"Investors have few spare tires left. Think of the image of a car on a bumpy road to an uncertain destination that has already used up its spare tire. The cash reserves of people have been eaten up by the recent market volatility." - Mohamed El-Erian
In 1952 Harry Markowitz pioneered a portfolio management theory that set in motion a broad shift in portfolio construction methodology and even won him a Nobel Prize. Today we refer to his framework as “Modern Portfolio Theory” (MPT). The main premise of MPT is that because investors are rational, markets always perfectly reflect all available information. This belief in efficient markets led him to conclude that the only way to generate returns in excess of the market was to take on more risk.
Fast forward to today and you have a slew of investors that are questioning the merits of the market’s efficiency given the violent ups and downs of the past decade. This has opened the door for a variety of alternative approaches, one of which is being marketed as “low volatility investing.” Several ETFs have been launched in this space with the most popular being the PowerShares S&P 500 Low Volatility ETF (ticker: SPLV). In just one year the ETF has managed to garner over $2 billion in assets, which speaks to the market’s thirst for anything that might provide an edge over buy-and-hold indexing. SPLV’s strategy is rather straightforward, it simply ranks all the stocks in the S&P 500 based on realized volatility over the past 12 months and invests in the top 100 stocks showing the least amount of volatility. Although the ETF is only a year old, S&P was able to produce back tested results for what this strategy would have returned had this rule based strategy been implemented starting back in November 1990.
The results of the back test show that the low volatility strategy earned its keep during volatile markets, but its value-add is inconsistent and statistically questionable over the entire backtested period. As shown in Table 1, it lagged significantly during the 90’s and then outpaced the market by a wide margin after the turn of the century.
During the 90’s the low volatility strategy underperformed the S&P 500 without significantly reducing risk as measured by volatility and the maximum loss over the time period. This led to a lower Sharpe ratio for the low volatility strategy versus the S&P 500 (a higher Sharpe ratio would indicate an investment has generated more return per unit of risk). It also had a negative up-down capture profile meaning it captured less of the index’s upside (69%) versus downside (76%). In contrast, since the turn of the century, the low volatility strategy has drastically outperformed the S&P 500 with roughly 2/3 of the risk. Higher returns with less risk produced a much better Sharpe ratio and a strongly positive up-down capture profile.
One of the main reasons for the difference in performance between the S&P 500 and the low volatility strategy is sector weights. Table 2 shows a recent snapshot of the sector weightings of an S&P 500 ETF (SPY) and that of the SPLV.
The low volatility ETF is drastically underweight more cyclical sectors like energy and financials while holding large overweights to more defensive sectors like utilities and consumer staples. In the sideways market we’ve experience over the past decade defensive stocks have dramatically outperformed cyclicals which explains most of the outperformance of the low volatility strategy versus the S&P 500.
Another more fundamental reason for the outperformance is due to something called volatility drag. Volatility drag is the difference between an investor’s simple average annual return and their realized compound return. It can be approximated using the following equation:
Volatility Drag = ½ * Variance of Returns
To illustrate this with an example, let’s assume an investor has the following return stream: Year 1 = 25%, Year 2 = 10%, Year 3 = -35%, Year 15%, & Year 5 = 5%. At the end of 5 years, this investor will have $107.92 for every $100 invested at the beginning. This equates to an annualized return of 1.5% yet the simple average of the five return numbers is 4.0% ([25% + 10% + -35% + 15% + 5%]/5). The difference in these two numbers (2.5%) is due to volatility drag. Going back to our original equation, the estimated volatility drag is half of the variance. This works out to be 2.7% which is fairly close to the actual volatility drag of 2.5%. This concept is so important and widely overlooked by most investors that we created a short video called The Economics of Loss that explores this topic in more detail.
In summary, there is value in being able to reduce the volatility of a return stream which can lead to making more money with less risk over time. Unfortunately, we aren't convinced that SPLV is the best way to accomplishing this goal. Looking at the full set of back-tested data for the low volatility strategy there is only a 30% probability that the outperformance is statistically relevant which means that there is a 70% chance that it is entirely due to random luck. Thus we view SPLV as a creative and well-marketed product but an incomplete solution to the problem.
A better way to reduce volatility would be to use a disciplined risk management process to adjust exposure to risky assets, like stocks and commodities, in response to various market cycles. This is the cornerstone of our approach which utilizes our quantitative downside protection program, MarketVANE. Over the same time frame as the low volatility back-test, our MarketVANE back-test showed a 94% probability that the outperformance was statistically relevant with only a 6% chance that it was due to random luck. Of course, there is no guarantee that the future will replicate the past, but we are highly confident in our disciplined risk management process versus relying on a passive, rules-based approach such as the one implemented inside SPLV.
Contributor: Elliott Orsillo,CFA
Season Investments, LLC
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