Tag Archives: etfs

Backtesting Smarter Beta: Do We Have A Winner?

Summary The smarter-beta strategy uses three smart-beta ETFs as sources for an investable portfolio that provides exposure to three risk-premia factors. The factors are low volatility, momentum and quality. In this article I report on a backtest of the strategy using data from the inception of the youngest of the three ETFs. I started an exercise to mine three of iShares smart-beta ETFs for investment ideas. My idea was to use the portfolios of the funds, which are designed to provide broad exposure to one of the risk-premia factors, as a source for devising and investable portfolio that provides exposure to all three factors. The three ETFs I selected are: iShares MSCI USA Minimum Volatility ETF (NYSEARCA: USMV ) iShares MSCI USA Momentum Factor ETF (NYSEARCA: MTUM ) iShares MSCI USA Quality Factor ETF (NYSEARCA: QUAL ) These are, as the names tell us, focused on low-volatility, momentum and quality factors. I refer you to my first article on the topic where I describe the methods and concepts in detail ( A Quest For The Smartest Beta ). Briefly, I compare the portfolios of the funds and select the equity positions that are held by all three. This is illustrated in the Venn Diagram to the right. I combine the stocks that overlap the portfolio holdings of all three funds in an equal-weighted portfolio. Readers have pointed out that I’m neglecting at least two important factors, value and size, which are also cards in the iShares ETF smart-beta deck. I looked into this ( Expanding The Smart Beta Filter: Does It Help? ) and concluded they offered no advantage over the three I selected. This was based on a very limited data set as I’ll describe, however. With access to earlier cycles for the funds’ portfolios it may be worth the effort to revisit this question as well. One feature of these funds is that their indexes are rebalanced twice annually, on the last business days of May and November. Until today, I was unable to do any sort of backtest. So, when I first introduced the concept in November I used the portfolio that was put into effect in June 2015 and looked at returns over the five-month period. At the end of November, I published a rebalanced portfolio ( Momentum, Quality and Low Volatility: Continuing the Quest for Smarter Beta ) and results for the full six-months of the ETFs’ rebalancing cycle. Those results were highly encouraging. Each time I wrote on the topic, I lamented not having access to historical portfolios for the funds to further explore performance. Then a sharp-eyed reader added a comment pointing out where those data were available (thanks again, ipaul66 ). So, I’ve downloaded holdings data going back to end-of-November rebalance for the inception of QUAL, the youngest of the three funds, in August 2013. I’ve also shown that the three funds together in an equal-weighted portfolio turned in a solid performance record vs. the broader market represented by the S&P 500 TR index (^SPXTR). I’ve included that portfolio in this analysis as a comparison. The backtest covers two years, still woefully short, but a huge improvement on six months. There are four six-month cycles with complete results. The most recent cycle began on the last day of November, so we have nothing meaningful from that as yet. CAGR Let’s start with the big result: CAGRs for each of the strategies. This table shows CAGRs for each six-month cycle for the smarter-beta portfolio (MQLV), the S&P 500 TR index, and the equal-weighted ETFs (3ETFsEqWt). Both the MQLV and the three ETFs beat the S&P 500. Only for the Dec 2013 through May 2014 cycle does the broader market outperform. Commutative and Cycle Returns The next chart shows cumulative return on $100,000 invested in the three strategies on December 1, 2013 through the November 29, 2015. (click to enlarge) And, for $100,000 invested at the beginning of each semi-annual rebalancing cycle: (click to enlarge) Conclusions and Caveats These results do support and validate the earlier finding. The smarter-beta strategy appears to be an effective filter that can add meaningful alpha relative to the broader market, or to equal-weighting the three source ETFs. I caution, however, that this is based on only two years’ history, and for a quarter of that period the smarter-beta strategy sharply under performed. The model is equal-weighted which may not be optimal and weighting needs a closer look. Having this two-year data set will give me the opportunity to explore other weighting strategies. This analysis makes no allowance for trading costs. One can often buy an S&P 500 index fund in a commission-free ETF. The three-ETF portfolio requires at most twice-yearly rebalancings for modest cost. The MQLV portfolios comprised 12 to 19 positions over the two years, so trading costs are significant, especially for smaller portfolios. If I introduce a 0.25% slippage factor (which allows for trading costs but not spread costs) the CAGR falls to 15.46% for a $100,000 portfolio, still beating the S&P 500 handily, but it does illustrate the cost of turnover. For a smaller portfolio, a larger slippage factor is required. For a $10K initial investment, 32 annual trades at $8/trade would be 2.56% and that much friction drops the CAGR to $10.17%. Even assuming the best interpretation of these results, the strategy generates substantial turnover and is only suitable for reasonably large portfolios (or for those who have accounts that provide free trades). I mention this because I have had commenters suggest they might try the strategy with only a small number of shares for each position. For the investor who is not interested in the turnover and trading this strategy will require, the equal-weighted portfolio of the three ETFs is an attractive alternative. That strategy did not turn in a single negative cycle, more than can be said for either the smart-beta portfolio or the S&P 500. Trading costs are modest with a maximum of 12 trades a year for the semi-annual rebalance, but even that may not be necessary as the ETFs do not vary much from on another over the course of a year or two. Comparing the two-year CAGR of 11.68% to 9.58% for the broad market would seem to indicate that the strategies being used in the MSCI indexes do in fact capture alpha from exposure to the risk-premia factors.

