PSU Bank Modeling- A comparative modeling ...

Asian Research Consortium Asian Journal of Research in Business Economics and Management Vol. 6, No. 6, June 2016, pp. 27-36

Asian Journal of Research in Business Economics and Management

ISSN 2249-7307 www.aijsh.com A Journal Indexed in Indian Citation Index DOI NUMBER: 10.5958/2249-7307.2016.00037.2

PSU Bank Modeling- A comparative modeling approach involving Artificial Neural Network and Panel Data Regression Dr. Bikramaditya Ghosh Prof. MC Krishna Prof. T S Ramachandran

Abstract Indian Baking, especially the PSU segment has been facing the fire for quite some time now. Be it NPA, or be it digitization or even be it cash management, they are finding it hard to cope up with the changes. ‘Earnings per Share’ (EPS) is a cardinal parameter which will impact the performance of the bank’s share in the bourses. So, in this study the banks of PSU segment that are included in the defined universe of S&P BSE Bankex have been under consideration (namely SBI, BOB and PNB). Determination of EPS with high degree of accuracy will help these banks to attract FPI flow consistently which in turn will be beneficial for their regular cash inflow and thus ease their liquidity crunch. Two distinctintly different methods such as Panel Data Regression (Econometric Method) and Artificial Neural Network (Machine Learning Tool) have in in action to set up an accurate model, with which the EPS prediction could be quite accurate in nature. Thirteen control variables are chosen carefully to construct both the models. Interestingly both the models are depicting similar picture (with accuracy measures) with different combinations.

Keywords: PSU Bank, Artificial Neural Network, Panel Data Regression, Predictive Model JEL Code : C31,D53,C45 ________________________________________________________________________________

1. Introduction The Indian banking industry has been most profoundly impacted by the digitization, capital regulations and the lackluster conditions of the economy impacting the recovery process of the money lent by banks. All these have impacted the earnings of the Indian banks, both private and public, leading to underperformance of banking stocks in the markets during recent times. Large Non-Performing Assets (NPAs) on the books of the Indian banks are affecting the banks’ earnings directly. NPAs are those amount which have been lent by the banks but not repaid by the borrowers

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Ghosh . (2016). Asian Journal of Research in Business Economics and Management, Vol. 6, No. 6, pp.27-36.

on the scheduled dates (either principal or interest or both) and have remained unpaid for a period as stipulated by the Reserve Bank of India from time to time for various types of borrowers. The NPA figures are staggering for the banking system as a whole. Further, more stringent capital regulations through Basle III norms are making capital all the more precious resulting in an imperative need to protect the earnings and grow them considerably. There are several issues that are coming into play all at once for Indian banks. The digitization is radically changing the payments and settlement systems in which financial considerations are settled between parties, which hitherto was the prerogative of banks. Internet banking and mobile wallets will dismantle the brick and mortar banking structure faster than we thought. How are these impacting the earnings of banks? Several studies have been made in the past. (Bansal and Mohanty 2013) discusses the CAMEL model, which stands for Capital Adequacy, Asset Quality, Management, Earnings and Liquidity, to evaluate the performance of selected banks in India. (Mondal and Ghosh 2012) studied the relationship between Intellectual Capital and Financial performance of Indian banks. A study was also conducted about whether during the global financial crisis of 2008; depositors shifted their money towards public sectors banks (Eichengreen and Gupta 2013). Banks performance gets captured in the net income of the bank. Net income divided by the number of shares provides the ‘earnings per share’ (EPS) for the bank. ‘Earnings per Share’ is an important parameter which will impact the performance of the bank’s share in the markets directly. Given the empirical evidence that price earnings multiple (PE ratio) is stable for banking industry, the earnings per share assumes importance in the performance of banking stocks in markets. Financial inclusion programs of the Government of India (read Prandhan Mantri Jan Dhan Yojana), is increasing the number of customers the banks are servicing, not necessarily the bottom line. Licensing to new category of banks such as Payment banks and Small Finance banks is increasing the competition for the full-fledged Universal banks, taking away the low cost funds available for banks. Peer to Peer lending, Crowd financing are emerging as alternative to the traditional bank lending model. Given all these developments from different aspects, how are banks coping with this and protecting / improving their earnings per share. The following table provides a glimpse of the strain the banks are undergoing in protecting their profitability. Return on assets and Return on equity of SCBs: Bank group-wise (Per cent) Sr. No. Bank Group/Year

