Using Loan Portfolio Management to Increase Profitability, Assess Risk

By Hyndman, Carl | American Banker, December 15, 1997 | Go to article overview
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Using Loan Portfolio Management to Increase Profitability, Assess Risk


Hyndman, Carl, American Banker


A long-time staple in the securities industry, modern portfolio theory is making its way to banking, and the OCC has asked banks to view risk management using a portfolio approach.

Commercial banks have experienced a recent period of robust earnings. These gains are the result of three factors: declining loan losses, increased operating efficiencies and a favorable interest rate environment. There are indications that income gains may be slowing, however. Profits are being squeezed because of intense competition for new business and declining loan spreads. In this environment, bank managers continue to explore new profit opportunities.

One area often overlooked in the effort to boost profitability is the bank's existing loan portfolio. Rather than look for new marketing opportunities, bankers should take Shakespeare's advice and look "not in our stars, but in ourselves." Modern portfolio theory (MPT) and new tools for analyzing risk and return offer opportunities for bankers to improve profitability without incurring additional risks or costs.

Portfolio management is not a new concept in finance. Mutual fund managers and investment advisors have used it for decades, while commercial bankers have been slower to embrace sophisticated portfolio management techniques.

Quantitative portfolio theory emanated from academic research begun in the 1950s that focused on rates of return in the stock market. Using quantitative models and high speed computers, academics from Harry Markowitz to James Tobin to William Sharpe tracked stock prices and their covariances over time. The research of these and other academics culminated in what we now call MPT.

applying mpt to banks

Using volatility of return as a surrogate for risk, these authors launched an assault on conventional investment thinking. The paradigm they constructed focused on expected value (the "return") and standard deviation or statistical variance (the "risk"). They plotted risk and return along two axes and graphically showed how an artful portfolio manager could construct an efficient frontier or optimal portfolio that minimized risk for each level of desired return.

The culmination of nearly two decades of academic work on portfolio theory was the Capital Asset Pricing Model or CAPM. The CAPM included the concept of Beta-the degree to which a stock's volatility is linked to the overall stock market-now used routinely in corporate finance as well as on Wall Street. The ValueLine Investment Survey, for example, generates a beta for each stock it follows.

Although intuitively intriguing, portfolio theory has been difficult to apply to banking. Banking assets consist of loans, which are not as uniform or measurable as stocks. Furthermore, unlike securities, loans prepay, are restructured and have fluctuating payment streams caused by variable interest rates.

In the past decade, analysts and academics have made some progress in applying portfolio theory to banking. Large banks are beginning to hire or establish portfolio management units to oversee bank loans on an aggregate basis. The San Francisco-based consulting firm, KMV, was one of the first firms to attempt to quantify loan risk by using portfolio theory. KMV's model measures an expected default frequency, or EDF, which can be calculated for most publicly traded and many privately owned firms. Although the theory is slow going to the layman, banks are warming now to KMV, particularly in the syndication desks of money center and foreign banks.

Recently, a bank and vendor group led by J.P. Morgan unveiled CreditMetrics, a mechanism for calculating credit value-at-risk. The group expects the CreditMetrics methodology to become the industry standard. CreditMetrics tackles one of the last resistance points to portfolio theory in banking -the fact that loan losses are not evenly (in statistical terms, normally) distributed. The volatility of stock prices conforms reasonably well to a normal distribution; default behavior for loans does not.

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