Tag Archives: alt-investing

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.

Common Sense Trumps Smart Beta

Summary Smart beta is more or less a marketing term. In application, it represents a form of factor investing. Trusting your money to a single factor that worked in the past has a far greater chance of producing inferior rather than superior returns in the future. Use your trump card known as common sense when Wall Street comes calling to offer you their latest great idea to make you rich. Every few years or so the powerful money men of Wall Street come up with a new idea they believe will lead to unimaginable wealth for their clients and themselves. These ideas often stem from rigorous statistical studies which attempt to confirm that a particular idea offers better than average returns to investors. Of course, in the world of academia, any believable idea originally promoted by one researcher will quickly be followed by additional research from academics around the world expanding on, reinforcing, or contradicting the original study. With the passage of time, the studies find their way into the hands of those on Wall Street who create investment products, and are then easily distributed through the army of Wall Street salesmen. At the present time, the “great idea” making the rounds is “smart beta.” Before I begin my discussion of smart beta, I want to briefly pay tribute to a soon-to-be relic from my past. For many who live near the border of North and South Carolina, Independence Day includes a trip to the Carowinds theme park for a day of thrills and fireworks. With that in mind, I wanted to give a tip of the hat to the grand old roller coaster Thunder Road, whose time is unfortunately up. Perhaps the wooden structure hasn’t been thrilling visitors like the park’s newest addition, the Fury 325, the world’s tallest and fastest coaster. However, I will certainly miss it. Back when the kids were young, we purchased season passes to the park every year. The best part of having season passes is that we could run up after work for a few hours on a weekday. One of our favorite rides was Thunder Road. Unlike the Fury 325, old Thunder Road traveled a mere 45 miles per hour. Instead of smooth and fast, she bucked, shook, and rattled over the rails. To increase the fun, a simple seat restraint was used, but it wasn’t quite tight enough to hold you firmly in place. During those weeknights we could ride once, twice, or as many times as we wanted, as we rarely had to wait in line between rides. What fun it was for all. As we look back over the past six months, the ride in the world of investing is a lot like the ride given by Thunder Road. The economy, interest rates, stock prices, the dollar, gas prices and just about everything else shook, rattled, and bucked around, but ended just about where they started on January 1st. I wish it was as fun as those days with my kids on the coaster, but it wasn’t, as you know. We have mentioned many times that when prices for common stocks are at or above our calculation of fair value, forward returns will depend on growth in earnings, dividends, and the general level of interest rates. All of these change slowly. As we see it today, forward returns for the rest of the year should be positive, but less than we are used to. Of course, anything could happen when prices are not at bargain levels, including some thrilling shakes, rattles, and rolls. An old sage used to warn me to “buckle up,” for we are in for a good ride. You can be assured that we have tightened the seat belt in hopes that it will hold us in place. Now, on to “smart beta.” The phrase sounds as though it must refer to something special. After all, every one of us wants to be a “smart” investor. As for the word “beta,” it sounds smart on its own. Combine the two words, add a great marketing team, and you are sure to capture a few dollars from investors who believe you can outsmart the market and reap better than average returns. And wouldn’t that be nice? Earning better than average returns over time would assure that each of us could easily have more than enough money to meet any goal we may have. Over the years, I have become quite skeptical of any claims of easy outperformance, and smart beta is no exception. Without going into mathematical equations, we can explain beta using an example. Take the size of your home. If your home is 3000 square feet, and your neighbor’s home is 1500 square feet, you know that your house is twice as large as your neighbor’s. At the same time, if you know that the average house in your community is 1500 square feet, you know that your house is twice the size as the average house in your community. Beta measures the volatility of one investment to the volatility of the average investment. It is the same as comparing the square feet of your house to the average square feet of the houses in your community. If the beta of one investment is high relative to the average investment, it would be considered riskier, or more volatile than the average of all investments. If the beta is low relative to the average, it would be considered less risky, or less volatile than the average of all investments. To simplify the measuring, in math, the beta of the average is always equal to 1. If an investment has a beta of 2.0 it would be twice as risky as the average of all investments. If the beta is 0.50 it would be one-half as risky as the average. I guess now, simply because we have some idea of what beta is, we can claim to be smart, and if we use that knowledge to make better than average returns, I guess we can claim to be “really smart!” Smart beta is more or less a marketing term. In application, it represents a form of factor investing. A factor such as price to book, price to earnings, dividend yield, or one of the multitude of individual stock characteristics that are studied to see how a portfolio which owns a number of companies with the same factor has performed relative to the entire market. If one of these characteristics produces greater than average returns historically, then an assumption is made that a portfolio based on these characteristics will outperform in the future. The last count I have, which seems to change daily, is that there are more than 300 