Return on Assets

Return on Equity

1

2 201213

3 201314

4 201415

5 201213

6 201314

7 201415

1

Public sector banks

0.8

0.5

0.46

13.24

8.48

7.76

2

Private sector banks

1.63

1.65

1.68

16.46

16.22

15.74

3

Foreign banks

1.92

1.54

1.87

11.53

9.03

10.25

All SCBs

1.04

0.81

0.81

13.84

10.69

10.42

Notes: Return on Assets = Net profit/Average total assets. Return on Equity = Net profit/Average total equity. Source: Annual accounts of respective banks / RBI 28

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Under these conditions, predicting the earnings of banks is a challenge and we need more advanced models like neural networks, fuzzy logic and genetic algorithms to achieve that by moving away from the traditional models of multiple regressions and time series analysis. As could be seen hereunder, before the end of the financial year 2018 banks need to progressively increase the capital adequacy ratio.

1 2 3 4 5 6 7 8

Classification of Capital

31/03/16

31/03/17

31/03/18

Minimum Common Equity Tier I (CET I) Capital Conservation Buffer (CCB) Min CET 1 + CCB (1 + 2) Additional Tier 1 Capital Minimum Tier 1 Capital (1 + 4) Tier 2 Capital Minimum Total Capital (5 + 6) Minimum Total Capital + CCB (2 + 7)

5.50% 1.25% 6.75% 1.50% 7.00% 2.00% 9.00% 10.25%

5.50% 1.875% 7.38% 1.50% 7.00% 2.00% 9.00% 10.875%

5.50% 2.50% 8.00% 1.50% 7.00% 2.00% 9.00% 11.50%

This would obviously entail additional capital. Several estimates are available as to the amount of additional capital the banks would require to bring in to meet the above norms. One such estimate puts the figure at around Rs.5 Trillion by 2018 to meet the above norms of Basel III, of which Rs.1.75 Trillion in equity capital and Rs.3.25 Trillion in non-equity. Hence keeping a close watch on the earnings is an important task that the bank managements should engage in. Technology has helped banks to device strategies to understand the money savings and spending behaviour of the public and hence bring out innovative products that would cater to all the needs of the public. In the process, the banks aim to retain customers and make gains out of these transactions. Data mining and data analytics tools have helped banks in this direction. Hence to be proactive to providing excellent customer service and meeting customer needs have become hygiene factors rather than a privilege offered to customers. Enjoying these enhanced value added services has only increased the customer expectations and hence banks are expected to do more in this direction to retain customers. It is possible that the unsatisfied customers have accounts with a bank but deal through other banks whose services are better. It may even be late before the banks realise that their, so called, customers do not deal with them, but with some other bank. Hence providing good customer service leveraging on technology, retaining them and make earnings is an essential aspect of banking business. Without this, the earnings of the banks will only suffer. Banks are now considering the development of new service quality policies and strategies that promote customer satisfaction and loyalty (Mahapatra et al. 2015). Summarizing the above the performance of a bank is a function of NPA management, NIM management, Income management and Cost management and Total Assets management and Risk management.

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Objectives of our study To forecast the EPS given the variables those have been taken for study. To ascertain the most important variables that would have an impact on the EPS. To maximize the predictability chances given the variables those have been taken into account.

