R.B.I. periodically publishes its research papers titled “Occasional papers” every year. On March 17, 2020, it published one under the title ‘Can financial markets predict banking distress: evidence from India’ which was undertaken by Dr. Snehal S. Herwadkar, Director and Mr. Bhanu Pratap, Manager in the Department of Economic and Policy Research, Reserve Bank of India (RBI), Mumbai. 26 pages of a research paper are available under the following RBI web:
https://rbidocs.rbi.org.in/rdocs/Content/PDFs/03AR_170320205377CC2ED6AC46B594BD070DB67DA22E.PDF
With the financial markets showing lot of variation during these days, the above article may be the most appropriate one to study and understand its implications.
Now, let us discuss the research paper in details.
Preamble
“In this paper, we test whether the efficient market hypothesis works in the context of the Indian banking sector. In particular, using a panel dataset of 39 publicly listed banks in India for 2009–2017, we test whether equity markets provide any lead information about stress in the banking system before quarterly data becomes available to the supervisors”.
An interesting hypothesis that the share value of “Yes Bank” which nosedived to low double-digit from very high three-digit figures would have indicated banking stress in the bank. Well, the normal investor could not arrive at this conclusion, even though the study of RBI officials, some of the best bankers in the country, could have helped.
The researchers took both the Indian situation as well as the internationally available research materials on similar lines for comparison. I am constrained to quote from the main study, the actual approach of the scholars.
“While research in the international arena has used non-performing assets (NPAs) as an indicator of banking stress, we have used the stressed assets ratio – which combines gross NPAs with restructured assets – as a more realistic depiction of stress in the Indian banking sector.
—— in recognition of the peculiar structure of the Indian banking sector – where public sector banks have implicit state guarantees and have also borne larger part of the recent stress – this paper extends the analysis by dividing banks on the basis of their ownership.
The aim of this exercise is to examine whether markets differentiate public sector banks from their private counterparts while pricing risk. —— while we present a panel fixed effect model using several alternative specifications, the analysis is complemented with a random forest model which serves as effective cross-validation without any a priori specification”.
If you are serious to learn the actual formulae used and further descriptions, I recommend Section III, Data, Methodology and Results as your domain for deeper understanding of high level mathematical/statistical analysis. But I am unable to learn much from an advanced level.
But for a common man, let me continue.
The following data were compiled from quarterly returns of supervisory returns of RBI as mentioned on page 19 of the study with additional information.
Appendix B, Data description and source (Page 19)
Variable Description
Stressed (Restructured Standard Advances + Gross Non-Performing Advances) /Total gross adv
Assets Ratio (%)
Assets (INR Billions) Total assets of the Bank
Current Deposits Ratio (%) Total current deposits/Total assets
Time Deposits Ratio (%) Total term deposits/Total assets
Savings Deposits Ratio (%) Total savings deposits/ Total assets
CASA Ratio (%) (Total Current Deposits+ Total Savings Deposits)/Total assets
Capital & Reserves (%) Total Capital & Reserves / Total Assets
Liquid Assets Ratio (%) Total Liquid Assets / Total Assets
Total CRAR (%) Total Regulatory Capital / Risk-Weighted Total Assets of Bank
Tier 1- Capital ratio Total Tier 1 Regulatory Capital/ Risk-Weighted Total Assets of Bank
Tier 2- Capital ratio Total Tier 2 Regulatory Capital/ Risk-Weighted Total Assets of Bank
Return on Assets (%) Total Net Profits / Average Total Assets
Return on Equity (%) Total Net Profits / Average Total Shareholders’ Equity for the Bank
Net Interest Margin (%) Net Interest Income / Average Total Assets
VIX NSE VIX Index
Excess Return over NSE Bank (%)*
For* next Column was as under:
∆%Q-o-Q(Pricei,q) – ∆%Q-o-Q(Price,niftybank,q);
where Pricei,q is the stock price value of ith Bank in quarter ‘q’
and Priceniftybank,q is the stock price value of NSE Bank Index in quarter ‘q’
For the following, Bloomsburg quarterly returns helped the scholars.
