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The Low Volatility Anomaly: A Theoretical Underpinning

Summary This article introduces a discussion of the theoretical underpinning for the Low Volatility Anomaly, or why lower-risk investments have outperformed higher-risk investments over time. It features long time interval studies of the Low Volatility Anomaly from famed academics, supplementing the more recent 25-year study referenced in the introductory article to the series. The article discusses the divergence between model and market of one of the most oft-cited financial concepts. Given the long-run structural alpha generated by low volatility strategies, I want to dedicate a more detailed discussion of the efficacy of this style of investing for Seeking Alpha readers. Providing a detailed theoretical underpinning of the strategy or detailing multiple examples of its outperformance can prove challenging in a single blog post, so I am providing a more academic examination of the topic over multiple articles that each zero in on a separate proof point describing the strategy. In the first article in this series, I provided an introduction to the Low Volatility Anomaly with an example depicting the outperformance of a low-volatility (NYSEARCA: SPLV ) bent to the S&P 500 (NYSEARCA: SPY ) relative to the broader market and high-beta stocks. In this second article, I am going to begin to delve into a theoretical underpinning for the Low Volatility Anomaly and demonstrate that it has been proven in research dating back to the 1930s. Theoretical Underpinning for the Low Volatility Anomaly Since its introduction in the early 1960s, the Capital Asset Pricing Model (CAPM) has permeated the investment management landscape. CAPM is used to determine a theoretically appropriate required rate of return of an asset added to a diversified portfolio. This model takes into account the asset’s sensitivity to non-diversifiable risk, which is oft represented through the beta coefficient. In CAPM, in what has become one of the most fundamental formulas of modern finance, the expected return of an asset is equal to the risk-free rate plus the product of beta multiplied by the difference between the expected market return less the risk-free rate, as seen in the following equation: E(R a ) = R f + Β a *(E(R m )-R f ) The idea of beta is axiomatic to many investment managers. Investment discussion is littered with the concept of beta. High-beta investments have higher expected returns and above-market risk. As we move back down the security market line (SML), the inverse is then true for low-beta investments, characterized by lower expected returns and below-market risk. Empirical evidence, academic research and long time series studies across asset classes and geographies have shown that the actual relationship between risk and return is flatter than the model or market expectations suggests. At the extremes, and as shown in the graphs above in this article, the relationship between risk and return might indeed be negative. Understanding the shortcomings of CAPM and the market’s misinformed notion of the relationship between beta, risk and expected return could produce a normative arbitrage opportunity that is exceedingly capital-efficient. If the Capital Asset Pricing Model held in practice, we should see a linear relationship between beta and return as predicted by the model. Low-beta/lower-volatility assets would be expected to generate proportionately lower returns than the market. Since CAPM can be mathematically derived, and this series will subsequently demonstrate that it has failed in empirical tests, then the assumptions underpinning CAPM must be unable to hold in practice. Criticisms of the Capital Asset Pricing Model are almost as old as the model itself, but the model’s simplicity and utility have become ingrained in modern finance nonetheless. In 1972, Black, Scholes and Jensen, in a study of NYSE-listed stocks from 1931-1965, found that when securities were grouped into deciles by their beta, a time series regression of these portfolios’ excess returns on the market portfolio’s excess returns indicated that high-beta securities had significantly negative intercepts and that low-beta securities had significantly positive intercepts – a contradiction to the expected finding from the CAPM model. An excerpt of their findings is tabled below, expanding the scope of the Low Volatility Anomaly far longer than my simple twenty-five year charts. High-beta stocks (left) had negative alpha, and low-beta stocks (right) had positive alpha. (click to enlarge) Excerpted from “The Capital Asset Pricing Model: Some Empirical Tests” by Fischer Black, Michael Jensen and Myron Scholes (1972) Three years later, Robert Haugen and James Heins produced a forty-year study that demonstrated that, over the long run, stock portfolios with lower variance in monthly returns experienced greater average returns than riskier cohorts through multiple business cycles, and that relative returns were time series-dependent. Fischer Black (1993) and Robert Haugen (2012) would both produce academic papers decades later with expanded market data sets that demonstrated the efficacy of low volatility strategies. Black, enshrined in the nomenclature of an option pricing model that won his frequent collaborator Myron Scholes a Nobel Prize after Black’s death, updated his previous study conducted with Scholes and Jensen in 1972 to include data through 1991. A period that takes us from their early Depression-era study