1. Literature Review From time immemorial, man has been attempting to predict the future outcomes. The Market, Suppliers, Consumers and the Governments are all interested in predicting the future outcome of their businesses. Predicting market price of a stock is one of the most intriguing thing, for an investor and more so for a trader. This study is an attempt to predict the market price of the stocks of Indian Public Sector Banks. In attempting this, the following questions became important.

a) What are the factors that affect the Market price of a stock? Especially, that of the Indian Public Sector Bank. b) What Statistical tools can make the predictions as accurately as possible? Literature review was carried out trying to see if one can get a reasonable answer for these questions. While the EPS are influenced by many a factor, the financial health, the financial performance and the financial returns of a corporation are traditionally held to be key factors that impact the EPS. These have been established by many research works across the globe. We begin with some of the literature, which bring out the factors that affect the returns of corporations. Al-Shubiri, F. N. (2010) in his work has empirically demonstrated that Capital Structure of a corporation impacts the returns. In this study, the author uses descriptive statistical tools like Simple and Multiple Regression to analyses the data from listed companies in Europe. Saramat, O. et al (2013) in their study have used GMM methodological framework, to establish that Financial Ratios of an entity show the Financial Health of the company. This work uses data from corporations in European and US geographies. A study by Motamedi, P. (2013), has found out that financial performance of Iranian Private Banks and the returns that the banks have given, have a direct correlation. This study uses Multiple Regression to arrive at this conclusion. Specifically in business of banking, the efficiencies brought in by the revenue and cost management have a significant impact on the profits and in turn, they impact the performance of their stock. This has been demonstrated, with the help of data, relating to the Central and Eastern European Banks, by Vardar, G. (2013), using Stochastic Frontier Analysis Model and Regression. In their work on the Risk contagion, Li, J. et al (2013), have developed a model, which can determine the impact of inter-bank exposure, its vulnerability and the systemic issues that it could create. This study significantly talks about, the possible impact of a single bank’s exposure to risk, on the entire banking system. To establish this, Transfer entropy-based method has been adopted and data from 16 banks in China have been used as the basis.

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After establishing the influencers of the performance in the Banking companies, the current research work looked at the possibility of predicting or forecasting the future outcomes. Researchers have recommended many a models over time. However, artificial intelligence models have been used in the recent past to predict business outcomes. For this work, following literature was reviewed. Abdullah, L. (2013) has used Distance based Fuzzy Series Model to forecast the exchange rates between different currencies. The study concludes that using the model it is possible to predict the exchange rates between Malaysian Ringgit and US Dollars more accurately than the exchange rates between New Taiwan Dollar and US Dollar. In a study to forecast conditional volatility of stock returns, Arneric, J. et al (2014) have used Standard GARCH (1, 1) model and Jordon Neural Network models on the data relating to Croatian stock market data. The aim of the work is to determine the right kind of Neural Network that can match GARCH (1, 1) model and its applications in accurately forecasting conditional variance of stock returns. The study also brings out the advantages that Neural Network models offer in terms of flexibility of nonparametric methods. Studies have also shown that Neural Network as a tool is quite handy in predicting various business outcomes specifically relevant to Banking industry. Taghva, M. et al (2011), has been able to predict the behavior of bank customers and its impact on the bank performance. Kohonen Neural Network and Output Neuron Matrix have been used to mine the empirical data from Iranian Banks to establish the behavioral pattern of customers. In another study in Iran by Ghodrati, H., & Taghizad, G. (2014) have attempted to predict with higher level of accuracy the credit risk that the banks face. Higher error rate in predicting the credit risk can have negative effect on the bank’s business. The researchers have used Artificial Neural Network with back propagation paradigm on Macroeconomic parameters to predict the credit risk exposure for banks. The conclusion from the study is that Neural Network helps in predicting credit risk exposures with lower error rates. After business predictions, the predicting the market behavior would be the next step. Therefore, factors and the tools required to predict the EPS in the market need to be understood. Following literature provide some guidance in this direction. A study by Das B et al. (2009) uses Correlation Analysis, Regression Analysis, Granger’s Causality Test and Measures of Out-of-Sample Forecast Performance, to predict the movement of Nifty - NSE Index, in India. Wuerges, A. F. E., & Borba, J. A. (2010) study reviews the publications of global relevance between 2000 and 2007. The study highlights that computational intelligence like Neural Network, Fuzzy Logic and Genetic Algorithms are very useful in the areas of Finance and Accounting, as they provide sufficient flexibility for making effective predictions. Vaisla, K. S., & Bhatt, A. K. (2010) in their study have also established the effectiveness of Artificial Neural Network, while predicting the Stock market performance, when applied using sufficient volume of quality data. Here there is a comparison made between the results obtained from other Statistical Techniques like Multiple Regression techniques and Time Series analysis, and Neural Network to establish the accuracy levels of the latter one. This study has established clearly that Neural Network is able to predict the EPS more accurately in comparison with other traditional Statistical Techniques. A comparative analysis of predictions by using Technical Analysis and Neural Network has been made by Sterba, K. H. M. L. J. (2011) in the research work, using data from corporations listed in stock exchanges of PIGS (Portuguese, Ireland, Greece and Spain) countries. The study looks at the financial crisis and the ability of Neural Network to accurately predict the EPS of companies listed in exchange of the 31

