How To Predict Bankruptcy With Data Envelope Analysis
|
|
- Bruce Stephens
- 3 years ago
- Views:
Transcription
1 Volume 3, Issue 9, September 2013 ISSN: X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: Analysing Variable Benchmark DEA: A Comparative Study and Empirical Evidence Ayan Mukhopadhyay* Ankit Narsaria Suman Tiwari Bhaskar Roy Karmaker Cognizant Technology Solutions IIM, Shillong IIFT, Kolkata RCCIIT, Kolkata India India India India Abstract - Bankruptcy prediction from a paradigm of multiplicity of methodologies is very imperative to evaluate the relative performance of a particular method. This paper attempts to investigate the performance of DEA (Data Envelopment Analysis), namely, Variable Benchmark DEA with that of popular methods particularly neural network (namely, multilayer perceptron) and discriminant analysis on Indian data. We note that Indian data has never been explored in this context, to the best of our knowledge. Also, empirical evidence of Variable Benchmark DEA from a comparative perspective as bankruptcy predictor is a rarity. Our result shows, performance of Variable Benchmark DEA is at par with other methods in terms of prediction capability of bankruptcy. Keywords - Data Envelopment Analysis, Variable Benchmark DEA, Bankruptcy, Multi-Layer Perceptron, Neural Networks, Multiple Discriminant Analysis I. INTRODUCTION As pointed out by [1], the study of bankruptcy appeals equally to sociologists, political economists and jurists. Bankruptcy, its history and its study has drawn interest of many and is well studied in literature. As described by [2], Bankruptcy is an integrated legal solution to the problem of overwhelming debt. [3] highlights that the first official law on bankruptcy was passed in the 1542 in England and goes on to describe bankruptcy from the Indian perspective. It also states that the laws of bankruptcy are different in different countries. [1] also states that it is impossible to define bankruptcy in a specific manner that will apply with equal accuracy to different nations, periods and people. Every bankruptcy law, however, no matter when or where devised and enacted, has at least two general objects in view. It aims, first, to secure an equitable division of the insolvent debtor's property among all his creditors, and, in the second place, to prevent on the part of the insolvent debtor conduct detrimental to the interests of his creditors. Assessment of firms financial health is of great importance because the ill performance of a firm incurs direct and indirect cost on the firm s stakeholders. The prediction of bankruptcy is therefore widely popular and has also been studied several times. [4] suggests that bankruptcy prediction dates back to 1930s when ratio analysis was used to predict future bankruptcy. Since then, numerous other prediction mechanisms have been suggested. This paper analyses Variable Benchmark DEA as a predictor for corporate bankruptcy by comparing its performance with other prevalent methods. The choice of the data sample is interesting from an empirical perspective because despite the significant number of reported cases of bankruptcy, there has been little study on the instances of Indian bankruptcy cases. Also, the analysis of Variable Benchmark DEA from a comparative perspective is a rarity. In this paper, we analyse the comparative capability of Variable Benchmark Data Envelopment Analysis by exploring Indian data. As a classifying technique, DEA has several advantages. It is non-parametric in approach. It gives a single measure of performance taking multiple dimensions of corporate aspect of a firm. There is no assumption of a-priori functional form of inputs and outputs correspondence. It also has the capability to solve inverse classification problem. Thus, DEA as a methodology is ideal for use in early detection of financial distress. This paper attempts to apply the DEA technique to Indian cases of bankruptcy with empirical objective and also tries to assess its comparative performance with other methods MLP (Multi-Layer Perceptron) and MDA (Multiple Discriminant Analysis). MDA is a very common technique for bankruptcy study whereas MLP is an upcoming technique. The rest of the paper is organized as follows. Section 2 is a review of bankruptcy studies from methodological perspective. It also provides a review of comparative studies made regarding prediction of corporate bankruptcy. Section 3 describes the methodologies that we have adopted in this study. Section 4 describes the sample data and the variables chosen. Results and discussions are done in section 5. Section 6 concludes and highlights some of the future directions for further studies. II. REVIEW OF LITERATURE The study of prediction of bankruptcy started in the beginning of the 1930s. Univariate study was the major basis of the research [4]. In 1968, multivariate study regarding bankruptcy was published by [5]. A comprehensive review, provided by [6] categorized the methodologies into statistical models, artificially intelligent expert system models and theoretic models. Statistical models include Univariate Analysis ([7], [8]), Multiple Discriminant Analysis (MDA) ([5], [9]), Linear Probability model ([10], [11], [12]), Logit model ([10], [11]), Probit model ([10], [11]), Cumulative Sums (CUSUM) procedure ([13], [14]) and Partial Adjustment Process ([12], [15]). Artificial Intelligent Systems consist of 2013, IJARCSSE All Rights Reserved Page 910
2 Decision Tree based model, Case Based Reasoning (CBR) model ([19]), Neural Network based model ([17], [18]), Genetic Algorithm based model ([19], [20] and Rough Sets model ([21], [22]. Theoretic category of models includes Balance Sheet Decomposition measure (BSDM) ([23], [24]), Gambler s Ruin theory ([8], [25]), Cash Management theory and Credit Risk theory ([26]). As pointed out by [6], Data Envelopment Analysis is not an element of any of the categories mentioned above. DEA as a classifier is studied in [27], [28], [29], [30], [31], [32], [33], [34] and [35]. Among these nine studies, the last five studies are direct applications of DEA as a potential method for prediction of bankruptcy. This paper uses the techniques from [32] and [36] to assess bankruptcy of Indian firms. The study of different models for the prediction of bankruptcy from a comparative perspective has attracted attention of many researchers. Table 1 lists the studies that focussed on comparing different methodologies used for the prediction of bankruptcy. Author (Year) Robert A. Collins (1980) James Scott (1981) TABLE 1 REVIEW OF COMPARATIVE STUDIES Methodologies Compared Comparison between using data from one period before failure and more periods Comparison of Empirical Predictions and Theoretical Models J. Efrim Boritz, Duane B. Kennedy (1995) Guoqiang Zhang, Michael Y. Hu, B Eddy Patuwo, Daniel C. Indro (1997) Hongkyu Jo, Ingoo Han (1997) Ways of training Neural networks : Back-Propagation and Optimal Estimation Theory Neural Networks and Logistic Regression Models Case-Based Reasoning, Neural Networks, and Discriminant Analysis Vineet Agarwal, Richard Taffler (2008) Market-based