Warren Buffett’s Secret Sauce Loses Its Flavor
In a recent article, Wall Street Journal columnist Jason Zweig noted that Berkshire Hathaway’s stock had underperformed the S&P 500 Index over the 10 years ending in 2017, 7.7% versus 8.5%.
Zweig hypothesized that the reason for the underperformance is that the size of Berkshire Hathaway’s portfolio has grown so large that it creates a burden that is difficult to overcome. “Too much money” is a long-known problem for active managers because success contains the seeds of future failure as cash flow, and the problems that come with it, swamps skill.
While size can become a problem for active managers, I suggest there is a far more important factor at work. As my co-author Andrew Berkin and I discuss in our book, “The Incredible Shrinking Alpha,” the market has become an increasingly difficult competitor. We provide four explanations: academics have converted alpha into beta; the pool of victims that can be exploited has dramatically shrunk; the competition has become increasingly skillful; and the supply of capital chasing alpha has increased.
Today I’ll focus on the publication of academic research converting what once were sources of alpha into common factors (beta), removing sources of excess risk-adjusted returns. To address this issue, we’ll take a brief walk through the history of modern financial thinking.
William Sharpe and John Lintner are typically given most of the credit for introducing the first formal asset pricing model, the capital asset pricing model (CAPM). CAPM provided the first precise definition of risk and how it drives expected returns. It looks at returns through a one-factor lens, meaning the risk and return of a portfolio is determined only by its exposure to beta.
Beta is the measure of the equity-type risk of a stock, mutual fund or portfolio, relative to the risk of the overall market. CAPM was the finance world’s operating model for about 30 years. However, all models, by definition, are flawed, or wrong. If they were perfectly correct, they would be laws, like we have in physics. Over time, anomalies that violated CAPM began to surface.
The more prominent ones:
- 1981: Rolf Banz’s “The Relationship Between Return and Market Value of Common Stocks” found that beta does not fully explain the higher average return of small stocks.
- 1981: Sanjoy Basu’s “The Relationship Between Earnings’ Yield, Market Value and Return for NYSE Common Stocks” found that the positive relationship between the earnings yield (earnings/price ratio) and average return is left unexplained by beta.
- 1985: Barr Rosenburg, Kenneth Reid and Ronald Lanstein found a positive relationship between average stock return and the book-to-market ratio in the paper “Persuasive Evidence of Market Inefficiency.”
The 1992 paper “The Cross-Section of Expected Stock Returns” by Eugene Fama and Kenneth French basically summarized the anomalies in one place. The essential conclusions from the paper were that the CAPM only explained about two-thirds of the differences in returns of diversified portfolios, and that a better model could be built using more than just the sole factor of market beta.
Fama-French Three-Factor Model
One year later, in 1993, Fama and French published “Common Risk Factors in the Returns on Stocks and Bonds.” This paper proposed a new asset pricing model, called the Fama-French three-factor model. This model proposes that, along with the market factor of beta, exposure to the size and value factors explains the cross section of expected stock returns.
The authors demonstrated that we lived not in a one-factor world, but in a three-factor world. They showed that the risk and expected return of a portfolio is explained not only by its exposure to beta, but also by its exposure to two other factors: size (small stocks) and price (stocks with low relative prices, or value stocks).
Numerous studies have confirmed that the model’s three factors explain an overwhelming majority of the differences in returns of diversified portfolios. In fact, the Fama-French three-factor model improved explanatory power from about two-thirds of the differences in returns between diversified portfolios to over 90%.
From 1927 through 2017, while market beta produced an annual average premium of 8.5%, the small and value factors produced premiums of 3.2% and 4.9%, respectively. That large value premium went a long way to explaining the superior performance of the superstar investors from the value school of Benjamin Graham and David Dodd. So the anomaly of these superstar investors became less of one. But we aren’t done yet.
In 1997, Mark Carhart was the first to use momentum, together with the three Fama-French factors, to explain mutual fund returns. This new momentum factor made a significant contribution to the explanatory power of the model. The four-factor model soon became the standard tool used to analyze and explain the performance of investment managers and investment strategies.
