International Journal of Technological Exploration and Learning (IJTEL) Volume 2 Issue 2 (April 2013)
Application of BPNN for Bankruptcy Prediction Dr Roli Pradhan Assistant Professor, Department of Humanities, Maulana Azad National Institute of Technology, Bhopal, India Abstract— Bankruptcy prediction is widely studied all over the world as it caused severe impacts on the economy of a nation as it regulates the bank lending decisions and profitability. In the first place the paper throws light on the bankruptcy .in the other part it The Altman Model for prediction of financial bankruptcy has been considered in this work. The backpropagation neural networks been used to forecast the Z Score for the firms. The research work first estimates the internal parameters of the Z Score for a firm from 2001-2008 to the train the BPNN and uses the estimates of the year 2009 and 2010 values for the validation process. Finally it dwells to draw predictions for the period 20112015 and emphasizes the growing role of BPNN application based Z Score computation of financial Bankruptcy.
Keywords- Bankruptcy; Prediction; Neural Network I.
INTRODUCTION
BANKRUPTCY prediction has long been an important and widely studied topic. The main impact of such research is in bank lending. The Banks need to predict the possibility of default of a potential counterparty before they extend a loan. This is to ensure sounder lending decisions thus resulting in significant savings. In this study we focus only on the corporate bankruptcy prediction problem. To get an idea about the potential impact of the bankruptcy prediction problem, we note that the volume of outstanding debt to corporations in any nation. An improvement in default prediction accuracy leads to enormous saving in any sector. There are several benefits like it can help in estimating a fair value of the interest rate of a loan (that reflects the creditworthiness of the counterparty), in accurately assessing the credit risk of bank loan portfolios. The regulators are acknowledging the need and are urging the banks to utilize cutting edge technology to assess the credit risk in their portfolios to engineer future lending transactions, so as to achieve targeted return/risk characteristics. There are two main approaches to loan default/bankruptcy prediction. The first approach, the structural approach, is based on modeling the underlying dynamics of interest rates and firm characteristics and deriving the default probability based on these dynamics. The second approach is the empirical or the statistical approach. Instead of modeling the relationship of default with the characteristics of a firm, this relationship is learned from the data. The focus of this article is on the application of BPNN to forecast the internal parameters of the Z Score and then predict the Z value to comply with the norms of credit in future.
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II. A REVIEW OF BANKRUPTCY PREDICTION MODELS USING NEURAL-NETWORK (NN) APPROACHES Research studies on using NNs for bankruptcy prediction have been continuing from 1990. It can be argued that there are saturation effects in the relationships between the financial ratios and the prediction of default. Currently, several of the major commercial loan default prediction products are based on NNs. Moody’s Public Firm Risk Model [32] is based on NNs as the main technology. Many banks have also developed and are using proprietary NN default prediction models. Vellido et al. survey the use of NNs in business applications. Also, the survey of Wong et al. on NNs in business applications includes some references on the bankruptcy prediction problem. Dimitras et al. provide a survey on the classical empirical approaches. Zhang et al. include in their paper a nice review of existing work on NN bankruptcy prediction. The majority of the NN approaches to default prediction use multilayer networks. One of the first studies to apply NNs to the bankruptcy prediction problem was the work by Odom and Sharda. Odom and Sharda used Altman’s financial ratios (described above) as inputs to the NN and applied their method, as well as MDA as a comparison, to a number of bankrupt and solvent US firms, where the data used for the bankrupt firms are from the last financial statement before declaring bankruptcy. They considered 128 firms and performed several experiments where they varied the proportion of bankrupt/healthy firms in the training set. The NN achieved a Type I correct classification accuracy in the range of 77.8% to 81.5% (depending on the training setup), and a Type II accuracy in the range of 78.6% to 85.7%. The corresponding results for MDA were in the range of 59.3% to 70.4% for Type I accuracy and in the range of 78.6% to 85.7% for Type II accuracy. Tam and Kiang considered the problem of bank failure prediction. They compared between several methods: MDA, LR, K-nearest neighbor (KNN), ID3 (a decision tree classification algorithm), single-layer network, and multilayer network. For the case of one-year-ahead, the multilayer network was the best, while for the case of twoyear-ahead, LR was the best. When they used a leave-one-out procedure instead of a hold-out sample, the multilayer network was the clear winner (for both forecast horizons). KNN and ID3 were almost always inferior to the other methods. Salchenberger et al. considered the problem of predicting thrift failures. They compared NN with LR. The NN significantly outperformed the LR. For example for 18-months ahead prediction the LR achieves 83.3–85.4% accuracy (depending on some threshold), whereas the NN achieves 91.7%. Coats and Fant compared between NN and MDA. They obtained a