and Accounting-Based Prediction Models I.M. Premachandra, Gurmeet Singh Bhabra, Toshiyuki Sueyoshi (2009) Toshiyuki Sueyoshi, Mika Goto (2009) Maryam Khalili Araghi, Sara Makvandi (2012) DEA and Logistic Regression DEA and DEA-DA (Discriminant Analysis) DEA, Logit and Probit Models III. METHODOLOGIES Discriminant analysis (DA), a statistical method of classification, was first used by [5] to differentiate between bankrupt and non-bankrupt firms. It was the first statistical method used for the purpose. Also known as Altman Z-score, the method uses a linear combination of independent variables to assign a score to each firm in the training set. Depending on a cut-off point, this score is then used to discriminate between bankrupt and non-bankrupt firms. However, the method s performance for out-of sample firms was weak despite its classification power being strong for firms in the sample. A multi-layer perceptron (MLP) is a feed-forward artificial neural network that can even distinguish between data that is not linearly separable. It maps a set of inputs to a set of outputs using neurons (processing elements). A directed graph is constructed with multiple layers of neurons with each neuron having a non-linear activation function and each layer connected to others through weights (called synaptic weights). At each neuron a simple weighted sum of the inputs is computed and depending on the activation function, it provides inputs to the next layer of neurons. Weights are adjusted on the basis of the error when the expected and actual output are compared. [40] and [41] developed these learning rules. The relationship between DA and MLP has been established in [37] and their classification powers have been compared in [38] and [39]. Data Envelopment Analysis (DEA) is a mathematical programming method that estimates the best practice production frontier by extending Farrell efficiency. DEA was first developed in [42]. DEA is a non-stochastic and nonparametric fractional linear programming approach. When j units consume i inputs (x) to produce r outputs (y), the efficiency of the j0th unit is computed as a solution to the LPP- Maximize Subject to 2013, IJARCSSE All Rights Reserved Page 911
3 For all j, v, u Ɛ Since the problem involves maximizing the output, the model is known as output-oriented DEA. The units on the frontier are considered efficient while those enveloped by the frontier can increase their outputs to reach the frontier. The best performers are thus on the best practice frontier. A variable benchmark model of the DEA algorithm is suggested in this paper for the purpose of classification. [44] has suggested that observations belonging to the same group should have the same production possibility set. These are also dominated by the same benchmarks which form a piecewise frontier. Two different frontiers generated in two different groups can be used for the purpose of classification. The goal, in case of discriminating between non-bankrupt and bankrupt firms, is to identify the bad performers rather than the good firms. The frontier used for classification, is hence, one that identifies the poor performers. The strategy is to identify variables that reflect poor utilization of resources or are unwelcome. The variables used for evaluating the frontier of non-bankrupt firms, for instance, are those that reflect poor financial health and indicate failure. The opposite is true for bankrupt firms as we are interested in finding the best firms that failed. Once the benchmarks have been identified, all the firms in the training set are evaluated using the following model Minimize δ (2) Subject to (1) Here x represents the inputs and y the outputs whereas E* is the identified benchmarks and p and r represent the number of inputs and outputs respectively. It has been suggested in [44] that a layering or peeling technique be used where frontiers are evaluated and firms on the frontier are removed. This process is iterated until two distinct hyper planes are identified that dominate disjoint PPS s. These hyper planes can now be used for the purpose of classification of the test data set. IV. POPULATION AND SAMPLE Our initial sample consists of 105 non-bankrupt and 14 bankrupt firms from database of The Centre for Monitoring Indian Economy (CMIE), which is an independent economic think-tank headquartered in Mumbai, India. The firms considered have filed for bankruptcy either in 1996 or Our sample does not contain matched pair of instances of bankrupt and non -bankrupt firms, necessarily. It is kind of a mixed sample to prevent loss of information as mentioned by [32]. In real world, the ratio of healthy firms to bankrupt firms is very high, somewhat like 100 to 1 for public companies. Also, we need to mention that our database contains a diverse range of industries. Our intention to use such a sample is to judge the robustness of other methods and the performance of DEA as a tool to assess bankruptcy. We have taken commonly used variables which are used to profile the strength and weakness of financial health of a firm. The variables used in this paper are common with those used by [5]. Table 2 indicates the variables used by different studies regarding bankruptcy. TABLE 2: REVIEW OF VARIABLES USED Author (Year) Altman (1993) Altman (1993) Ward (1995) I.M. Premachandra, Gurmeet Singh Bhabra, Toshiyuki Sueyoshi (2007) Variables Used Working Capital / Total Assets Retained Earnings / Total Assets Earnings Before Interest and Tax / Total Assets Market Value of Equity / Total Liabilities Sales / Total Assets Working Capital / Total Assets Retained Earnings / Total Assets Earnings Before Interest and Tax / Total Assets Market Value of Equity / Total Liabilities Sales / Total Assets Stability of Earnings Earnings Before Interest and Tax /Interest Expense Current Ratio Common Equity / Total Capital Lower operating payment outflows Long term investment inflows + Capital assets inflows Long-term financing inflows Short-term financing inflows Total debt/ Total Assets Current Liabilities/ Total Assets 2013, IJARCSSE All Rights Reserved Page 912