1 Excess Return over NSE Bank (%):
∆%Q-o-Q(Pricei,q) – ∆%Q-o-Q(PriceNIFTY,q);
where Pricei,q is the stock price value of ith Bank in quarter ‘q’
and PriceNIFTY,q is the stock value of NSE NIFTY Index in quarter ‘q’
2. Price-to-Book Ratio
Market price per share / Book value per share;
where book value per share is equal to (total assets – total liabilities) / number of shares outstanding.
3. Market Capitalization
(Current market price per share) x (Total number of shares outstanding);
4. 90-d Price Volatility
The standard deviation of daily logarithmic price changes for the 90 most recent trading days closing price.
Appendix C Summary Statistics contained information on following heads for all scheduled commercial banks, public sector banks, and private sector banks separately for study.
- Variable
- Mean Std. Dev. Min. Max.
- Stressed Assets Ratio (%)
- Assets (INR billions)
- Total CRAR (%)
- Return on Equity (%)
- Return over NSE Bank (%)
- Price-to-Book Ratio
- Market Capitalization (INR billions)
- 90-d Stock Volatility
Based on the information obtained and contributing them to the following tests, the authors arrived at their conclusions.
- Joint Wald Test of Significance for Inclusion of Time-Fixed Effects
- Modified Wald Test for Group-wise Heteroscedasticity in Fixed Effect
- Wooldridge Test for Autocorrelation in Panel Data
- Pesaran’s Test of Cross-sectional Independence.
Let us venture to get some main feedbacks from the extensive study undertaken by RBI scholars.
- The results of the study of separate segments of banking, both private and public sector banking was intentional:
- For the public sector banks, asset base and CRAR showed inverse association with SAR as expected. However, the coefficient of return on equity (RoE) was not statistically significant.
- Regarding the predictive power of equity markets, none of the equity market variables were found to have a statistically significant relationship with SAR, even at 10 percent level of significance. The only exception to this was the two-quarter lagged observed volatility in the bank stock price, which might signal trading activity on a bank stock on account of policy announcements such as recapitalization. With the coffers of the central government always available for replenishment at no notice, public sector banks drove on the assumption that performance has no correlation with their share value.
- The situation is so bad that barring SBI, no other bank- share carries any consideration of any worthy investor recommendations or discussion.
- One can look at nearly Rs 800 crores fraud noticed in one of the largest public sector banks in all the channels or newspapers yesterday, and notice its share value has been as dumb as possible. People take it for granted that any public sector bank is liable for distress at any time but its standing on the shoulders of the central government obviates the pain.
Let me quote the authors blunt message as under:
“The results of the public and private sector banks indicate that the markets differentiate their anticipation of stress based on ownership pattern. Acquisition, verification, and pricing of information is costly. For public sector banks, which have implicit state guarantees, these costs seem to outweigh the benefits. In particular, if the investors are confident that the stress on a public sector bank – however, grave it maybe – would be relieved by the government through various means such as recapitalization, then the market has little incentive to price-in the stress.
Stressed assets affect the bank’s balance sheet because they involve higher provisioning and reduce the lendable resources available with banks. However, if the government stands ready to recapitalize the banks then the stress on their balance sheet is relieved automatically. Factoring in these considerations, markets may be providing meaningful information about the impending stress in case of private sector banks but not in the case of public sector banks.”
Conclusion
The purpose of this article is to draw the attention of readers to some serious study undertaken by our own top scholars from RBI, considered as among the best in the world among central banks. This study, an offshoot of an obscure observation, as it may seem for a novice, reveals a sharp observation that the distressed banks do exhibit their signals by variation in share values. For a simple shareholder like myself, yourselves, or any foreign investor, it is mind-boggling that one has to revise his compass of assessing the investments in Indian banks with careful calculations and reassess whether it is worth at any point of time. The assumption too helps one to invest at the deepest distressed level if the history of long-standing service of the bank can also be factored into the calculation. Can a long-standing cultural icon like Punjab National Bank or Bank of Baroda emit signals for investment, divestment or reinvestment based on their distressed share values?
Do private sector banks like Yes Bank, Axis Bank, HDFC Bank or other foreign banks working in India do fall under the above assumptions done by me?
Yes, I want you to spare your professional time to study the RBI research paper which has quoted 20 research papers, Appendix A, B, C, D, D- 5, D- 6, D- 7 and D- 8 as the results of their senior officer’s arduous tasks extended over hundreds of hours of billable time. Bravo!
Reference: www.rbi.org.in
Author Bio