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International Journal of Technological Exploration and Learning (IJTEL) Volume 2 Issue 2 (April 2013) classification accuracy in the range of 81.9% to 95.0% for the NN (depending on the horizon: from three-years ahead to less than a year-ahead), and in the range of 83.7% to 87.9% for the MDA (also depending on the horizon). Kerling and Poddig compared NN with MDA for a database of French firms for a three-year-ahead forecast. The NN achieved prediction accuracy in the range of 85.3–87.7% compared to 85.7% for MDA. Kerling tested several cross-validation procedures and early-stopping procedures in a follow-through study .Altman et al. applied NN and MDA to a large database of 1000 Italian firms for one-year ahead prediction. The comparison yielded no decisive winner, though MDA was slightly better. Boritz and Kennedy compared between a number of techniques, including different NN training procedures, LR and MDA, using the indicators chosen by Altman, and those chosen by Ohlman. The results of the comparison are inconclusive. Fernandez and Olmeda compared NN with MDA, LR, MARS and C4.5 (two well known methods that are based on the CART decision tree algorithm) on Spanish banks (no horizon is specified). The NN obtained 82.4% accuracy compared with 61.8–79.4% for the competing techniques. Alici used principal component analysis and self-organizing maps for the input selection phase, together with a skeletonization step for the NN. He achieved accuracy in the range of 69.5% to 73.7% (depending on some parameter variation), compared with 65.6% for MDA and 66.0% for LR for a database of UK firms (no horizon is mentioned). Leshno and Spector used a novel NN architectures containing cross-terms and cosine terms and achieved prediction accuracy for the two-years-ahead case in the range of 74.2–76.4% (depending on the order of the network), compared with 72% for the linear perceptron network. Lee et al. propose hybrid models. Specifically, they tested combinations of the models MDA, ID3, self-organizing maps and NN. They applied their study to the problem of default prediction of Korean firms. Back et al. propose the use of genetic algorithms for input selection, to be used in conjunction with multilayer networks. They applied their method to data covering the periods one to three years before the bankruptcy, where it obtains significant improvement over MDA and LR. Kiviluoto use self-organizing maps on an extensive database of Finnish firms (horizon is not specified), and show that it obtains comparable results to MDA and learning vector quantization (in the range from 81% to 86%). Kaski et al. (this issue) developed a novel self-organizing map procedure based on the Fisher metric and applied it also to a number of Finnish firms. Zhang et al. compared between NN and LR and employed a five-fold cross-validation procedure, on a sample of manufacturing firms (horizion is not specified). They used Altman’s five financial ratios plus the ratio current assets/current liabilities as inputs to the NN. The NN significantly outperformed LR with accuracy of 88.2% versus 78.6%.Piramuthu et al. developed a technique to construct symbolic features, to be input for to a multilayer network. They applied their technique to a collection of Belgian firms (no forecast horizon is mentioned), where they obtained an accuracy of 82.9% versus 76.1% for the nontransformed input case. They applied it also to a problem of one- and two-year ahead default prediction for US banks. They get superior results and significantly outperform the nontransformed input case. Piramuthu applies a similar input selection technique in conjunction with decision tree classifiers. Martinelli et al. compared between two decision tree algorithms, C4.5 and CN2, and NN on a database of Brazilian firms. C4.5 outperforms the other methods. Yang et al. used probabilistic NNs (PNNs), which essentially implement the Bayes IJTEL || ISSN:2319-2135
classification rule. They tested it on firms in the oil sector. The results were mixed: PNN tied with the multilayer networks, but with a particular preprocessing step MDA was the best. McKee and Greenstein developed a method based on decision trees and applied it to a number of US firms for one year ahead forecast. Their method obtains better results than NN and MDA for Type II error, but worse results for Type I error. Fan and Palaniswami propose the use of support vector machines (SVMs) for predicting bankruptcies among Australian firms, and compared it with NN, MDA and learning vector quantization (LVQ). III.
MODEL DESIGN AND METHODOLOGY
In this paper, a two step methodology has been adopted. The part A provides the steps formulated for the prediction of internal parameters of Z Score, followed by part B which enlists the steps followed for the prediction of Z Score using artificial neural networks. A. Part A: Formulation of Internal Parameters of Z Score The basic ratios are formulated from details mentioned in published statements like balance sheet, cash flow statements, yearly details of banks, profit and loss statements obtained from CMIE database, Reserve Bank of India. Data is also taken from the official websites of the banks and financial institutions and the internet. Prior researches have identified financial ratio for bankruptcy prediction and the usefulness of these financial ratios for bankruptcy prediction can be known from the literature survey. Consequently this research work uses financial data i.e. published time series data for the last 11 years from 2000 to 2009. a) b) c) d)
(Current Assets-Current Liabilities )/Total Assets Retained Earnings/ Total Assets. EBIT/ Total Assets Equity/Total Liabilities
