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The Problem with Banks

Although it seems like fortuitous hindsight at this point, Magic Formula investors have largely been spared from the absolute calamity in the banking industry over the past year. The strategy throws out the financial industry as a matter of course, because their businesses are fundamentally different from nearly every other industry out there, and valuation techniques used by the strategy do not allow them to be accurately valued. Magic Formula investors would never have bought stocks like Bear Stearns, Lehman Brothers (LEH), Merrill Lynch (MER), or AIG (AIG) - at any price.

In actuality, this has been a lucky break. However, those who value the economics and principles of business models know that banking is an inherently risky industry. And over the past decade, it has become even more risky as "innovative" new financial products have been made available to drive ever higher incomes for these companies, especially the investment bank variety. This article will take a look at the basic business of banking and show why these stocks are best avoided by individual investors.

Banks do not make money the way everyone else does. For traditional banks, the core of the business is simple: the bank earns money on the interest paid to it through loans, which is at a higher rate than the interest it pays to depositors. The spread between these two rates is where the bulk of revenues comes from. Because it is a pain for people and businesses to switch bank accounts, many banks also make incremental revenue by charging fees to customers (like ATM fees, maintenance fees, overdraft fees, etc.). Since banking is not capital intensive in a buildings-and-equipment sense, the cash flow generated can be put back to work into more loans, which leads to more interest spread income.

Investment banks are somewhat different. While most have a traditional banking arm, these banks earn the majority of their revenues by helping businesses raise capital by underwriting debt (usually through bonds), advising on business transactions, and buying and repackaging securities.

While most businesses are valued primarily based on revenues, earnings, and cash flow, these are not effective methods for valuing a bank. Traditional banks have little control over the interest spread, which leads to fluctuating levels of revenue and earnings. Investment banks can see business levels vary depending on the prevailing interest rate and climate for large deals. The primary way to value a bank is by looking at it's book value, or the net value of it's assets. Historically, the rule of thumb has been to look for banks trading at under 2 times book value. This ensures you are not paying too much for the bank's assets, while still accounting for future growth.

The difficulty arises in determining what these assets are truly worth. For traditional banks, this primarily consists of valuing outstanding loans. In essence, the loan is worth the principal plus the interest to maturity, minus a provision for inevitable loan defaults. The assets of investment banks are largely the same, except instead of consumer loans we're talking about corporate debt. This is a very simple way to look at things. One reason for the recent collapse is the invention of exotic debt securities that are even more difficult to value.

Before making a loan or underwriting debt, there is a process that needs to be performed to protect against default. First, the bank needs to accurately evaluate the creditworthiness of the borrower through documentation and credit history. Then it needs to ensure that the loan is collateralized by an asset that can be sold to recoup the principal amount. Lastly, it needs to put aside an allowance for revenue losses due to loan or debt defaults, which are unavoidable. That's a lot of assumptions, and when you have to make a lot of assumptions, inevitably mistakes will be made. Which leads us to the core problem...

The problem with banks is that bad assumptions snowball into unrecoverable problems! When banks get lax on lending standards, as we saw during the real estate boom of the early decade, loans are given to non-creditworthy borrowers, asset values are overstated (leaving loans under-collateralized), but loan default allowances remain the same. As more and more loans default, the default allowances are greatly exceeded, leading to additional write-downs. Eventually, management does not have the capital to cover these write-downs. When this bad news gets out, things snowball. The stock price falls, making raising capital through issuing equity increasingly difficult. Depositors can get spooked and rush to pull their assets, compounding the problem.

At this point, there is no way to survive outside of undesirable actions such as taking on more debt, selling assets at fire-sale prices, or diluting shareholders by selling massive amounts of equity. All of this leads to more tanking stock prices and unhappy shareholders. If the bank is unable to complete one or more of these actions, it may even go bankrupt. Perhaps the scariest part of all of this is that it can happen in a matter of months. Consider Lehman Brothers (LEH), which had never even reported a quarterly loss in nearly 160 years until June, and just 3 months later is filing for bankruptcy protection.

Even in the best of circumstances, investors have very little solid ground to stand on when valuing a financial institution. That 2x book value rule? Well, you are valuing assets that are theoretical in value. Nobody knows what a loan is really worth. When you add in the absurd amount and style of loans owned by most banks, it becomes impossible to tell which loan values are real or not. How many people really know how to value a credit swap, a collateralized debt obligation (CDO), or a mortgage-backed security that is composed of possibly thousands of real estate loans?

By sticking to the Magic Formula, we automatically stick to companies that have real assets, predictable revenues, and stable cash flows. By sticking with MagicDiligence, you are buying companies that can maintain those attributes over the long term.

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