4 Cash flow/ Total assets. Net income/ Total assets. Working capital/ Total assets. Current assets/ Total assets. Earnings before interest and Taxes / Total assets. Earnings before interest and taxes/ Interest A description of the variables used in this paper is as follows Current Ratio - A liquidity ratio that measures a company s ability to pay short-term obligations. The ratio is given by: Net Working Capital to Total Assets - Net Working Capital to Total Assets ratio, is defined as the net current assets (net working capital) of a company expressed as a percentage of its total assets. Therefore, the formula is: Return on Assets - It is an indicator of how profitable a company is relative to its total assets. ROA gives an idea as to how efficient management is at using its assets to generate earnings. ROA is displayed as a percentage. The formula for the same is - Market to Book Ratio - It is a ratio used to calculate a firm s market value to its book value. It is given by - Interest Coverage Ratio - A ratio used to determine how easily a company can pay interest on outstanding debt. The interest coverage ratio is calculated by dividing a company s earnings before interest and taxes (EBIT) of one period by the company s interest expenses of the same period. It is given by - Total Debt Ratio - A ratio that indicates what proportion of debt a company has relative to its assets. The measure gives an idea to the leverage of the company along with the potential risks the company faces in terms of its debt-load. It is given by - All the variables used for the analysis are taken related to immediate preceding year of bankruptcy. The descriptive statistics of the variables are given in Table 3, both for the group of bankruptcy and non-bankruptcy. The Wilcoxon s rank sum test indicates that the median of all the variables are significantly different. The variable EBDIT (Earnings before Depreciation, Interest and Taxes) was excluded from the analysis, as it did not have significant difference between the two groups. Now, to select the input and the output variable among the set of variables we followed the approach found in [32]. Current ratio, Working Capital to Total Assets, Return on Assets, Market to Book ratio and Interest Coverage ratio are positive in nature and contribute to better financial health of a firm. On the other hand, the Total Debt ratio is opposite in nature. So while evaluating the output oriented negative DEA model for identifying the frontier from the non-bankrupt firms, we took Total Debt ratio as output and Current ratio, Working Capital to Total Assets, Return on Assets, Market to Book ratio and Interest Coverage ratio as inputs. The opposite was done while identifying the frontier from the bankrupt firms. We also mention that for other models of MDA and MLP, no such distinction is needed among the variables. All the variables are considered of the same nature for predicting the financial health of a firm. The models, once trained were then put to test using another set of data. The test dataset consists of 38 companies - 29 non bankrupt and 9 bankrupt. The test data is acquired from the same source and year as the dataset used to construct the initial sample. A description of the results is summarized in the next section. Variables -- > Bankrupt firms TABLE 3 : DESCRIPTIVE STATISTICS OF VARIABLES USED Net Working Interest Total Current Capital/Total Return Market to Coverage Debt ratio Assets on Assets Book ratio ratio ratio Mean Median Standard Deviation , IJARCSSE All Rights Reserved Page 913
5 Mean Non- Median Bankrupt Standard firms Deviation Wilcoxon s Rank Sum Test Z score P value 5.204e e e e e e-18 V. RESULTS Results show that variable benchmark DEA and MDA outperform MLP in correctly predicting bankruptcy. The first combination of frontiers identified separately from the bankrupt and the non-bankrupt firms forms a couple of distinct boundaries that successfully differentiates between the bankrupt and non-bankrupt firms included in the training sample. These are subsequently used on the test dataset. The performance of variable benchmark DEA and MDA is observed to be almost identical. They result in no type I error. However, a small percentage of type II error occurs in both the methods. MLP, on the other hand, correctly identifies all the non-bankrupt firms resulting in no type II error. A summary of the errors and the accuracies of the methods are given in Table 4. We find, the performance of variable benchmark DEA is at par with the other two methods chosen as far as the accuracy of prediction is concerned. TABLE 4 RESULTS Method Type 1 Error Type 2 Error Accuracy Variable Benchmark DEA % 96.55% MDA % 96.55% MLP 11.11% % However, it is vital to appreciate the aspect of costs of type I and type II errors rather than numerical accuracy in the perspective of prediction. The size of the loan and the recovery rate determine the cost of type I error (loss resulting from a company defaulting on a loan). As suggested by [32], the amount of loan recovered depends upon the nature of the debt and where it ranks among the obligations of the failed company. On the other hand, the cost of a Type II error (the loss represented by the revenue the bank would have received if it had made the successful loan) depends on the spread between the risk free interest rate and the foregone rate of return on the rejected loan. Therefore, the cost of type I error is a lot more than that of type II error. Hence, in terms of the total cost of errors, variable Benchmark DEA and MDA outperform MLP. The variable bench mark DEA can be successfully applied to predict bankruptcy even when dataset comes from a diverse group of industries. This indirectly indicates about the robustness of the method itself. The following section discusses the conclusions and future scope of study. VI. FUTURE SCOPE AND DISCUSSIONS The study establishes one more use of potential use of DEA, more specifically variable benchmark DEA as a potential technique for bankruptcy assessment. The classification accuracy in within-sample cases after finding two hyper planes that can be used for classification is 100% for bankrupt firms. It should also be noted that the Asymmetric DEA model, as suggested by [45], uses the same idea as variable benchmark DEA but applies a peeling technique to identify the optimal combination of hyper planes and focuses on minimizing the total misclassification cost and thereby recognizes the importance of avoiding type I errors over type II errors. The misclassification cost of other methods should be compared to that of Asymmetric DEA. The DEA method has the capability to classify and assess the state of financial health of a firm in a very computationally efficient manner without estimation of parameters as done in the other methods. Therefore it can be used as a ready reference for the assessment of firms from investment perspective. The major shortcoming is that it does not have the capability of prediction as found in the other methods. Even the existing dynamic DEA techniques are not suitable for bankruptcy assessment as they cannot handle negative values of variables. So, incorporation of time horizon factor into DEA methods regarding bankruptcy assessment can be a potential future research agenda. The performance of the adopted DEA method can be investigated with other existing econometric methods, DEA methods and hybrid methods. Also, we need to evaluate the capability of the methods in real life scenario of decisional cases and ex ante planning. REFERENCES [1] L. E. Levinthal, The Early History of English Bankruptcy, University of Pennsylvania Law Review and American Law Register 67.1, pp. 1-20, [2] K. Porter, Pretend Solution, An Empirical Study of Bankruptcy Outcomes, Tex. L. Rev. p. 103, 2011, vol.90 [3] A. Mukhopadhyay et al., A New Approach to Predicting Bankruptcy: Combining DEA and Multi-Layer Perceptron, International Journal of Computer Science Issues, 2011 [4] J. Bellovary, D. Giacomino and M. Akers, A Review of Bankruptcy Prediction Studies: 1930 to Present, Journal of Financial Education, 2007, Vol , IJARCSSE All Rights Reserved Page 914