B. Part B: Prediction of Z Score Internal Parameters using BPNN a) b) c) d) e) f) IV.
Catering to Neural Network inputs Tolerance level Minimization Data convergence using Neural Networks Formulation of Absolute error Prediction of ratios in each Ratios pillar Data Validation
BPNN MODEL APPLICATION FOR HDFC BANK
HDFC Bank is India’s second largest private commercial bank with 2.2% market share in terms of total banking sector assets. The analysis will support the Bank in augmenting its prospective lending to expand its share in the market. This would also help to regulate phenomena like housing bubble in the Indian market. HDFC Bank is a domestic AAA rated institution and has the strong backing of its parent - Housing Development Finance Corporation (HDFC) – a housing finance institution that IFC helped founded in 1978. HDFC Bank is listed on the Mumbai and National Stock Exchanges, as well as on the NY Stock exchange. It has built up a formidable brand name and franchise in a relatively short span of time and has outperformed most other banks, private sector or government-owned, by a significant margin. The basic input sheets for all the eight pillars are formulated for HDFC Bank. The process of ratio pillar formulation uses the book formulae for computation of the 90 || www.ijtel.org
International Journal of Technological Exploration and Learning (IJTEL) Volume 2 Issue 2 (April 2013) ratios in each pillar, which will further be used as input parameters for Artificial Neural Network. The Altman Z-Score prediction uses the Neural Network (1- 5- 4).The number if input rows are 1. The hidden layers are 5 and the outcomes are 4 internal parameters. The input point is time and output has been the required ratios. The period for input has been from 2000-2006 which has been normalized from 1 to 8.The details of the ratios and the values are enlisted in the Table 1. Backpropogation Neural Network has been used to transfer data sets. Trained network is used for prediction of ratios for the forthcoming two years being 2008, 2009 and 2010. The initial weights of the neural paths were in the range of -0.02 to 0.05. Convergence study of neural network was carried out for difference tolerance error of 1, 0.75, 0.5, 0.4, 0.3, 0.2, 0.1, 0.01, 0.001. The predicted values obtained from the neural network were compared with the actual field data or the arithmetic computation done from the published statements. The convergence study is detailed in Table 2. TRAINING PATTERN FOR HDFC BANK INTERNAL PARAMETERS OF Z-SCORE
(CA-CL) /Total Assets 0.95057 0.95779 0.91201 0.89952 0.89667 0.80316 0.87101
% Error 0.08 0.06
0.88
0.07 0.04
0.85
0.09 0.06
0.01
1.94 46
8.57
0.07 0.04
Actual 0.07 0.07
0.93
18.92 17.22
% Error
0.06 0.05
0.05 6.4
-46 10
0.05
0.06 6.77
6.1
0.03 6.8
0.03
26.73 3.48
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0.7
0.04 6.25
Z Value
Toler ance
Years
0.01
PREDICTION OF INTERNAL PARAMETERS OF Z-SCORE USING BPNN.
2009
(Current Assets – Current Liability) / Total Assets 0.791113
Output Retained Earnings earnings/ Before Total Interest Assets and Tax / Total Assets 0.069513 0.063035
0.002193
2010 2011 2012 2013 2014 2015
0.778543 0.770487 0.765746 0.763036 0.761463 0.760497
0.069634 0.069681 0.069701 0.069711 0.069715 0.069717
0.001946 0.001816 0.001747 0.001711 0.001692 0.001681
0.065842 0.067364 0.068132 0.068492 0.068638 0.068674
Equity/ Total Liability
OBSERVATIONS:
The validation was carried out for all the internal parameters of Z-Score value. The Z-Score internal parameter estimates were considered from 2001 to 2008 were applied to train the backpropagation neural network and subsequently estimates of the year 2008 to 2010 the data values were used for validation. Based on these values predictions were drawn using BPNN from 2011 to 2015.these values have then been substitutes in the Z-Score formula for market credits to compute the Z-Score values from 2008 to 2015. The market has witnessed several ups and downs during the period 2005 and 2010 and the modeled BPNN has been able to closely predict the Z-Score values from 2005 to 2010. The trained BPNN has been able to forecast the Z-Score values in approximation to the actual values suggesting that the BPNN has the ability to forecast the Z-Score parameters financial ratios. These values are provided by Table 4. TABLE IV.