Ghosh . (2016). Asian Journal of Research in Business Economics and Management, Vol. 6, No. 6, pp.27-36.

PIGS countries. Application of ‘Three Layered Feed-Forward Neural Network with Back Propagation algorithm’ could not accurately predict the next day price movement. But that could predict the absolute prices with higher degree of accuracy. Also, Neural Network was able to predict the absolute prices before the crisis and technical analysis did better during the prices. The reviews of research work above have provided reasonable assurance that Neural Network models would be able help us in predicting with a greater degree of accuracy, the EPS of Public Sector Banks in India.

2. Research Methodology This study has been focused on constructing predictive model for PSU Banks in S&P BSE Bankex segment with the help of ANN and PDR. Earnings per share (EPS) was measured by using an artificial neural network model (ANN) and re-checked it by a Panel Data Regression model with fixed effects. Time period of data collection was within 2006 to 2015. Since S&P BSE Bankex has only three PSU Banks as representative of the sector (namely SBI, BOB and PNB), so those three banks were taken in to consideration here. Thirteen control variables were chosen carefully for this study of substantial proportion. They were Profit after tax (PAT), Total Assets (TA), Return on Assets (ROA), Non-performing assets (GNPA), Price to Book ratio (PB), Book Value (BV), Graham’s Number for bankruptcy measurement (GRHM), Price earnings ratio(PE), Return on Investments (ROI), Interest Income (II), Interest Expense (IE), Average Assets (AA) and Net Interest Margin (NIM). ANN is applied with these 13 control variables as input variables to the network. So, the network structure is built as [13, 20, 10,1]. The last layer i.e. 1 serves as the deterministic output layer to predict EPS of these banks as a combined set. These neuron layers are totally four in numbers and they work with back-propagation method of errors for error reduction. Output continuously gets trained till the highest level of accuracy has been achieved. In this case RMSE or Root Mean Square Error has been minimized. Training set has been kept at 70% and testing set has been kept at 30% in this study. Training set is where the algorithm runs again and again as back-propagation methodology and minimizes RMSE. Then the predictive model gets overlapped in testing set to judge the accuracy of the model. These ANN models are found to be quite accurate as they replicate the logical functioning of the human brain. The second part of the study has been carried out on the same data set, with the same control variables to determine EPS by using Panel Data Regression (PDR), fixed effects method. As this data set is a balanced panel, that means perfect mixture of cross-sectional and time series data, so PDR is applied. As the time dependent effects such as rate cut or hike , NPA cleaning up from balance sheet etc. are virtually fixed for all the three banks under scrutiny here (SBI, BOB and PNB), so instead of random-effect, fixed effect is a more rational choice.

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3. Study Output Artificial Neural Network Output Table 1

Impact Variable Graham's Number Book Value

Impact on RMSE 198% 100%

RMSE 93.08 48.28

Table 2

Post processed Results Number of Observations Max. Negative Error Max. Positive Error MAE RMSE Residual Sum SD of Residuals Adjusted R Squared Correlation

