Application of support vector machines on the basis of the first Hungarian bankruptcy model

In our study we rely on a data mining procedure known as support vector machine (SVM) on the database of the first Hungarian bankruptcy model. The models constructed are then contrasted with the results of earlier bankruptcy models with the use of classification accuracy and the area under the ROC curve. In using the SVM technique, in addition to conventional kernel functions, we also examine the possibilities of applying the ANOVA kernel function and take a detailed look at data preparation tasks recommended in using the SVM method (handling of outliers). The results of the models assembled suggest that a significant improvement of classification accuracy can be achieved on the database of the first Hungarian bankruptcy model when using the SVM method as opposed to neural networks

Portfolio Optimization of Commercial Banks- An Application of Genetic Algorithm

Portfolio optimization, in case of finance, is the trade- off between risk and return to maximize profit or return from the portfolio. Financial regulations are country specific and it depends upon the economic conditions prevailing in the country. The portfolio of a commercial bank can be constrained by regulatory prescription of exposure limits, risk weights and returns from each category of assets. Hence, optimization of return, in case of the loan portfolio, presents a challenging problem due to its large set of local extremes. In this context, Genetic Algorithm is used as a possible solution to optimize the risk-return trade-off and achieve an ideal solution for portfolio optimization. Keywords: Portfolio Management, Risk-Return Trade Off, Commercial Bankin

Financial distress prediction of Tehran Stock Exchange companies using support vector machine

The main objective of this study is to evaluate and to compare the power to predict company financial distress by utilizing the Support Vector Machine (SVM) to the multiple-discriminant analysis and the logistic regression models. Companies approved for acceptance into Tehran Stock Exchange Market between 2007 and 2013 comprise the statistical population for the study. In order to predict financial distress based on financial ratios such as profitability, activity ratio, ratios per share, etc. by using the Support Vector Machine (SVM), the sample data has been divided into two separate groups: the training group and the experimental group. The training set is made up of 540 year-company and the experimental set is comprised of 120 companies in 2013.  Finally, conclusions obtained from SVM, multiple-discriminant analysis and the logistic regression models for predicting financial failure were surveyed and compared. Results of testing hypothesis indicate with a 95% certainty ratio that there is a significant difference in the average prediction accuracy of the three models. Consequently among the three, the SVM model has the highest accuracy level for predicting company financial failure and the multiple-discriminant analysis model has the lowest

Financial distress prediction of Tehran Stock Exchange companies using support vector machine

The main objective of this study is to evaluate and to compare the power to predict company financial distress by utilizing the Support Vector Machine (SVM) to the multiple-discriminant analysis and the logistic regression models. Companies approved for acceptance into Tehran Stock Exchange Market between 2007 and 2013 comprise the statistical population for the study. In order to predict financial distress based on financial ratios such as profitability, activity ratio, ratios per share, etc. by using the Support Vector Machine (SVM), the sample data has been divided into two separate groups: the training group and the experimental group. The training set is made up of 540 year-company and the experimental set is comprised of 120 companies in 2013.  Finally, conclusions obtained from SVM, multiple-discriminant analysis and the logistic regression models for predicting financial failure were surveyed and compared. Results of testing hypothesis indicate with a 95% certainty ratio that there is a significant difference in the average prediction accuracy of the three models. Consequently among the three, the SVM model has the highest accuracy level for predicting company financial failure and the multiple-discriminant analysis model has the lowest

Do firm failure processes differ across countries: evidence from Finland and Estonia

This study considers the novel topic of comparing firm failure processes between different countries. For seventy bankrupt Finnish firms corresponding pairs are found among Estonian bankrupt firms based on industry, size and time of bankruptcy. Despite the similarity of firms from two countries, the analysis shows remarkable differences in both pre-failure financial data and reasons for failure. Based only on financial data, five failure processes are detected for Finnish and six for Estonian firms. Established failure processes associate with different failure reasons. The study contributes to literature by showing that for similar companies failure processes can differ across countries. In practice, the established information about different failure processes can be applied when building or using bankruptcy prediction models

Loan products and credit scoring methods by commercial banks

This study describes the loan products offered by the commercial banks and credit scoring techniques used for classifying risks and granting credit to the applicants in India. The loan products offered by commercial banks are: Housing loans, Personal loans, Business loan, Education loans, Vehicle loans etc. All the loan products are categorized as secures and unsecured loans. Credit scoring techniques used for both secured as well as unsecured loans are broadly divided into two categories as Advanced Statistical Methods and Traditional Statistical Methods.peer-reviewe

A Data-driven Case-based Reasoning in Bankruptcy Prediction

There has been intensive research regarding machine learning models for predicting bankruptcy in recent years. However, the lack of interpretability limits their growth and practical implementation. This study proposes a data-driven explainable case-based reasoning (CBR) system for bankruptcy prediction. Empirical results from a comparative study show that the proposed approach performs superior to existing, alternative CBR systems and is competitive with state-of-the-art machine learning models. We also demonstrate that the asymmetrical feature similarity comparison mechanism in the proposed CBR system can effectively capture the asymmetrically distributed nature of financial attributes, such as a few companies controlling more cash than the majority, hence improving both the accuracy and explainability of predictions. In addition, we delicately examine the explainability of the CBR system in the decision-making process of bankruptcy prediction. While much research suggests a trade-off between improving prediction accuracy and explainability, our findings show a prospective research avenue in which an explainable model that thoroughly incorporates data attributes by design can reconcile the dilemma