (ANN), Ordered Probit models (OP) and Multiple Discriminant Analysis (MDA). We have also compared the performance of the three methods with two models, the first one being an extended model of 34 financial predictors and a second model restricted to only six factors, accounting for more than 80% of the data variability. Although ANN provides better performance within the training sample, OP and MDA are better choices for classifications in the testing sample respectively.Credit Risk Ratings, Ordered Probit Models, Artificial Neural Networks, Discriminant Analysis, Principal Components, Local Governments, Public Finance, Emerging Markets

EU Banks Rating Assignments: Is there Heterogeneity between New and Old Member Countries?

We model EU countries' bank ratings using financial variables and allowing for intercept and slope heterogeneity. Our aim is to assess whether "old" and "new" EU countries are rated differently and to determine whether "new" ones are assigned lower ratings, ceteris paribus, than "old" ones. We find that country-specific factors (in the form of heterogeneous intercepts) are a crucial determinant of ratings. Whilst "new" EU countries typically have lower ratings than "old" ones, after controlling for financial variables we also discover that all countries have significantly different intercepts, confirming our prior belief. This intercept heterogeneity suggests that each country's rating is assigned uniquely, after controlling for differences in financial factors, which may reflect differences in country risk and the legal and regulatory framework that banks face (such as foreclosure laws). In addition, we find that ratings may respond differently to the liquidity and operating expenses to operating income variables across countries. Typically ratings are more responsive to the former and less sensitive to the latter for "new" EU countries compared with "old" EU countries.EU countries, banks, ratings, ordered probit models, index of indicator variable

Rating Assignments: Lessons from International Banks

This paper estimates ordered logit and probit regression models for bank ratings which also include a country index to capture country-specific variation. The empirical findings provide support to the hypothesis that the individual international bank ratings assigned by Fitch Ratings are underpinned by fundamental quantitative financial analyses. Also, there is strong evidence of a country effect. Our model is shown to provide accurate predictions of bank ratings for the period prior to the 2007 – 2008 banking crisis based upon publicly available information. However, our results also suggest that quantitative models are not likely to be able to predict ratings with complete accuracy. Furthermore, we find that both quantitative models and rating agencies are likely to produce highly inaccurate predictions of ratings during periods of financial instability.international banks, ratings, ordered choice models, country index