and links it with our S&P data from 1991 to current. (click to enlarge) Excerpted from “Beta and Return: Announcement of the Death of Beta Seem Premature”, Fischer Black 1993 In the chart above, one can see that in this expanded sample period, low-beta stocks (right) again did much better than predicted by CAPM (positive alpha), and high-beta stocks did worse still. Robert Haugen published several papers in the subsequent decades focused on the low volatility anomaly. In 1991, Haugen and collaborator Nardin Baker demonstrated that a low volatility subset of the capitalization-weighted Wilshire 5000 would have outperformed from 1972 to 1989. Shortly before Haugen’s death in early 2013, Baker and Haugen demonstrated that from 1990 through 2011, in a sample set that included stocks in twenty-one developed countries and twelve emerging markets, low-risk stocks outperformed in the total sample universe and in each individual country – a study I have previously referenced in past articles. Excerpted from: Low Risk Stocks Outperform within All Observable Markets of the World. Baker and Haugen (2012) If CAPM is a descriptive, but not practicable, model of investing, then violations of its underpinning assumptions could serve as possible explanations for successful strategies that appear to deviate from what one would expect from the model. The following pages are dedicated to examining how violations of CAPM’s assumptions lead to market returns that deviate from expectations. Sharpe (1964) formalized the assumptions underpinning Markowitz’s (1954) Modern Portfolio Theory . With the market fifty years later still thinking about risk-adjusted returns in a ratio bearing his name, it seems prudent to use Sharpe’s underlying model assumptions: Investors are rational and risk-averse, and when choosing among portfolios, they care only about maximizing economic utility of their one-period investment return; A common pure rate of interest, with all investors able to borrow or lend funds on equal terms; Homogeneous investor expectations, including expected values, standard deviations and correlation coefficients; The absence of taxes or transaction costs. The second of these underlying assumptions will form the basis of our first hypothesis, Leverage Aversion, for the existence and persistence of the Low Volatility Anomaly, which will be captured in the next article in this series. Disclaimer My articles may contain statements and projections that are forward-looking in nature, and therefore, inherently subject to numerous risks, uncertainties and assumptions. While my articles focus on generating long-term risk-adjusted returns, investment decisions necessarily involve the risk of loss of principal. Individual investor circumstances vary significantly, and information gleaned from my articles should be applied to your own unique investment situation, objectives, risk tolerance, and investment horizon. Disclosure: I am/we are long SPLV, SPY. (More…) I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.

The Low Volatility Debate: SPLV Vs. USMV

Summary The Low Volatility Anomaly describes portfolios of lower volatility securities that have produced higher risk-adjusted returns than higher volatility securities historically. Two ETFs – SPLV and USMV – have amassed $5B apiece in assets under management seeking to capitalize on this anomaly. This article discusses the relative differences in how these funds are constructed and how these discrepancies can impact their respective risk-return profiles. I recently reprised my series on five buy-and-hold strategies that have historically produced better absolute and risk-adjusted returns than the broader market. The third of these five strategies was about the Low Volatility Anomaly, or why lower risk stocks have historically outperformed their higher risk counterparts. A reader in the comments section of the article asked why I preferred the Powershares S&P 500 Low Volatility ETF (NYSEARCA: SPLV ) over the iShares MSCI USA Minimum Volatility ETF (NYSEARCA: USMV ). Given the increasing popularity of low volatility strategies, I thought that this would make an excellent topic for Seeking Alpha Readers. (For readers looking for a primer on Low Volatility Strategies prior to delving into a review of the top two domestic fund choices, please reference the links in the article or read Making Buffett’s Alpha Your Own .) What are the differences in the strategies? Given that these are both passive funds seeking to replicate the returns of an index, the answer to this question will be driven by the differences between the two benchmarks. SPLV seeks to replicate the S&P 500 Low Volatility Index, which is constituted by the one-hundred least volatile stocks in the S&P 500 (NYSEARCA: SPY ) as measured by the standard deviation of the security’s daily price returns over the trailing year and rebalanced quarterly. In contrast, the MSCI USA Minimum Volatility Index is calculated by optimizing its parent index the MSCI USA Index for the lowest absolute risk subject to constraints to maintain replicability, investability, and to limit turnover and industry concentrations. What have the risk and return profiles of these indices been historically? Below is a cumulative return series of the two indices since the earliest dually available data points. You can see that the S&P Low Volatility Index has outperformed by 55bp per annum. (click to enlarge) Drilling down further into these index return series, I have tabled some summary risk and return statistics for the