Model Fit 30 -3.55452 3.035 1.251 1.639 3.694 1.6394 0.998841 0.999441

Model based on ANN Equation 1 EPS = 0.00334099 - N9*0.195624 + N2*1.19559 Table 3 Neural variables are defined as below: N2 = 0.0328272 - N16*0.312976 + N3*1.31264 N3 = 0.68677 - N37*0.0234263 + N5*1.01632 N5 = -0.011322 + N9*0.91074 + N12*0.0893774 N12 = 1.76618 - N27*0.793867 + N24*1.77558 N24 = -4.61042 + N26*0.99182 + N38*0.0559104 N38 = 308.1 - ROI*27.3636 N26 = -36.114 + ROA*5254.72 + GRHM*0.0730436 N27 = -65.2956 + ROA*9757.61 + BV*0.116867 N37 = 37.9748 - TA*0.00014861 + PAT*0.0283462 N9 = -3.43172 + N40*0.067579 + N16*0.967949 N16 = 6.35551 + N31*1.87511 - N39*0.940902 N39 = 19.2455 + BV*0.131775 - AA*1.80755e-05 N31 = 7.4646 + GRHM*0.0803018 - AA*1.16189e-05 N40 = -90.7338 + IE*0.00125803 + ROA*17199.7

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Ghosh . (2016). Asian Journal of Research in Business Economics and Management, Vol. 6, No. 6, pp.27-36.

Panel Data Regression Output Method: Panel Least Square-: Fixed Effect Periods: 10 Cross-Sections:3 Table 4

Variable Intercept PAT TOTAL ASSETS ROA (%) GROSS NPA PB BV PE ROI GRAHAM INTEREST INCOME INTEREST EXPENSE AVERAGE ASSETS NIM (%)

Coefficient -38.6724 -0.00216 7.45E-05 3137.404 1.45E-05 -13.0634 -0.13188 0.909433 -1.25323 0.160292 -0.00021 -0.00068 -1.41E-05 880.4149

Std. Error 23.68517 0.001069 4.87E-05 1596.533 0.000257 12.02805 0.021652 0.877874 0.941135 0.013208 0.000679 0.001072 2.83E-05 758.8827

t-Statistic -1.632767 -2.020019 1.527818 1.965136 0.056555 -1.086078 -6.090902 1.03595 -1.331612 12.13566 -0.314544 -0.636001 -0.49916 1.160146

Prob. 0.1634 0.0994 0.1871 0.1066 0.9571 0.327 0.0017 0.3477 0.2405 0.0001 0.7658 0.5527 0.6389 0.2984

Occurrence 90% 81% 89% 4% 67% 100% 65% 76% 100% 23% 45% 36% 70%

Table 5

R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)

0.999873 0.999261 1.369343 9.375505 -25.1217 1635.634 0

Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat

94.67667 50.38365 3.341447 4.509111 3.714993 2.049124

Model based on PDR Equation 2 EPS = -38.6723687577 - 0.00215845195735*PAT + 7.44536316033e-05*TA + 3137.4039631*ROA + 1.4548531117e-05*GNPA 13.0634025401*PB 0.131878939307*BV + 0.909433468259*PE 1.25322679672*ROI + 0.160292356438*GRHM 0.000213519322672*II 0.000681923353647*IE 1.41165394254e-05*AV Asset + 880.414902331*NIM + [CX=F, PER=F]

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4. Conclusion Conclusive remarks from ANN output (refer Equation 1) ANN output shows that out of 13 control variables or regressors, Graham’s Number for bankruptcy measurement and Book Value were the cardinal parameters for reducing RMSE (error) and making the model robust. Adjusted R Squared is over 99.88% makes it plausible that this model can define EPS of PSU segment (SBI,PNB and BOB) in BSE Bankex 99.88 times out of 100 times. Up to 40 Neural variables came out of the stud , all of which were represented as a combination of various control variables at different specific proportion. So, the neural model is quite efficiently predicting the EPS for the above specified segment under consideration. Conclusive remarks from PDR output (refer Equation 2) PDR confirms that Book Value and Graham’s number for bankruptcy measurement do determine the EPS with 100% occurrence probability. PAT and ROA though are quite close with about 90% occurrence yet they cannot be considered as the confidence is 95% in this PDR. Adjusted R Squared is over 99.92% makes it plausible that this model can define EPS of PSU segment (SBI,PNB and BOB) in BSE Bankex 99.92 times out of 100 times. BV however shares an inverse relationship with EPS, and Graham’s number shares a positive correlation with EPS. F-Stat in ANOVA is more than 1 and probability of F Stat is zero makes the model robust. AIC, SC and HQ coefficients are quite small indicating the accuracy as very high. Durbin-Watson is greater than R Squared means the PDR is valid and it is non-spurious in nature. Both ANN and PDR show the same results and reaffirm the solidity of this study.