6 [5] E. I. Altman, Financial ratios, Discriminant Analysis and The Prediction of Corporate Bankruptcy, Journal of Finance, pp , 1968, vol. 22 [7] M. A. Aziz, H. A. Dar, Predicting Corporate Bankruptcy: Whither do we stand? Corporate Governance, pp.18-33, 2006, Vol. 6 Iss: 1 [8] E. I. Altman, Corporate Financial Distress and Bankruptcy: A Complete Guide to Predicting Avoiding Distress and Profiting from Bankruptcy, Wiley Finance Edition, 1993 [9] R. Morris, Early Warning Indicators of Corporate Failure: A Critical Review of Previous Research and Further Empirical Evidence, Ashgate Publishing Company, 1998 [10] W. R. Klecka, Discriminant Analysis, London, Sage Publications, 1982 [11] G.S. Maddala, Limited Dependent and Qualitative Variables in Econometrics Cambridge, Cambridge University Press, 1983 [12] P. T. Theodossiou, Alternative Models for Assessing the Financial Condition of Business in Greece, Journal of Business Finance and Accounting 18 (5), pp , September, 1991 [13] D. N. Gujarati, Basic Econometrics Singapore, 3rd ed. McGraw-Hill Inc., 1998 [14] E.S. Page, Continuous Inspection Schemes, Biometrika, pp , 1954, vol. 41 [15] J. D. Healy, A Note on Multivariate CUSUM Procedures, Technometrics, , (1987). [16] E. K. Laitinen, T. Laitinen, Cash Management Behaviour and Failure Prediction, Journal of Business Finance and Accounting 25 (7 & 8), pp , 1998 [17] J. Kolodner, Case-Based Reasoning, San Mateo, CA: Morgan Kaufmann Publishers, Inc., 1993 [18] L. M. Salchenberger, E. Cinar, N.A. Lash, Neural Networks: A New Tool for Predicting Thrift Failures, Decision Sciences, pp , 1992 [19] P.K Coats, L.F. Fant, Recognizing Financial Distress Patterns using a Neural Network Tool, Financial Management, pp , 1993, vol. 22 [20] K. Shin, Y. Lee, A Genetic Algorithm Application in Bankruptcy Prediction Modelling, Expert Systems with Applications 23 (3) pp , 2002 [21] F.Varetto, Genetic Algorithms Applications in the Analysis of Inslovency Risk, Journal of Banking and Finance, pp , 1998 vol. 22 [22] Z. Pawlak, Rough Sets, International Journal of Information and Computer Sciences, pp , 1982, Vol. 22 [23] W. Ziarko, Variable Precision Rough Set Model, Journal of Computers and Systems Sciences, pp , 1993, vol. 46 [24] H. Theil, On the Use of Information Theory Concepts in the Analysis of Financial Statements, Management science, pp , May, 1969 [25] B. Lev, Decomposition Measures for Financial Analysis, Financial Management, pp , spring, 1973 [26] J. Scott, J, The Probability of Bankruptcy: A Comparison of Empirical Predictions and Theoretic Models, Journal of Banking and Finance, pp , 1981 [27] S. Westgaard, N. Wijst, Default Probabilities in a Corporate Bank Portfolio: A Logistic Model Approach, European Journal of Operational Research, pp , 2001, vol. 135 [28] M. D. Troutt, A. Rai, and A. Zhang, The potential use of DEA for credit applicant acceptance systems, Computers Operations Research, pp , 1996, Vol. 23, No. 4 [29] D. Retzlaff-Roberts, R. Puelz, Classification in automobile insurance using a DEA and discriminant hybrid, Journal of Productivity Analysis, pp , vol. 17 [30] L. M. Seiford, J. Zhu, An acceptance system decision rule with data envelopment analysis, Computers Operations Research, pp , 1998, Vol. 25, No. 4 [31] P. C. Pendharkar, A potential use of data envelopment analysis for the inverse classification problem, Omega, pp , 2002, Vol. 30 [32] A. Cielen, L. Peeters and K. Vanhoof, Bankruptcy prediction using a data envelopment analysis, European Journal of Operational Research, pp , 2004, Vol. 154 [33] J. C. Paradi, M. Asmild, and P.C. Simak, Using DEA and worst practice DEA in credit risk evaluation, Journal of Productivity Analysis, pp , 2004, Vol. 21 [34] C. Kao, S-T Liu, Prediction bank performance with financial forecasts: A case of Taiwan commercial banks, Journal of Banking and Finance, pp , 2004, vol. 28 [35] I. M. Premachandra, G. S. Bhabra, T. Sueyoshi, DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique, European Journal of Operational Research pp , 2009 [36] J.C. Paradi, M. Asmild, P.C. Simak, DEA based analysis of corporate failure, Working Paper, CMTE, Department of Chemical Engineering, University of Toronto, 2001 [37] P. Gallinari, S. Thiria, F. Badran F and F. Fogelman-Soulie, On the relations between discriminant analysis and multilayer perceptrons, Neural Networks, pp , 1991, vol. 4 [38] T. B. Bell, G. S. Ribar and J. R. Verchio, Neural nets versus logistic regression: a comparision of each model s ability to predict commercial bank failures, Proc. of the 1990 Deloitte and Touche/University of Kansas Symposium on Auditing Problems, pp , 1990 [39] Y. Yoon, G. Swales and T. H. Margavio, A comparison of discriminant analysis versus artificial neural networks, J. Opl Res. Soc., pp , 1993 vol. 44 [39] P.J. Werbos, Beyond regression: new tools for prediction and analysis in the behavioural sciences, Harvard 2013, IJARCSSE All Rights Reserved Page 915
7 University, Master s Thesis, 1974 [40] D. E. Rumelhart, G. E. Hinton and R. J. Willians, Learning representations by backpropagating errors, Nature, pp , 1986, vol. 323 [41] A. Charnes, W. W. Cooper and E. Rhodes, Measuring the Efficiency of Decision Making Units, European Journal of Operational Research, pp , 1978, Vol. 2, No. 6 [42] M. J. Farrell, The Measurement of Productive Efficiency, Journal of the Royal Statistical Society, pp , 1957, Vol. 120, No.3 [43] E. Thanassoulis, Setting Achievements Targets for School Children, Education Economics, pp , 1999, Vol. 7, No. 2 [44] D. S.Chang, Y. C. Kuo, A Variable Benchmark Approach for the Two-Group Discriminant Problem, Kluwer Academic Publishers, 2005 [45] Y. C. Kuo, Applying Asymmetric-Stratified Data Envelopment Analysis Model for Bankruptcy Prediction, 40 th International Conference on Computers and Industrial Engineering, , IJARCSSE All Rights Reserved Page 916
A New Approach to Predicting Bankruptcy: Combining DEA and Multi-Layer Perceptron
www.ijcsi.org 71 A New Approach to Predicting Bankruptcy: Combining DEA and Multi-Layer Perceptron Ayan Mukhopadhyay 1,Suman Tiwari 2, Ankit Narsaria 3 and Bhaskar Roy Karmaker 4 1 Cognizant Technology
More informationBritish Journal of Economics, Finance and Management Sciences 37 October 2011, Vol. 2 (1) Hybrid Financial Analysis Model for Predicting Bankruptcy
British Journal of Economics, Finance and Management Sciences 37 Hybrid Financial Analysis Model for Predicting Bankruptcy Gholamreza Jandaghi, Ph.D. Professor, University of Tehran, Iran Reza Tehrani,
More informationBANKRUPTCY AND THE ALTMAN MODELS. CASE OF ALBANIA
BANKRUPTCY AND THE ALTMAN MODELS. CASE OF ALBANIA Eni Numani Department of Finance, Faculty of Economy, University of Tirana, Tirana, Albania eninumani@feut.edu.al Abstract: This paper examines the univariate
More informationPredicting corporate bankruptcy: where we stand?
Predicting corporate bankruptcy: where we stand? M. Adnan Aziz and Humayon A. Dar M. Adnan Aziz is a Doctoral Researcher, and Humayon A. Dar is a Lecturer, both in the Department of Economics at Loughborough
More informationPrediction of Corporate Financial Distress: An Application of the Composite Rule Induction System
