Stanislav Naumenko
Master, Institute of Business Education,
Kyiv National Economic University
named after Vadym Hetman
VALUATION OF THE BANK
Valuing banks and investment banks has always been difficult, but the market crisis of 2008 has elevated the concern to the top of the list of valuation issues. The problems with valuing financial service firm stem from two key characteristics. The first is that the cash flows to a financial service firm cannot be easily estimated, since items like capital expenditures, working capital and debt are not clearly defined. The second is that most financial service firms operate under a regulatory framework that governs how they are capitalized, where they invest and how fast they can grow. Changes in the regulatory environment can create large shifts in value. [1]
There are several main methods to valuate the bank. Some of them are:
- Asset Based approach, where there is a requirement to value the loan portfolio of the bank and subtract the outstanding debt to estimate the value of equity. [2]
- Market Approach, where the value of the equity can be estimated using the market capitalization. [2]
- Income approach focuses on the conversion of expected future economic benefits into their present value. The common free cash flow on equity method is highly valid for bank valuation because it reflects the fact that banks can create value from the liability side of the balance sheet. [2]
- Contingent claim valution that is based on more advanced mathematical appliance.
The Black-Scholes model is appopriate for vlauation of companies, which assets and liabilities measures are comparable by significance. The model is feasible for usage in bank valuation, since operations on both assets and liabilities are singificant for the banking business structure. [3]
In this research paper, the author proposes to use the income approach where forecasts are based on more advanced mathematical techniques. The idea is to estimate the future cash flow using the dynamic balance sheet model with the operational and liquidity risks.
The fact that banks’ balance sheets tend to change dynamically may be particularly for financial analysts and banking regulators. A usual static balance sheet assmution may be valid only in some very special cases of shocks of the low magnitude and in relatively short periods. There are at least foyur policy relevant aspects of the relationship between investment strategies and funding conditions. There are : macro-prudentail policy analysis dynamic solvency conditions, credit supply conditions, effectiveness of luquidity management. [5]
The complexity of the balance sheet management is related to the fact that is a multi-criteria problem with goals changing in time depeding on the liquidity and solvency outlook. The main goal is that Banks try to maximise their profits but also have to build adequate buffers against possible fluctuations of their funding, especially given the high leverage of the bank’s business model. [6]
Furthremore, the Monte Carlo method was implemented in the dynamic balance sheet model. The situation was simulated approximately 1m times and depth of the simulation is 10 years. Macro economic fluctuations and international economic fluctuations were modelled using the stochastic methods. The correlations between macroeconomical factors and the banks balance sheet and results were modelled using net neural techniques and artificial intelligence. [7]
The implementation of the model is comprehensive in how it treats the return-vs-risk trade-off and regulatory aspects of the banking system and helps to predict more precisely the future economic benefits of the bank that can be converse to the present value. Moreover, it is easy to solve by the Monte Carlo simulations and the complex, VaR-based risk constraints are reduced to the closed-form, analytical formulas. Futhermore, it can be used to analyse potential strategic responses of banks to macro-prudential capital buffers, portfolio specific risk weights or liquidity requirements.
References
1. Damodaran, A. Investment Valuation: Tools and Techniques for Determining the Value of Any Asset. New York : John Wiley & Sons, Inc., 2002. 992 p.
2. Horvatova, E. Method of banks valuation. In Economic Analysis. 2009, Vol. 42,
No. 1, pp. 50–60.
3. Meron, R. C. Theory of rational option pricing. In Bell Journal of Economics and
Management Science. 1973, Vol. 4, No. 1, pp. 141–183.
4. Dermine, J. (2009) Bank Valuation and Value-Based Management. McGraw-Hill, New York.
5. K. Aoki and N. Sudo. Bank’s regulation, asset portfolio choice of banks, and macroeconomic dynamics. CARF F-Series CARF-F-323, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo, July 2013.
6. J. R. Birge and P. J´udice. Long-term bank balance sheet management: Estimation and simulation of risk-factors. Journal of Banking & Finance, 37(12):4711 – 4720, 2013. 7. G. Consigli and M.A.H. Dempster. Dynamic stochastic programmingfor asset-liability management. Annals of Operations Research, 81(0):131–162, 1998.