5. Further Scope of study Further scope of work is possible on the Private and Foreign banking sectors in India, in fact inside S&P BSE Bankex out of 11 Banks, eight banks are either Foreign or Private in nature. Apart from that Fuzzy logic and ANFIS-ICA methods could make more accurate and robust models in future.

6. Citations [1] Abdullah, L. Performance of Exchange Rate Forecast Using Distance-Based Fuzzy Time Series. Al-Shubiri, F. N. (2010). Capital structure and value firm: an empirical analysis of abnormal returns. Economia. Seria Management, 13(2), 240-253. [2] Arnerić, J., Poklepović, T., & Aljinović, Z. (2014). GARCH based artificial neural networks in forecasting conditional variance of stock returns. Croatian Operational Research Review, 5(2), 329-343. Bansal Mohanty (2013) A Study on Financial Performance of Commercial Banks in India: Application of Camel Model. Al-Barkaat Journal of Finance and Management, Volume 5, Number 2, July 2013, pp. 60-79. [3] Debasish, S. S., & Das, B. (2009). Forecasting Movement of Stock Index–Use Of Spread between E/P Ratio and Interest Rate. Asian Social Science,4(11), 68. [4] Eichengreen. B, Gupta. P, (2013). The financial crisis and Indian banks: Survival of the fittest?, Journal of International Money and Finance, Volume 39, December 2013, Pages 138-152. Ghodrati, H., & Taghizad, G. (2014). Credit risk assessment: Evidence from banking industry. Management Science Letters, 4(8), 1765-1772. 35

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[5] Ghosh and Mondal (2012) "Intellectual capital and financial performance of Indian banks", Journal of Intellectual Capital, Vol. 13 Issue: 4, pp.515 – 530. [6] Li, J., Liang, C., Zhu, X., Sun, X., & Wu, D. (2013). Risk contagion in Chinese banking industry: A Transfer Entropy-based analysis. Entropy, 15(12), 5549-5564. [7] L. Abdullah. (2013). Performance of Exchange Rate Forecast Using Distance-Based Fuzzy Time Series. [8] Mahapatra and Mahapatra (2015), Service Quality of Indian Banks: A Fuzzy inference system approach. Asian Academy of Management Journal, Vol. 20, No. 2, 59–80. [9] Motamedi, P. (2013). Investigating different factors influencing on return of private banks. Management Science Letters, 3(9), 2467-2472. [10] Şărămăt, O., Dima, B., Angyal, C., & Ştefana Maria, D. (2013). FINANCIAL RATIOS AND STOCK PRICES ON DEVELOPED CAPITAL MARKETS. Studia Universitatis Vasile Goldiş, Arad-Seria Ştiinţe Economice, (1), 1-12. Šterba, K. H. M. L. J. THE IMPACT OF FINANCIAL CRISIS ON THE PREDICTABILITY OF THE STOCK MARKETS OF PIGS COUNTRIES–COMPARATIVE STUDY OF PREDICTION ACCURACY OF TECHNICAL ANALYSIS AND NEURAL NETWORKS. [11] Taghva, M., Bamakan, S., & Toufani, S. (2011). A data mining method for service marketing: A case study of banking industry. Management Science Letters, 1(3), 253-262. [12] Vardar, G. (2013). Efficiency and Stock Performance of Banks in Transition Countries: Is There A Relationship?. International Journal of Economics and Financial Issues, 3(2), 355. [13] Vaisla, K. S., & Bhatt, A. K. (2010). An analysis of the performance of artificial neural network technique for stock market forecasting. International Journal on Computer Science and Engineering, 2(6), 2104-2109. [14] Wuerges, A. F. E., & Borba, J. A. (2010). Neural networks, fuzzy logic and genetic algorithms: applications and possibilities in finance and accounting. JISTEM-Journal of Information Systems and Technology Management, 7(1), 163-182.

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