The International Journal of Digital Accounting Research Vol. 1, No. 1, pp. 69-85 ISSN: 1577-8517 Prediction of Corporate Financial Distress: An Application of the Composite Rule Induction System Li-Jen
More informationAn Application of Support Vector Machines in Bankruptcy Prediction; Evidence from Iran
World Applied Sciences Journal 17 (6): 710-717, 2012 ISSN 1818-4952 IDOSI Publications, 2012 An Application of Support Vector Machines in Bankruptcy Prediction; Evidence from Iran 1 2 3 Mohsen Moradi,
More informationPredicting Bankruptcy of Manufacturing Firms
Predicting Bankruptcy of Manufacturing Firms Martin Grünberg and Oliver Lukason Abstract This paper aims to create prediction models using logistic regression and neural networks based on the data of Estonian
More informationMaster s Thesis Riku Saastamoinen 2015
Master s Thesis Riku Saastamoinen 2015 LAPPEENRANTA UNIVERSITY OF TECHNOLOGY School of Business Strategic Finance Riku Saastamoinen Predicting bankruptcy of Finnish limited liability companies from historical
More informationPREDICTION FINANCIAL DISTRESS BY USE OF LOGISTIC IN FIRMS ACCEPTED IN TEHRAN STOCK EXCHANGE
PREDICTION FINANCIAL DISTRESS BY USE OF LOGISTIC IN FIRMS ACCEPTED IN TEHRAN STOCK EXCHANGE * Havva Baradaran Attar Moghadas 1 and Elham Salami 2 1 Lecture of Accounting Department of Mashad PNU University,
More informationBankruptcy Prediction Model Using Neural Networks
Bankruptcy Prediction Model Using Neural Networks Xavier Brédart 1 1 Warocqué School of Business and Economics, University of Mons, Mons, Belgium Correspondence: Xavier Brédart, Warocqué School of Business
More informationData quality in Accounting Information Systems
Data quality in Accounting Information Systems Comparing Several Data Mining Techniques Erjon Zoto Department of Statistics and Applied Informatics Faculty of Economy, University of Tirana Tirana, Albania
More informationPredicting Bankruptcy with Robust Logistic Regression
Journal of Data Science 9(2011), 565-584 Predicting Bankruptcy with Robust Logistic Regression Richard P. Hauser and David Booth Kent State University Abstract: Using financial ratio data from 2006 and
More informationApplication of the Z -Score Model with Consideration of Total Assets Volatility in Predicting Corporate Financial Failures from 2000-2010
Application of the Z -Score Model with Consideration of Total Assets Volatility in Predicting Corporate Financial Failures from 2000-2010 June Li University of Wisconsin, River Falls Reza Rahgozar University
More informationMultiple Discriminant Analysis of Corporate Bankruptcy
Multiple Discriminant Analysis of Corporate Bankruptcy In this paper, corporate bankruptcy is analyzed by employing the predictive tool of multiple discriminant analysis. Using several firm-specific metrics
More informationHYBRID PROBABILITY BASED ENSEMBLES FOR BANKRUPTCY PREDICTION
HYBRID PROBABILITY BASED ENSEMBLES FOR BANKRUPTCY PREDICTION Chihli Hung 1, Jing Hong Chen 2, Stefan Wermter 3, 1,2 Department of Management Information Systems, Chung Yuan Christian University, Taiwan
More informationPrediction of Stock Performance Using Analytical Techniques
136 JOURNAL OF EMERGING TECHNOLOGIES IN WEB INTELLIGENCE, VOL. 5, NO. 2, MAY 2013 Prediction of Stock Performance Using Analytical Techniques Carol Hargreaves Institute of Systems Science National University
More informationPREDICTION OF BUSINESS BANKRUPTCY FOR SELECTED INDIAN AIRLINE COMPANIES USING ALTMAN S MODEL
Z IMPACT: International Journal of Research in Business Management (IMPACT: IJRBM) ISSN 2321-886X Vol. 1, Issue 4, Sep 2013, 19-26 Impact Journals PREDICTION OF BUSINESS BANKRUPTCY FOR SELECTED INDIAN
More informationA hybrid financial analysis model for business failure prediction
Available online at www.sciencedirect.com Expert Systems with Applications Expert Systems with Applications 35 (2008) 1034 1040 www.elsevier.com/locate/eswa A hybrid financial analysis model for business
More informationCredit Risk Assessment of POS-Loans in the Big Data Era
Credit Risk Assessment of POS-Loans in the Big Data Era Yiyang Bian 1,2, Shaokun Fan 1, Ryan Liying Ye 1, J. Leon Zhao 1 1 Department of Information Systems, City University of Hong Kong 2 School of Management,
More informationEVALUATION OF FINANCIAL HEALTH OF MMTC OF INDIA: A Z SCORE MODEL
EVALUATION OF FINANCIAL HEALTH OF MMTC OF INDIA: A Z SCORE MODEL Nilanjana Kumari Faculty of Commerce, Banaras Hindu University, Varanasi, U.P., India-2215 Abstract: Financial position of any company can
More informationHybrid Data Envelopment Analysis and Neural Networks for Suppliers Efficiency Prediction and Ranking
1 st International Conference of Recent Trends in Information and Communication Technologies Hybrid Data Envelopment Analysis and Neural Networks for Suppliers Efficiency Prediction and Ranking Mohammadreza
More informationPredicting Corporate Financial Distress: Whither do We Stand?
Predicting Corporate Financial Distress: Whither do We Stand? M. Adnan Aziz, Humayon A. Dar * Department of Economics, Loughborough University, UK Abstract An increasing number of prediction models has
More informationBankruptcy Prediction for Large and Small Firms in Asia: A Comparison of Ohlson and Altman
第 一 卷 第 二 期 民 國 九 十 三 年 十 二 月 1-13 頁 Bankruptcy Prediction for Large and Small Firms in Asia: A Comparison of Ohlson and Altman Surapol Pongsatat Institute of International Studies, Ramkhamhaeng University,
More informationBankruptcy Prediction
Bankruptcy Prediction using Classification and Regression Trees Bachelor Thesis Informatics & Economics Faculty of Economics Erasmus University Rotterdam M.A. Sprengers E-mail: masprengers@hotmail.com
More informationFINANCIAL PERFORMANCE AND BANKRUPTCY ANALYSIS FOR SELECT PARAMEDICAL COMPANIES AN EMPERICAL ANALYSIS MAHESH KUMAR.T ANAND SHANKAR RAJA.
FINANCIAL PERFORMANCE AND BANKRUPTCY ANALYSIS FOR SELECT PARAMEDICAL COMPANIES AN EMPERICAL ANALYSIS MAHESH KUMAR.T ANAND SHANKAR RAJA. M ** Ph.D **Research scholar, School of commerce, Bharathiar University,
More informationBankruptcy Risk Financial Ratios of Manufacturing Firms
Bankruptcy Risk Financial Ratios of Manufacturing Firms KATEŘINA MIČUDOVÁ Department of Economics and Quantitative Methods University of West Bohemia Husova 11, Pilsen CZECH REPUBLIC pitrovak@kem.zcu.cz
More informationPredicting Bankruptcy: Evidence from Israel
International Journal of Business and Management Vol. 5, No. 4; April 10 Predicting cy: Evidence from Israel Shilo Lifschutz Academic Center of Law and Business 26 Ben-Gurion St., Ramat Gan, Israel Tel:
More informationDr. Edward I. Altman Stern School of Business New York University
Dr. Edward I. Altman Stern School of Business New York University Problems With Traditional Financial Ratio Analysis 1 Univariate Technique 1-at-a-time 2 No Bottom Line 3 Subjective Weightings 4 Ambiguous
More informationThe Financial Crisis and the Bankruptcy of Small and Medium Sized-Firms in the Emerging Market
The Financial Crisis and the Bankruptcy of Small and Medium Sized-Firms in the Emerging Market Sung-Chang Jung, Chonnam National University, South Korea Timothy H. Lee, Equifax Decision Solutions, Georgia,
