Warren Buffett is a pretty smart guy. There's no denying that. He has a nearly 50-year track record proving just that.
A pretty big part of that track record is successfully investing in financial stocks. Berkshire Hathaway (BRK.A -0.30%) (BRK.B -0.26%) is, at its core, an insurance company, after all. But there's more to the story than that.
Some of the Oracle of Omaha's largest market positions are in bank stocks. He owns Wells Fargo (WFC -1.11%) and U.S. Bancorp (USB -1.49%), two of the most widely respected banks in the world. And then there's that massive stake of warrants that Berkshire bought on the cheap from Bank of America (BAC -1.07%) at the bottom of the financial crisis.
According to a 2013 interview on CNBC, Buffett evaluates banks quite simply. He looks to banks with outstanding returns on assets. For Buffett, those banks are efficiently run, more profitable and, in general, will command higher valuations in the market.
The question for us today, then, is if Buffett's logic shows up in industry data, how can we use that to imitate his success investing in bank stocks?
The test
Using data from S&P Capital IQ, I pulled together a list of 61 of the largest banks in the U.S. From there, we can compare the return on asset figures with each bank's current price-to-tangible book value -- a common valuation benchmark in the banking industry. If Buffett's logic is correct, then we should expect to see a correlation between these two measures.
As you can see on the chart, there does seem to be some correlation between these two metrics; however, that correlation isn't perfect. Statisticians use a calculation called R-squared to measure just how correlated a given data set is. The R-squared always falls between zero and one, with values closer to one representing a higher correlation.
In this case, the R-squared value is 0.2, indicating a slight correlation, but nothing earth shattering. Is it time to panic? Could the Oracle of Omaha be... wrong?
At this point, a statistician would likely tell you that, just because the R-Squared is low, does not mean that the data isn't correlated. In fact, in social science research, researchers will rarely have R-Squared values higher than 0.5. Human behavior, it turns out, is pretty erratic.
What does this mean for investors?
The data implies that Buffett is on to something here. I suppose that isn't much of a surprise.
For value investors, the results of this experiment show a potentially helpful method to finding bank stocks that could be undervalued or overvalued. For example, TD Bank (TD 0.75%) trades at 2.9 times tangible book value, and yet only reports a 0.9% return on assets. This is an indication that the stock may be overvalued (it's the dot on the chart at the far right and bottom).
Or, on the other side of the data set, we can see SVB Financial Group (SIVB.Q), which trades at just 2.1 times tangible book value, even with its industry best 2.5% return on assets. In the chart, SVB is the highest dot on the chart and centered left to right. Relative to the rest of the industry, this specialty bank appears to be undervalued.
Buffett's favorite banks fall about where you would expect them -- they each command a premium price, and deliver a premium return on assets. Wells Fargo trades at 2.0 times tangible book value and reports 1.5% return on assets. U.S. Bancorp trades at 2.7 times tangible book value and has a return on assets of 1.6%.
A word of caution
Market inefficiencies are the key to value investing. When the market prices a stock at less than what it is inherently worth, value investors swoop in and buy the stock at a discount.
In the same way, there are specific circumstances that could cause a stock to buck the trend we've discussed here. A stock may trade at a lower multiple because of dangerous problem loans, or because of a unique business model. TD Bank, for example, uses leverage to stoke its return on equity figure; it's a unique example of a bank with great earnings and ROE with below average return on assets.
The key, of course, is to always do your homework. If you find an anomaly, make sure you ask yourself, "Why?".
This model is a great place to start, and can potentially uncover hidden value opportunities in the banking business. But just like the stock market itself, there is a margin of error. After all, there's a reason that Buffett reads all those SEC filings for years before he invests in a company.