different factors that people claim offer better than average returns. Of course finding something which worked in the past is meaningless unless the future is identical to the past. Trying to identify factors that provide superior returns has had its rewards. But not in the way you might think. I want to share a little story about a young man who early in his career thought he was pretty darn smart. In fact, he was so full of himself that he knew with a little extra effort he could find a way to build portfolios that would perform better than everyone else’s. This young man, with a brand new CFA (Chartered Financial Analyst) Charter hanging on his wall, a new computer, and a database full of information on thousands of individual public companies, plus a big head, began a study of the 500 companies that were currently held in the S&P 500 index. He ranked all 500 companies based on S&P quality ratings, price to book value, price to cash flow, price to earnings, dividend yield and a few others factors that he believed were important. To build a portfolio, he divided the 500 companies into five equal weighted portfolios of 100 each. Then he compared the returns of each portfolio to the returns of the entire market Low and behold, if he had purchased the 100 companies with the highest S&P Quality rating at the beginning of the year, and rebalanced at the end of the year, selling those shares whose S&P Quality rating dropped and replacing them with the highest over the past five years, he would have outperformed the S&P 500 by over 3% a year. Not only that, but he would have done it with less risk as measured by a beta of 0.90 over the same five years. Excited, the young CFA wanted to share this with as many people as he could knowing full well that they would be just as excited about making extra returns as he was. Of course it was a good thing he did not do that until some real-time testing could be done. Since his own portfolio was meager, buying 100 stocks was beyond his means, so he just did it on paper, thinking that if it worked over the next twelve months surely a big investor would come along and reward him for his expertise. A year later, the young CFA was quite embarrassed at the results. This real-time testing without using real money taught him a pretty good lesson. On review, the young man recognized one problem after another. Let’s take the time to look at a few of these. Every year the companies included in the S&P 500 change. There may not be a large number of changes, but changes there are. The original 500 companies used in his study were not the same as the 500 used to build his portfolio. In other words, he was comparing apples to oranges. The original results may have had nothing to do with quality. It may have been that those companies with a high quality rating also increased cash flow, or dividends, or the market simply wanted to own quality during the five years for any given reason. The original study did not include any extra cost. He did not account for the cost of commissions, fees, or the actual price paid for each company bought or sold during the day instead of just using a closing price. These costs were so great that any outperformance on paper was erased. In the real world, five years isn’t even a full economic cycle. Would the performance still be good over a lifetime, and not just the last five years? Would it have performed just as well over a ten year period, or over a five year period twenty years earlier? The recent five years is just too short of a time period to have real meaning. If the excess returns were real, and all he needed was S&P’s Quality rating, anyone could find the same quality rating, causing others to piggyback on his research. Given how easy it would be to do that, other investors could easily wipe out any extra returns by driving up the prices of the highest quality companies. Most of the products created by investment companies that market to you under the heading of smart beta are based on a single factor. This is dangerous for individual investors. The only guarantee is that the future will be different than the past. It may rhyme, but it will not be the same. Trusting your money to a single factor that worked in the past has a far greater chance of producing inferior rather than superior returns in the future. Our young CFA was able to learn the pitfalls of back-testing without causing damage to himself or his clients. Managing a portfolio to meet long or short term goals is far more difficult than applying a little math. So this is a reminder: use your trump card known as common sense when Wall Street comes calling to offer you their latest great idea to make you rich. _______________________________________________________________________________ Anderson Griggs & Company, Inc., doing business as Anderson Griggs Investments, is a registered investment adviser. Anderson Griggs only conducts business in states and locations where it is properly registered or meets state requirement for advisors. This commentary is for informational purposes only and is not an offer of investment advice. We will only render advice after we deliver our Form ADV Part 2 to a client in an authorized jurisdiction and receive a properly executed Investment Supervisory Services Agreement. Any reference to performance is historical in nature and no assumption about future performance should be made based on the past performance of any Anderson Griggs’ Investment Objectives, individual account, individual security or index. Upon request, Anderson Griggs Investments will provide to you a list of all trade recommendations made by us for the immediately preceding 12 months. The authors of publications are expressing general opinions and commentary. They are not attempting to provide legal, accounting, or specific advice to any individual concerning their personal situation. Anderson Griggs Investments’ office is located at 113 E. Main St., Suite 310, Rock Hill, SC 29730. The local phone number is 803-324-5044 and nationally can be reached via its toll-free number 800-254-0874. Disclosure: I am/we are long SPY. (More…) I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it. I have no business relationship with any company whose stock is mentioned in this article.