More informationAccurately and Efficiently Measuring Individual Account Credit Risk On Existing Portfolios
Accurately and Efficiently Measuring Individual Account Credit Risk On Existing Portfolios By: Michael Banasiak & By: Daniel Tantum, Ph.D. What Are Statistical Based Behavior Scoring Models And How Are
More informationBankruptcy Prediction for Chinese Firms: Comparing Data Mining Tools With Logit Analysis
Journal of Modern Accounting and Auditing, ISSN 1548-6583 October 2014, Vol. 10, No. 10, 1030-1037 D DAVID PUBLISHING Bankruptcy Prediction for Chinese Firms: Comparing Data Mining Tools With Logit Analysis
More informationProblem Loan Workout and Debit Restructuring for SME s in Egypt
Problem Loan Workout and Debit Restructuring for SME s in Egypt Course Hours: 24 Course Code: 12167 Objectives The principal objectives of this programme are to provide delegates with a developed understanding
More informationUSING LOGIT MODEL TO PREDICT CREDIT SCORE
USING LOGIT MODEL TO PREDICT CREDIT SCORE Taiwo Amoo, Associate Professor of Business Statistics and Operation Management, Brooklyn College, City University of New York, (718) 951-5219, Tamoo@brooklyn.cuny.edu
More informationA Property & Casualty Insurance Predictive Modeling Process in SAS
Paper AA-02-2015 A Property & Casualty Insurance Predictive Modeling Process in SAS 1.0 ABSTRACT Mei Najim, Sedgwick Claim Management Services, Chicago, Illinois Predictive analytics has been developing
More informationLina Warrad. Applied Science University, Amman, Jordan
Journal of Modern Accounting and Auditing, March 2015, Vol. 11, No. 3, 168-174 doi: 10.17265/1548-6583/2015.03.006 D DAVID PUBLISHING The Effect of Net Working Capital on Jordanian Industrial and Energy
More informationCredit Risk Models. August 24 26, 2010
Credit Risk Models August 24 26, 2010 AGENDA 1 st Case Study : Credit Rating Model Borrowers and Factoring (Accounts Receivable Financing) pages 3 10 2 nd Case Study : Credit Scoring Model Automobile Leasing
More informationThe impact of Working Capital Management on Profitability of the Listed Firms in Sri Lanka S. A. Jude Leon
The impact of Working Capital Management on Profitability of the Listed Firms in Sri Lanka S. A. Jude Leon Officer, Union Bank of Colombo PLC, Sri Lanka. Abstract In this research the researcher attempt
More informationEquity forecast: Predicting long term stock price movement using machine learning
Equity forecast: Predicting long term stock price movement using machine learning Nikola Milosevic School of Computer Science, University of Manchester, UK Nikola.milosevic@manchester.ac.uk Abstract Long
More informationTowards applying Data Mining Techniques for Talent Mangement
2009 International Conference on Computer Engineering and Applications IPCSIT vol.2 (2011) (2011) IACSIT Press, Singapore Towards applying Data Mining Techniques for Talent Mangement Hamidah Jantan 1,
More informationArtificial Neural Network, Decision Tree and Statistical Techniques Applied for Designing and Developing E-mail Classifier
International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-1, Issue-6, January 2013 Artificial Neural Network, Decision Tree and Statistical Techniques Applied for Designing
More informationData Envelopment Analysis of Corporate Failure for Non- Manufacturing Firms using a Slacks-Based Model
Data Envelopment Analysis of Corporate Failure for Non Manufacturing Firms using a SlacksBased Model By D Andre Wilson Supervised by Dr. Joseph C. Paradi A thesis submitted in conformity with the requirements
More informationOptimization of technical trading strategies and the profitability in security markets
Economics Letters 59 (1998) 249 254 Optimization of technical trading strategies and the profitability in security markets Ramazan Gençay 1, * University of Windsor, Department of Economics, 401 Sunset,
More informationA HYBRID MODEL FOR BANKRUPTCY PREDICTION USING GENETIC ALGORITHM, FUZZY C-MEANS AND MARS
A HYBRID MODEL FOR BANKRUPTCY PREDICTION USING GENETIC ALGORITHM, FUZZY C-MEANS AND MARS 1 A.Martin 2 V.Gayathri 3 G.Saranya 4 P.Gayathri 5 Dr.Prasanna Venkatesan 1 Research scholar, Dept. of Banking Technology,
More informationHow To Use Neural Networks In Data Mining
International Journal of Electronics and Computer Science Engineering 1449 Available Online at www.ijecse.org ISSN- 2277-1956 Neural Networks in Data Mining Priyanka Gaur Department of Information and
More informationA comparative study of bankruptcy prediction models of Fulmer and Toffler in firms accepted in Tehran Stock Exchange
Journal of Novel Applied Sciences Available online at www.jnasci.org 2013 JNAS Journal-2013-2-10/522-527 ISSN 2322-5149 2013 JNAS A comparative study of bankruptcy prediction models of Fulmer and Toffler
More informationCREDIT RISK ASSESSMENT FOR MORTGAGE LENDING
IMPACT: International Journal of Research in Business Management (IMPACT: IJRBM) ISSN(E): 2321-886X; ISSN(P): 2347-4572 Vol. 3, Issue 4, Apr 2015, 13-18 Impact Journals CREDIT RISK ASSESSMENT FOR MORTGAGE
More informationDiscussion Board Articles Ratio Analysis
Excellence in Financial Management Discussion Board Articles Ratio Analysis Written by: Matt H. Evans, CPA, CMA, CFM All articles can be viewed on the internet at www.exinfm.com/board Ratio Analysis Cash
More informationExtension of break-even analysis for payment default prediction: evidence from small firms
Erkki K. Laitinen (Finland) Extension of break-even analysis for payment default prediction: evidence from small firms Abstract Break-even analysis (BEA) is widely used as a management method to analyze
More informationPrice Prediction of Share Market using Artificial Neural Network (ANN)
Prediction of Share Market using Artificial Neural Network (ANN) Zabir Haider Khan Department of CSE, SUST, Sylhet, Bangladesh Tasnim Sharmin Alin Department of CSE, SUST, Sylhet, Bangladesh Md. Akter
More informationImpact of Feature Selection on the Performance of Wireless Intrusion Detection Systems
2009 International Conference on Computer Engineering and Applications IPCSIT vol.2 (2011) (2011) IACSIT Press, Singapore Impact of Feature Selection on the Performance of ireless Intrusion Detection Systems
More informationCorporate Financial Evaluation and Bankruptcy Prediction Implementing Artificial Intelligence Methods
Corporate Financial Evaluation and Bankruptcy Prediction Implementing Artificial Intelligence Methods LOUKERIS N. (1), MATSATSINIS N. (2) Department of Production Engineering and Management, Technical
More informationNEURAL NETWORKS IN DATA MINING
NEURAL NETWORKS IN DATA MINING 1 DR. YASHPAL SINGH, 2 ALOK SINGH CHAUHAN 1 Reader, Bundelkhand Institute of Engineering & Technology, Jhansi, India 2 Lecturer, United Institute of Management, Allahabad,
More informationPredictive time series analysis of stock prices using neural network classifier
Predictive time series analysis of stock prices using neural network classifier Abhinav Pathak, National Institute of Technology, Karnataka, Surathkal, India abhi.pat93@gmail.com Abstract The work pertains
More informationtheoretical framework, Merton Model, Gambler s Ruin Bankruptcy Prediction: Theoretical Framework Proposal