The next contribution to asset pricing models, one that helped further explain Warren Buffett’s superior performance, was made by Robert Novy-Marx.
His June 2012 paper, “The Other Side of Value: The Gross Profitability Premium,” provided investors with new insights into the cross section of stocks returns. He found that profitable firms generate significantly higher returns than unprofitable firms, despite having significantly higher valuation ratios (i.e., higher price-to-book ratios). Controlling for profitability dramatically increases the performance of value strategies, with the most profitable firms earning average returns 0.31 percentage points per month higher than the least-profitable firms.
This idea has been extended to a quality factor that captures a broader set of quality characteristics among stocks. (See the 2013 paper “Quality Minus Junk” by Clifford Asness, Andrea Frazzini and Lasse Pedersen.)
High-quality stocks that are profitable, stable, growing and have high payout outperform low-quality stocks with the opposite characteristics. With this insight, we have all the information we need to address the issue of the sources of Buffett’s outperformance (outside, of course, the underperformance noted in the Zweig column).
The conventional wisdom has always been that Buffett’s success is explained by his stock-picking skills and his discipline—keeping his head while others are losing theirs. However, the 2013 study “Buffett’s Alpha,” by Andrea Frazzini and David Kabiller of AQR Capital Management and Lasse Pedersen of New York University and the Copenhagen Business School, offers some very interesting and unconventional answers.
They found that, in addition to benefiting from the use of cheap leverage provided by Berkshire Hathaway’s insurance operations, Buffett bought stocks that are “safe” (meaning stocks that have low beta and low volatility), “cheap” (value stocks with low price-to-book ratios), high-quality (meaning stocks that are profitable, stable, growing and with high payout ratios) and large.
The most interesting finding of the study was that stocks with these characteristics—low risk, cheap and high quality—tend to perform well in general, not just the ones that Buffett buys.
High-quality companies, according to the study, have the following characteristics: low earnings volatility, high margins, high asset turnover (indicating efficiency), low financial leverage and low operating leverage (indicating a strong balance sheet and low macroeconomic risk) and low specific-stock risk (volatility unexplained by macroeconomic activity). Companies with these attributes historically have provided higher returns, especially in down markets.
In other words, it’s Buffett’s strategy that generated “alpha,” not his stock selection skills. The authors adjusted Buffett’s performance for the betting-against-beta (BAB) factor (from Frazzini and Pedersen’s study “Betting Against Beta,” which was published in the January 2014 issue of the Journal of Financial Economics) and the quality factor mentioned previously. The authors found that once all the factors (beta, size, value, momentum, BAB and quality) and leverage are accounted for, a large part of Buffett’s performance is explained and his alpha is statistically insignificant.
It’s extremely important to understand this finding doesn’t detract in any way from Buffett’s performance. After all, it took decades for modern financial theory to catch up with Buffett and discover his “secret sauce.”
As my friend and fellow author Bill Bernstein points out, being the first, or among the first, to discover a strategy that beats the market is what buys you the yachts, not simply copying the strategy after it’s already well-known and the low-hanging fruit has been picked. However, the research does provide insights into why Buffett was so successful—it was strategy and not stock picking.
Buffett’s genius, as Frazzini, Kabiller and Pedersen observe, thus appears to be in recognizing long ago that “these factors work, applying leverage without ever having a fire sale, and sticking to his principles.” The authors noted that Buffett himself stated in Berkshire Hathaway’s 1994 annual report: “Ben Graham taught me 45 years ago that, in investing, it is not necessary to do extraordinary things to get extraordinary results.”
Public Vs. Private Returns
The authors also considered “whether Buffett’s skill is due to his ability to buy the right stocks versus his ability as a CEO.” To address this, they decomposed Berkshire Hathaway’s returns into two parts: one due to investments in publicly traded stocks, and another due to private companies run within the parent firm.
The idea is that the return of the public stocks is mainly driven by Buffett’s stock selection skill, whereas the private companies could have a larger element of management skill. They found that the public companies performed better. So apparently it’s not Buffett’s skill as a manager that is responsible for his alpha.