Why The Law Of Large Numbers Dictates Effective Risk Management

Summary It is effective risk management that determines profitability rather than the incidence of wins to losses. The law of large numbers suggests that a larger number of trades with a positive reward to risk ratio will be more effective than a smaller number of trades. In this regard, it is possible for a trader to be “wrong” a majority of the time while continuing to remain profitable. “It’s not whether you’re right or wrong that’s important, but how much money you make when you’re right and how much you lose when you’re wrong.” – George Soros The entire dynamic of successful trading could probably be summed up in the above sentence. When I started out trading forex, I was overly concerned with getting the trades right. However, I have come to learn that the most successful traders are not the ones who are right all the time; rather they are the ones who know how to manage their risk most effectively . For instance, the odds that a plane will crash somewhere in the world are 1 in 11 million. Indeed, this is a very low probability. However, when one considers the vast number of flights that take off and land every day, it is sadly almost inevitable that there will be a plane crash at some point in the future. The odds of a golfer getting a hole in one are 5,000 to 1. However, across the world there are far more than 5,000 games of golf being played in a single day; it is therefore almost inevitable that a player somewhere in the world will manage to score a hole in one today. The above phenomenon is known as the law of large numbers ; where an event with a low probability of occurring on its own has a higher probability of occurring when subjected to a large number of trials. This has important implications for risk management, and moreover it demonstrates how a trader can still be wrong the majority of the time while continuing to be profitable. Let us take this as an example. Suppose that we have eight forex trades in a particular month, with a 1:3 risk-reward ratio, or a stop loss of 50 pips and a take profit of 150 pips. For each trade (discounting technical or fundamental factors), the odds are greater that we will make a loss rather than a profit. However, the profit on each trade far outweighs the potential loss. With a 1:3 risk-reward ratio, we have a 75 percent chance of the price hitting our stop loss with a 25 percent chance of it hitting our take profit ratio. However, this also means that only two of the eight trades need to be profitable for us to breakeven. Moreover, the law of large numbers dictates that at least two of our trades are indeed likely to be profitable. 1-(1-p)^number of trials where p is the probability of an event occurring In the above instance, we need at least three of our trades to hit the take profit point in order to be profitable. Given that we have a 0.25 probability of this happening, our probabilities are as follows: 1-(0.25)^1 = 0.25 1-(0.25)^2 = 0.4375 1-(0.25)^3 = 0.5781 1-(0.25)^4 = 0.6835 1-(0.25)^5 = 0.7626 1-(0.25)^6 = 0.8220 1-(0.25)^7 = 0.8665 1-(0.25)^8 = 0.8998 We see that with eight trades being placed, we have an 89 percent probability that at least one trade will hit our take profit point. Given that we need three trades to be profitable: 0.8998^3 = 72.85 percent probability of three trades being profitable In this regard, we see that the law of large numbers provides us with an attractive risk-reward set up in that it limits our downside while maximizing our upside. Moreover, we can be wrong more often than we are right and still remain profitable. One of the big reasons why most new traders fail is the inability to manage risk effectively. For instance, if we decided to set up trades with a high risk and low return, e.g. 150-pip stop loss and 50-pip profit, then even if we were right a majority of the time it would only take a couple of losing trades to wipe out our winnings. Ultimately, being a successful trader is not always about being right. It is about managing your risk effectively. As we can see, the law of large numbers plays a key role in doing so. Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours. (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.