Bankruptcy Prediction: Theoretical Framework Proposal Thian Cheng Lim BEM department, Xi an Jiaotong-Liverpool University 111 Ren ai Road, Dushu Lake Higher Education Town, Suzhou Industrial Park 215123,
More informationGlobal Review of Business and Economic Research GRBER Vol. 8 No. 2 Autumn 2012 : 237-245. Jian Zhang *
Global Review of Business and Economic Research GRBER Vol. 8 No. 2 Autumn 2012 : 237-245 Jian Zhang * Abstract: This study analyzes the contribution of return on asset (ROA) and financial leverage gain
More informationEFFICIENT DATA PRE-PROCESSING FOR DATA MINING
EFFICIENT DATA PRE-PROCESSING FOR DATA MINING USING NEURAL NETWORKS JothiKumar.R 1, Sivabalan.R.V 2 1 Research scholar, Noorul Islam University, Nagercoil, India Assistant Professor, Adhiparasakthi College
More informationInstitute for Small Business & Entrepreneurship
The bankruptcy determinants of Swedish SMEs Darush Yazdanfar, assistant Professor Social Sciences, Mid Sweden University Regementsgatan 25-27, 831 25, Östersund 831 25 Tel: +46 730 9892800 E-mail: darush.yazdanfar@miun.se
More informationEstimating likelihood of filing a petition for reorganization and bankruptcy: evidence from Finland
LTA 1/12 p. 15 40 Erkki K. Laitinen Estimating likelihood of filing a petition for reorganization and bankruptcy: evidence from Finland ABSTRACT The objective of Finnish Company Reorganization Act (FCRA)
More informationNeural Network Applications in Stock Market Predictions - A Methodology Analysis
Neural Network Applications in Stock Market Predictions - A Methodology Analysis Marijana Zekic, MS University of Josip Juraj Strossmayer in Osijek Faculty of Economics Osijek Gajev trg 7, 31000 Osijek
More informationCorporate Bankruptcy Prediction Using a Logit Model: Evidence from Listed Companies of Iran
World Applied Sciences Journal 17 (9): 1143-1148, 2012 ISSN 1818-4952 IDOSI Publications, 2012 Corporate Bankruptcy Prediction Using a Logit Model: Evidence from Listed Companies of Iran 1 2 Akbar Pourreza
More informationEfficiency in Software Development Projects
Efficiency in Software Development Projects Aneesh Chinubhai Dharmsinh Desai University aneeshchinubhai@gmail.com Abstract A number of different factors are thought to influence the efficiency of the software
More informationSales Forecast for Pickup Truck Parts:
Sales Forecast for Pickup Truck Parts: A Case Study on Brake Rubber Mojtaba Kamranfard University of Semnan Semnan, Iran mojtabakamranfard@gmail.com Kourosh Kiani Amirkabir University of Technology Tehran,
More informationDATA MINING TECHNIQUES AND APPLICATIONS
DATA MINING TECHNIQUES AND APPLICATIONS Mrs. Bharati M. Ramageri, Lecturer Modern Institute of Information Technology and Research, Department of Computer Application, Yamunanagar, Nigdi Pune, Maharashtra,
More informationAmerican International Journal of Research in Science, Technology, Engineering & Mathematics
American International Journal of Research in Science, Technology, Engineering & Mathematics Available online at http://www.iasir.net ISSN (Print): 2328-349, ISSN (Online): 2328-3580, ISSN (CD-ROM): 2328-3629
More informationCorporate Default Analysis in Tunisia Using Credit Scoring Techniques
INTERNATIONAL JOURNAL OF BUSINESS, 15(2), 2010 ISSN: 1083 4346 Corporate Default Analysis in Tunisia Using Credit Scoring Techniques Loredana Ureche-Rangau a and Nadia Ouertani b* a Université de Picardie
More informationARTIFICIAL INTELLIGENCE METHODS IN EARLY MANUFACTURING TIME ESTIMATION
1 ARTIFICIAL INTELLIGENCE METHODS IN EARLY MANUFACTURING TIME ESTIMATION B. Mikó PhD, Z-Form Tool Manufacturing and Application Ltd H-1082. Budapest, Asztalos S. u 4. Tel: (1) 477 1016, e-mail: miko@manuf.bme.hu
More informationEARLY WARNING INDICATOR FOR TURKISH NON-LIFE INSURANCE COMPANIES
EARLY WARNING INDICATOR FOR TURKISH NON-LIFE INSURANCE COMPANIES Dr. A. Sevtap Kestel joint work with Dr. Ahmet Genç (Undersecretary Treasury) Gizem Ocak (Ray Sigorta) Motivation Main concern in all corporations
More informationExtending Factor Models of Equity Risk to Credit Risk and Default Correlation. Dan dibartolomeo Northfield Information Services September 2010
Extending Factor Models of Equity Risk to Credit Risk and Default Correlation Dan dibartolomeo Northfield Information Services September 2010 Goals for this Presentation Illustrate how equity factor risk
More informationFinancial Ratios as Bankruptcy Predictors: The Czech Republic Case
Financial Ratios as Bankruptcy Predictors: The Czech Republic Case MICHAL KARAS, MÁRIA REŽŇÁKOVÁ Department of Finance Brno University of Technology Brno, Kolejní 2906/4 CZECH REPUBLIC karas@fbm.vutbr.cz,
More information30-1. CHAPTER 30 Financial Distress. Multiple Choice Questions: I. DEFINITIONS
CHAPTER 30 Financial Distress Multiple Choice Questions: I. DEFINITIONS FINANCIAL DISTRESS c 1. Financial distress can be best described by which of the following situations in which the firm is forced
More informationFINANCIAL ANALYSIS CS. Sample Reports. version 2008.x.x
FINANCIAL ANALYSIS CS Sample Reports version 2008.x.x TL 19887 (10/14/2008) Copyright Information Text copyright 2004-2008 by Thomson Reuters/Tax & Accounting. All rights reserved. Video display images
More informationPrediction Model for Crude Oil Price Using Artificial Neural Networks
Applied Mathematical Sciences, Vol. 8, 2014, no. 80, 3953-3965 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ams.2014.43193 Prediction Model for Crude Oil Price Using Artificial Neural Networks
More informationForecasting of Indian Rupee (INR) / US Dollar (USD) Currency Exchange Rate Using Artificial Neural Network
Forecasting of Indian Rupee (INR) / US Dollar (USD) Currency Exchange Rate Using Artificial Neural Network Yusuf Perwej 1 and Asif Perwej 2 1 M.Tech, MCA, Department of Computer Science & Information System,
More informationCash Flow in Predicting Financial Distress and Bankruptcy
Cash Flow in Predicting Financial Distress and Bankruptcy OGNJAN ARLOV, SINISA RANKOV, SLOBODAN KOTLICA Faculty of Business Studies University Megatrend Belgrade Goce Delceva 8 SERBIA oarlov@megatrend.edu.rs,
More informationA Multi-level Artificial Neural Network for Residential and Commercial Energy Demand Forecast: Iran Case Study
211 3rd International Conference on Information and Financial Engineering IPEDR vol.12 (211) (211) IACSIT Press, Singapore A Multi-level Artificial Neural Network for Residential and Commercial Energy
More informationWorking Capital, Financing Constraints and Firm Financial Performance in GCC Countries
Information Management and Business Review Vol. 7, No. 3, pp. 59-64, June 2015 (ISSN 2220-3796) Working Capital, Financing Constraints and Firm Financial Performance in GCC Countries Sree Rama Murthy Y
More informationA Comparison of Financial Performance in Investment Banking Sector in Pakistan
International Journal of Business and Social Science Vol. 2 No. 9 [Special Issue - May 211] A Comparison of Financial Performance in Investment ing Sector in Pakistan Ali Raza Hailey College of Commerce,
More informationAnalysis of Financial Statement of Hindustan Petroleum Corporation Ltd.