return profiles of these two indices. In addition to higher cumulative returns over the matched sample period, the S&P 500 Low Volatility Index had lower variability of returns and a smaller peak-to-trough drawdown. The underlying indices are of course uninvestable, with the exchange-traded funds seeking to replicate these index returns the best way for retail investors to follow these strategies. Respectively, the ETF tracking these indices have only been outstanding since May and October 2011. It is difficult to determine the efficacy of either strategy in a market characterized by such strong returns over the short life span of these funds. I have graphed the cumulative returns of these ETFs since USMV’s later inception below: (click to enlarge) While the index data is necessarily backcasted, I believe that the longer time series for the indices, which featured three economic recessions and two large stock market drawdowns, is more informative than the history of the exchange-traded funds, which have existed only during a historic bull market. I hope that this analysis is valuable to Seeking Alpha readers interested in low volatility strategies but who might not have access to the historical return data. How does the composition of these two funds differ today? Despite the very strong correlation noted in the historical return series above, the composition of the two indices is quite unique. I examined the industry concentrations, top holdings, and index fundamentals in this section. Industry Concentrations The MSCI USA Minimum Volatility Index constraint to keep sector weightings within 5% of the market-weighted index gives it a more diversified set of industry exposures than the S&P Low Volatility Index, which is industry agnostic and formed from the one-hundred stocks in the S&P 500 with the lowest realized volatility. Readers likely share my surprise that financials dominate the Low Volatility Index. Also of note, utilities, traditionally a defensive, low beta industry, are under-represented. When I wrote about Low Volatility Stocks in mid-2013 , utilities represented more than a quarter of the Low Volatility index. You can bet that the Low Volatility Index was relatively underweight financials prior to the financial crisis as rising return volatility would have seen these stocks excluded from the portfolio. An industry-agnostic tilt towards lower volatility stocks is likely what caused the relative outperformance of the Low Volatility Index relative to the Minimum Volatility Index through the stock market slump in 2008- early 2009. Top Holdings There is some decided overlap between the top holdings, but the interesting part of this chart is less about how they are similar but rather how they are different. Despite the USMV index having 64% more holdings (164 vs. 100), it is still slightly more concentrated in its top holdings. Because the index weights of SPLV are the inverse of their trailing one-year volatilities rebalanced quarterly, the fund is much more close to equal-weighted because stock volatilities are likely to be less divergent than a capitalization-weighting. Like low volatility strategies, equal weighting is also one of my five factor tilts that have historically produced higher risk-adjusted returns than the market . Readers should also note that Exxon Mobil (NYSE: XOM ) is in the top ten holdings of USMV whereas no Energy stocks are included in the one-hundred constituents in the S&P Low Volatility Index. Falling oil prices have led to more volatile returns in that space, excluding those stocks from the Low Volatility Index. USMV is required to maintain an Energy exposure to keep the index from deviating outside of its industry band with the parent index. Exxon and its fortress balance sheet represent a whopping 48% of the Energy sector weight for USMV. Index Fundamentals The average index fundamentals are relatively similar. Lower volatility stocks currently trade at incrementally higher multiples than the market, and their more steady business profiles lend to higher dividend yields. Multiples throughout the market are stretched, and investors should be asking whether the premium multiple in low volatility stocks is attractive given their higher downside protection. Some might counter that it is a valuable feature while others might contend that this downside protection is now priced too expensively. I remain in the former camp. As I wrote in my 10 Themes Shaping Markets in the Back Half of 2015 : “Stretched equity multiples domestically will necessitate that valuations be driven by changes in earnings, tempering further price gains. As equity prices rise, investors may look to opportunistically rotate into underperforming rate-sensitive assets and lower volatility assets.” Conclusion For me, the S&P Low Volatility Index’s construction is a simple and transparent way to access a low volatility bent. I am not seeking to minimize volatility, but generate higher risk-adjusted returns, which the S&P Low Volatility Index has done historically versus both the broader market and the MSCI USA Minimum Volatility Index. There are certainly cases to be made for USMV. The replicating ETF is lower cost (15bp to SPLV’s 25bp), and has more constituents and less industry concentration. This greater diversification has not led to lower risk however in the historical study. You want to be incrementally overweight more defensive industries as markets are correcting. In a great 2011 paper, ” Benchmarks as Limits to Arbitrage: Understanding the Low Volatility Anomaly “, the