The Story of Relative Value Investing (RVI™)
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| Drs. John Peavy (r) and David Goodman, Southern Methodist University ca. 1982 |
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| "Aside from a good hunch now and then, there's no magic to producing successful portfolio strategies. We just took a hard look at a lot of data and computer analyzed it to find out what really works."
John Peavy, Ph.D.
on the academic research that led
to the discovery of
Relative Value Investing (RVI)™
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It started with a few simple questions -- when it comes to stock investing, what really works? Among all the different styles and methods of investing, which is best? Is there a way for an investor to achieve excess returns with less volatility, and do it consistently?
In 1979, two professors at Southern Methodist University decided to see if there was an answer to those questions. Dr. John Peavy was chairman of the Finance Department, and Dr. David Goodman was a professor of statistics. Together they set out on a research project that would have groundbreaking results.
The idea was straightforward -- quantitatively test every style of equity investing across every market cycle, and try to determine what would have delivered the best results. Using SMU's mainframe computer -- at the time the only computer on campus -- the two professors input data and assumptions each night, and meticulously analyzed the results the following morning.
The research tested everything -- growth vs. value investing, momentum investing, market timing, conviction-weighting stocks, sector rotation, quantitative-only or "black box" strategies, highly concentrated portfolios vs. those with many stocks.
The results showed which techniques didn't work, i.e. failed to deliver reliable excess returns, or exposed the portfolio to too much volatility. At the same time, one style produced results consistently -- an investment technique that would later become known as Relative Value Investing (RVI™).
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What the Academic Research Found:
Value Beats Growth
Relative Value Beats Value
| What Worked - Value Investing
The data conclusively showed that value investing trumped growth investing. Stocks that were undervalued -- with lower price-to-earnings, price-to-book, price-to-cash flow and P/E-to growth (PEG) ratios and other value metrics -- simply performed better, with less downside risk. And the longer the time frame, the more pronounced the outperformance became.
That wasn't to say that all growth characteristics were to be ignored. Interestingly, some growth metrics -- earnings per share (EPS) growth rate, dividend yield -- when used to examine value stocks, produced good results.
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What Worked Better - Relative Value Investing
Although value did better than growth, a trend emerged -- stocks with attractive value numbers had a tendency to cluster in sectors. The model portfolio would end up being overweight in that sector, and might ignore another sector entirely. Then when the overweight sector began to lag, the portfolio would too. And when the ignored sector began to run, the portfolio would be out of position to take advantage.
The researchers had an idea -- instead of ranking value stocks against the overall market, rank them within sectors. Then build a portfolio of undervalued stocks relative to their sector peers.
The data showed that the highest P/E stocks yielded only a 2.2% return, while the S&P Index appreciated at 9% for the same period. Meanwhile, stocks with low P/E Relatives yielded an annual return of 24%.
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What the Academic Research Found:
Sector Bets Don't Always Pay
| What Didn't Work - Sector Rotation, Sector Bets
The data showed time and again that sector rotation or sector bets amount to little more than a guessing game, and it's merely a matter of time before a wrong guess wreaks havoc on the portfolio. And with traditional value investing, value stocks tend to cluster in sectors, causing inadvertent sector bets that can go wrong.
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What Did Work - Sector Neutral Portfolios
A sector neutral portfolio -- keeping the same sector weights as the benchmark -- consistently outperformed with lower volatility in nearly every market environment and time frame. The key was in picking stocks that were attractive relative to their sector peers, as opposed to the market overall.
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What the Academic Research Found:
Equally Weighted Portfolios Perform Best
| What Didn't Work - Overweighting Particular Stocks
Overweighting or "conviction weighting" particular stocks in the portfolio carries the same risk as sector bets - it incorporates an element of guesswork, and at some point the manager will guess wrong.
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What Did Work - Equally Weighted Portfolios
The research repeatedly showed that portfolios performed best when the stock weightings were kept about equal. Success depends on avoiding costly mistakes, such as having one stock blow up and ruin an otherwise sound portfolio.
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What the Academic Research Found:
50 Stocks = The Sweet Spot
| What Didn't Work - Portfolios With Too Many Stocks, or Too Few
The research tested portfolios of all different sizes, from those with hundreds of stocks to those with only a handful. Portfolios with too many names did not offer meaningful outperformance, while the most highly concentrated portfolios were too volatile.
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What Did Work - 50-Stock Portfolios
After testing portfolios of all different sizes, the data showed that between 50 and 60 stocks is the sweet spot -- concentrated enough to allow for meaningful returns, yet diverse enough to control volatility.
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What the Academic Research Found:
Beware of Black Boxes and Value Traps
| What Didn't Work - Quantitative Stock Selection
The researchers started out picking stocks solely on their quantitative score from the models they'd developed. It didn't take long to discover the shortcomings of that approach. One stock in particular, a piano company, had the top score in their research models. That piano company decided to try its hand at the annuity business, and before long the stock went to zero.
The history of stock investing is replete with can't-miss "black box" methods that looked good on paper and did well for a while, only to blow up in spectacular fashion. As the researchers found, the market does unexpected things that no black box can account for.
Also, "value traps" -- stocks that turn out to be cheap for a reason, and only get cheaper -- have tripped up many value investors. Solid fundamental analysis is the answer to avoiding value traps.
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What Did Work - Fundamental Stock Selection
Quantitative models have their place, and they have their limitations. Peavy and Goodman's research led them to build models that rank stocks within sectors, an idea at the heart of Relative Value Investing (RVI™). But the models are only a tool to show which stocks merit a closer look. The real work, and the real value added, comes from the fundamental analysis that determines if a stock really is a compelling value relative to its sector and industry peers.
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To see how academic research built the foundation for Cimarron's investment process, please see the next section, The 5 Pillars of RVI™.
For a list of selected publications relating to this research, please see here.
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