However, they did find that the companies Berkshire Hathaway owns provide a steady source of financing at low cost, allowing him to leverage any stock selection ability he has—36% of Buffett’s liabilities consisted of insurance float with an average cost below the Treasury-bill rate.
Another advantage, though a less important one, is that Berkshire Hathaway “also appears to finance part of its capital expenditure using tax deductions for accelerated depreciation of property, plant and equipment.” Accelerated depreciation acts just like an interest-free loan.
Again, once the authors accounted for style factors (market, size, value, momentum, BAB and quality) and leverage, the alpha of Berkshire Hathaway’s public stock portfolio drops to a statistically insignificant annualized 0.1%.
In other words, these factors almost completely explain the performance of Buffett’s public portfolio. The bottom line is that we now know Buffett’s secret sauce is to buy safe, high-quality value stocks coupled with the application of low-cost leverage.
With the publication of the academic findings, mutual fund families, such as AQR, Bridgeway and Dimensional Fund Advisors (DFA), have incorporated the information into their portfolio construction rules, giving investors a way to invest like Warren Buffett. (Full disclosure: My firm, Buckingham Strategic Wealth, recommends AQR, Bridgeway and DFA funds in constructing client portfolios.)
Having reviewed the history of asset pricing models and some related research, we can now take a closer look at the evidence.
To more properly analyze Buffett’s (that is, Berkshire Hathaway’s) performance, we will compare the returns from Berkshire Hathaway (BRK.A) to those of the Vanguard 500 Index Fund (VFIAX) and DFA’s Large Value Fund (DFLVX).
The reason for doing so is that Buffett’s investment strategy has always been a value strategy, and the underperformance noted by Zweig might just be a result of the fact that the value premium was negative over the period 2008 through 2017—the average annual return of the Fama-French value factor (HML, or high minus low) was -0.7%.
Zweig noted that BRK.A underperformed VFIAX (7.7% versus 8.5%) over the 10 years ended 2017. However, it also underperformed DFLVX, which returned 8.7%. Also note that DFLVX slightly outperformed VFIAX. BRK.A did have a lower standard deviation of returns, a measure of volatility, than DFLVX (16.9% versus 19.2%), but DFLVX’s Sharpe ratio was slightly higher (0.52 versus 0.51). VFIAX experienced volatility of just 15.1%. It also had the highest Sharpe ratio, at 0.60.
If we extend the period to examine returns over the last 20 years (1998 through 2017), a period when the annual average value premium was 2.0%, the results look much better for BRK.A relative to the S&P 500. BRK.A returned 8.8% versus just 6.3% for VFIAX, though it did exhibit greater volatility (15.5% versus 14.4%). DFLVX produced the same 8.8% return, though it exhibited higher volatility still (17.1%). The Sharpe ratios were: BRK.A: 0.54; DFLVX: 0.50; and VFIAX: 0.40.
BRK.A’s underperformance relative to the S&P 500 over the last 10 years is virtually fully explained by the negative performance of the value factor over that period. This underperformance is not unusual—since 1927, value has had a negative premium in 14% of 10-year periods, not much different than the 10% figure for a negative market beta premium.
That said, when we compare the performance of BRK.A to that of DFLVX, and whether looking at the 10- or 20-year period, there is little-to-no evidence of stock selection skill. And BRK.A was managing a lot less money 20 years ago than it is today. It appears that market efficiency has caught up with Warren Buffett.
Again, none of this is meant to take anything away from the decades of outperformance Buffett delivered prior to the periods we examined. After all, he discovered the “secret sauce” that could outperform the market well before the academics did. However, those days appear to be gone.
This commentary originally appeared March 9 on ETF.com
By clicking on any of the links above, you acknowledge that they are solely for your convenience, and do not necessarily imply any affiliations, sponsorships, endorsements or representations whatsoever by us regarding third-party Web sites. We are not responsible for the content, availability or privacy policies of these sites, and shall not be responsible or liable for any information, opinions, advice, products or services available on or through them.
The opinions expressed by featured authors are their own and may not accurately reflect those of the BAM ALLIANCE. This article is for general information only and is not intended to serve as specific financial, accounting or tax advice.
© 2018, The BAM ALLIANCE