0.05
Equity/ Total Liability
2.26
0.88
Retained Earnings/Total Assets EBIT/ Total Assets
0.86
(CA-CL)/ Total Assets
0.08
Predicted
2010
0.06
Actual
2009
6.4
Tolerance 0.01
Ratios
TABLE III.
VI.
Z-SCORE CONVERGENCE STUDY FOR HDFC BANK
2008
BPNN MODELING ANALYSIS, RESULTS AND OUTCOMES
After the computation of the basic ratio pillars, as suggested by Table 1, this section uses the ratios in each pillar as inputs to train the network. The network after training computes the values of the ratios from 2009 upto the year 2015 at different tolerance level. The validation is done by the values obtained for the year 2009 and 2010.The tolerance level that provides the closest values is considered for prediction. A 1-6-5 size backpropagation neural network is used for prediction of the ZScore internal parameters. The internal parameters are than used in the formula to find the Z-Score value for the banks upto the year 2015. The tolerance level is 0.01. It took the network 1445578 epochs to converge. Table 3 provides details of the percentage error at the adopted level of tolerance.
0.14826 0.35887 0.15714 0.14487 0.14229 0.09647 0.06175
Predicted
TABLE II.
Equity/ Total Liabilities
Actual
1 2 3 4 5 6 7
0.86
1 2 3 4 5 6 7
Input Parameters Retained EBIT/ Earnings Total / Total Assets Assets 0.01574 0.08317 0.02942 0.08302 0.03507 0.07360 0.03746 0.06007 0.05655 0.04495 0.06411 0.06284 0.04247 0.05446
% Error
Time
S.No
Input
Predicted
TABLE I.
V.
PREDICTED VALUES OF Z SCORE USING BPNN
Year
2009
2010
2011
2012
2013
2014
2015
HDFC
6.229
6.320
6.473
6.571
6.269
6.168
6.136
For HDFC bank the movement of Z-Score has been from 0.4% to 5%. The trend exhibited by the predicted value is from 0.5% to 4.2%. (Figure No:1)
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International Journal of Technological Exploration and Learning (IJTEL) Volume 2 Issue 2 (April 2013) [2]
Z-Score
6.7 6.6
Series1 prediction
6.5 6.4 6.3 6.2 6.1 6 5.9 5.8 2000
[3]
[4]
2001
2002
2003
2004
2005
2006
2007 2008
2009
2010
2011
2012
2013
2014
2015
[5]
Year
[6] Figure 1. Z Score for HDFC Bank.
A company’s total asset consists of current assets and long term assets. The total asset gives some indication of the size of the firm. Therefore it is frequently used as a normalizing factor. The current assets can or will typically be turned into money fairly fast. The firm’s liability consists of current liabilities and long term debt. The current liabilities include short term loans (less than one year due), accounts payable, taxes due, etc. The working capital is current assets minus the current liabilities. It is an indication of the ability of the firm to pay its short term obligations. If it is too negative, the company might default on some payments. The firm’s total assets is financed by a) the total liabilities and b) the shareholders’ equity .The shareholders’ equity consists of the capital raised in share offerings and the retained earnings. The retained earnings means the accumulation of the firm’s earnings since the firm’s inception. The shareholders’ equity is also called the book value of the firm. Even though it is based on the historical costs (plus adjustments through depreciation /amortization) of the firm’s assets and liabilities, rather than market values, it has been a very useful indicator in assessing the financial health of a firm. Retained earnings are a related and similarly useful indicator. The firm’s earnings is also an important indicator. Highly negative earnings indicate that the firm is losing its competitiveness and that jeopardizes its survival. Another related, widely used indicator is the cash flow. It is less prone than earnings to management manipulation. In addition, it measures directly the ability of the firm to generate cash to retire debt. The rationale behind Altman’s fourth indicator is the following. The firm can issue and sell new shares in the market to repay its debt. A large market capitalization (relative to the total debt) indicates a high capacity to perform that. Finally, the firm’s sales are an indication of the health of its business. However, this indicator is probably the least effective among the five Altman indicators, because sales to total assets can vary a lot from industry to industry.
[7]
VII. SUMMARY AND CONCLUSION
[21]
In this article we reviewed the problem of bankruptcy prediction using NNs. We have proposed novel inputs extracted from the financial markets. As can be seen from the results, the new indicators improve the prediction considerably. This is especially true for long horizon forecast. This can be explained by the tendency of the equity markets to be highly predictive, not only of the health of a firm, but also of the health of the economy, which in turn affects the creditworthiness of the firm. REFERENCES [1]
A. Fan and M. Palaniswami, “A new approach to corporate loan default prediction from financial statements,” in Proc. Computational Finance/Forecasting Financial Markets Conf. CF/FFM-2000, London (CD), UK, May 2000.
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