EUROPEAN ACADEMIC RESEARCH Vol. II, Issue 8/ November 2014 ISSN 2286-4822 www.euacademic.org Impact Factor: 3.1 (UIF) DRJI Value: 5.9 (B+) Analysis of Financial Statement of Hindustan Petroleum Corporation
More informationDATA MINING IN FINANCE AND ACCOUNTING: A REVIEW OF CURRENT RESEARCH TRENDS
DATA MINING IN FINANCE AND ACCOUNTING: A REVIEW OF CURRENT RESEARCH TRENDS Efstathios Kirkos 1 Yannis Manolopoulos 2 1 Department of Accounting Technological Educational Institution of Thessaloniki, Greece
More informationBusiness Analytics and Credit Scoring
Study Unit 5 Business Analytics and Credit Scoring ANL 309 Business Analytics Applications Introduction Process of credit scoring The role of business analytics in credit scoring Methods of logistic regression
More informationDesign call center management system of e-commerce based on BP neural network and multifractal
Available online www.jocpr.com Journal of Chemical and Pharmaceutical Research, 2014, 6(6):951-956 Research Article ISSN : 0975-7384 CODEN(USA) : JCPRC5 Design call center management system of e-commerce
More informationA Big Data Analytical Framework For Portfolio Optimization Abstract. Keywords. 1. Introduction
A Big Data Analytical Framework For Portfolio Optimization Dhanya Jothimani, Ravi Shankar and Surendra S. Yadav Department of Management Studies, Indian Institute of Technology Delhi {dhanya.jothimani,
More informationVariable Selection for Credit Risk Model Using Data Mining Technique
1868 JOURNAL OF COMPUTERS, VOL. 6, NO. 9, SEPTEMBER 2011 Variable Selection for Credit Risk Model Using Data Mining Technique Kuangnan Fang Department of Planning and statistics/xiamen University, Xiamen,
More informationStock Portfolio Selection using Data Mining Approach
IOSR Journal of Engineering (IOSRJEN) e-issn: 2250-3021, p-issn: 2278-8719 Vol. 3, Issue 11 (November. 2013), V1 PP 42-48 Stock Portfolio Selection using Data Mining Approach Carol Anne Hargreaves, Prateek
More informationTHE HYBRID CART-LOGIT MODEL IN CLASSIFICATION AND DATA MINING. Dan Steinberg and N. Scott Cardell
THE HYBID CAT-LOGIT MODEL IN CLASSIFICATION AND DATA MINING Introduction Dan Steinberg and N. Scott Cardell Most data-mining projects involve classification problems assigning objects to classes whether
More informationA study of economic index effects on return on equity in iranian companies
International Research Journal of Applied and Basic Sciences. Vol., 3 (8), 1691-1696, 2012 Available online at http:// www. irjabs.com ISSN 2251-838X 2012 A study of economic index effects on return on
More informationUsing Altman's Model and Current Ratio to Assess the Financial Status of Companies Quoted In the Malaysian Stock Exchange
International Journal of Scientific and Research Publications, Volume, Issue 7, July 0 ISSN 50-353 Using Altman's Model and Current Ratio to Assess the Financial Status of Companies Quoted In the Malaysian
More informationFuzzy Numbers in the Credit Rating of Enterprise Financial Condition
C Review of Quantitative Finance and Accounting, 17: 351 360, 2001 2001 Kluwer Academic Publishers. Manufactured in The Netherlands. Fuzzy Numbers in the Credit Rating of Enterprise Financial Condition
More informationCREDIT RISK MANAGEMENT IN INDIAN COMMERCIAL BANKS
CREDIT RISK MANAGEMENT IN INDIAN COMMERCIAL BANKS MS. ASHA SINGH RESEARCH SCHOLAR, MEWAR UNIVERSITY, CHITTORGARH, RAJASTHAN. ABSTRACT Risk is inherent part of bank s business. Effective risk management
More informationTHE USE OF PREDICTIVE MODELLING TO BOOST DEBT COLLECTION EFFICIENCY
CREDIT SCORING AND CREDIT CONTROL XIII EDINBURGH 28-30 AUGUST 2013 THE USE OF PREDICTIVE MODELLING TO BOOST DEBT COLLECTION EFFICIENCY MARCIN NADOLNY SAS INSTITUTE POLAND Many executives fear that the
More informationPredictive Modeling Techniques in Insurance
Predictive Modeling Techniques in Insurance Tuesday May 5, 2015 JF. Breton Application Engineer 2014 The MathWorks, Inc. 1 Opening Presenter: JF. Breton: 13 years of experience in predictive analytics
More informationReturn on Equity has three ratio components. The three ratios that make up Return on Equity are:
Evaluating Financial Performance Chapter 1 Return on Equity Why Use Ratios? It has been said that you must measure what you expect to manage and accomplish. Without measurement, you have no reference to
More informationImpact of working capital on firms profitability
African Journal of Business Management Vol. 5(27), pp. 11005-11010, 9 November, 2011 Available online at http://www.academicjournals.org/ajbm DOI: 10.5897/AJBM11.326 ISSN 1993-8233 2011 Academic Journals
More informationFinancial-Institutions Management
Solutions 3 Chapter 11: Credit Risk Loan Pricing and Terms 9. County Bank offers one-year loans with a stated rate of 9 percent but requires a compensating balance of 10 percent. What is the true cost
More informationThe Combination Forecasting Model of Auto Sales Based on Seasonal Index and RBF Neural Network
, pp.67-76 http://dx.doi.org/10.14257/ijdta.2016.9.1.06 The Combination Forecasting Model of Auto Sales Based on Seasonal Index and RBF Neural Network Lihua Yang and Baolin Li* School of Economics and
More informationPREDICTIVE ANALYSIS SOFTWARE FOR MODELING THE ALTMAN Z-SCORE FINANCIAL DISTRESS STATUS OF COMPANIES
Annals of the University of Petroşani, Economics, 12(3), 2012, 231-240 231 PREDICTIVE ANALYSIS SOFTWARE FOR MODELING THE ALTMAN Z-SCORE FINANCIAL DISTRESS STATUS OF COMPANIES ILIE RĂSCOLEAN, REMUS DOBRA,
More informationAn Empirical Analysis of Insider Rates vs. Outsider Rates in Bank Lending
An Empirical Analysis of Insider Rates vs. Outsider Rates in Bank Lending Lamont Black* Indiana University Federal Reserve Board of Governors November 2006 ABSTRACT: This paper analyzes empirically the
More informationNeural network models: Foundations and applications to an audit decision problem
Annals of Operations Research 75(1997)291 301 291 Neural network models: Foundations and applications to an audit decision problem Rebecca C. Wu Department of Accounting, College of Management, National
More informationIMPACT OF WORKING CAPITAL MANAGEMENT ON PROFITABILITY
IMPACT OF WORKING CAPITAL MANAGEMENT ON PROFITABILITY Hina Agha, Mba, Mphil Bahria University Karachi Campus, Pakistan Abstract The main purpose of this study is to empirically test the impact of working
More information