authors concluded that behavioral biases towards high volatility stocks coupled with delegated investment management with fixed benchmarks without the use of leverage flattens the relationship between risk and return. If benchmarking is an impediment to capturing the Low Volatility Anomaly, why would I want my Low Volatility fund exposure to have more rigid industry constraints. Since the S&P Low Volatility Index is less constrained, its industry concentrations can swing meaningfully. I discussed previously the sharp reduction in utility exposure, which has likely been a function of that sector’s greater interest rate sensitivity and a pickup in interest rate volatility. Investors may look at the current higher allocation of utilities in USMV or lower allocation to financials and determine that industry mix is preferable to them. In analyzing the funds in this manner, they can be viewed more as complements than substitutes. Both of these funds have their merits, and I applaud the fund families’ efforts to provide low-cost solutions to retail investors seeking to capture the Low Volatility Anomaly. Hopefully, readers now better understand the differences in index construction and how that manifests into different risk-return profiles Author’s Postscript As an aside, this article was prompted by reader feedback. Intelligent discussion and debate is what transitions Seeking Alpha from a collection of articles into a community. Please share your thoughtful observations that you believe could further this research as we all try to “Seek Alpha” together. Disclaimer My articles may contain statements and projections that are forward-looking in nature, and therefore inherently subject to numerous risks, uncertainties and assumptions. While my articles focus on generating long-term risk-adjusted returns, investment decisions necessarily involve the risk of loss of principal. Individual investor circumstances vary significantly, and information gleaned from my articles should be applied to your own unique investment situation, objectives, risk tolerance, and investment horizon. Disclosure: I am/we are long SPLV, SPY. (More…) I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.

Low Volatility ETFs Turn The Lights Off And That’s A Good Thing

Summary Concerns about a rising rate environment has triggered a sell-off in utilities stocks. Low-volatility ETFs have trimmed their exposure to utilities. A look at the changes in low-volatility ETF options. By Todd Shriber & Tom Lydon Rising 10-year Treasury yields have, predictably, stoked chatter about the vulnerability of interest rate-sensitive asset classes and sectors. Case and point: Utilities stocks and exchange traded funds. Over the past three months, the Utilities Select Sector SPDR ETF (NYSEARCA: XLU ) , the largest utilities ETF, is off 2.2%, as 10-year Treasury yields have surged nearly 13%. There was a time when such a yield spike would have been problematic for the PowerShares S&P 500 Low Volatility Portfolio ETF (NYSEARCA: SPLV ) , particularly if investors did not properly understand how SPLV works, but that is not the case today. The low-volatility ETF targets 100 of the least volatile stocks from the S&P 500 index and weights the positions inverse to volatility – the least volatile stocks has a greater weight in the portfolio. That led to spurious accusations that SPLV was a utilities ETF in disguise. Critics will be heartened to learn that the utilities sector is now SPLV’s second-smallest sector weight. The ETF’s utilities allocation has dwindled to 2.6% as of June 12 from 19.4% in September. In fact, SPLV is underweight utilities stocks by 20 basis points relative to the S&P 500. “Given the prospect of higher rates, investors may wish to consider a low volatility investment approach and check their holdings for interest rate sensitivity. Over the past five years, financial stocks have been among the most sensitive to rising interest rates – especially insurance and diversified financial shares,” according to a recent PowerShares note. Of course, utilities are the group worst affected by rising interest rates. So, the double dose of good news for SPLV is its scant utilities weight combined with a 35.6% weight to financial services names, by far the ETF’s largest sector allocation. Digging deeper into SPLV’s financial services lineup reveals opportunity. Seventeen of the ETF’s financial services holdings are either insurance providers or regional banks, two industries that are positively correlated to rising interest rates. “In fact, since its May 2011 inception, SPLV has exhibited lower volatility than the S&P 500 Index. This is because the fund’s underlying index follows an unconstrained investment approach that allows for dynamic sector rotation,” according to PowerShares. “Due to SPLV’s unconstrained sector rotations, the fund has shed much of its exposure to the underperforming utility sector over the past two years, from just over 30% in March 2013 to under 3% currently.” SPLV’s primary rival, the iShares MSCI USA Minimum Volatility ETF (NYSEARCA: USMV ) , has a utilities weight of 7.7%, nearly triple that of the PowerShares offering. PowerShares S&P 500 Low Volatility Portfolio ETF (click to enlarge) Disclosure: The author has no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. (More…) The author wrote this article themselves, and it expresses their own opinions. The author is not receiving compensation for it. The author has no business relationship with any company whose stock is mentioned in this article.