We Eat Dollar Weighted Returns – VII

Photo Credit: Fated Snowfox I intended on writing this at some point, but Dr. Wesley Gray (an acquaintance of mine, and whom I respect) beat me to the punch. As he said in his blog post at The Wall Street Journal’s The Experts blog: WESLEY GRAY: Imagine the following theoretical investment opportunity: Investors can invest in a fund that will beat the market by 5% a year over the next 10 years. Of course, there is the catch: The path to outperformance will involve a five-year stretch of poor relative performance. “No problem,” you might think-buy and hold and ignore the short-term noise. Easier said than done. Consider Ken Heebner, who ran the CGM Focus Fund, a diversified mutual fund that gained 18% annually, and was Morningstar Inc.’s highest performer of the decade ending in 2009 . The CGM Focus fund, in many respects, resembled the theoretical opportunity outlined above. But the story didn’t end there: The average investor in the fund lost 11% annually over the period. What happened? The massive divergence in the fund’s performance and what the typical fund investor actually earned can be explained by the “behavioral return gap.” The behavioral return gap works as follows: During periods of strong fund performance, investors pile in, but when fund performance is at its worst, short-sighted investors redeem in droves. Thus, despite a fund’s sound long-term process, the “dollar-weighted” returns, or returns actually achieved by investors in the fund, lag substantially. In other words, fund managers can deliver a great long-term strategy, but investors can still lose. That’s why I wanted to write this post. Ken Heebner is a really bright guy, and has the strength of his convictions, but his investors don’t in general have similar strength of convictions. As such, his investors buy high and sell low with his funds. The graph at the left is from the CGM Focus Fund, as far back as I could get the data at the SEC’s EDGAR database. The fund goes all the way back to late 1997, and had a tremendous start for which I can’t find the cash flow data. The column marked flows corresponds to a figure called “Change in net assets derived from capital share transactions” from the Statement of Changes in Net Assets in the annual and semi-annual reports. This is all public data, but somewhat difficult to aggregate. I do it by hand. I use annual cash flows for most of the calculation. For the buy and hold return, I got the data from Yahoo Finance, which got it from Morningstar. Note the pattern of cash flows is positive until the financial crisis, and negative thereafter. Also note that more has gone into the fund than has come out, and thus the average investor has lost money. The buy-and-hold investor has made money, what precious few were able to do that, much less rebalance. This would be an ideal fund to rebalance. Talented manager, will do well over time. Add money when he does badly, take money out when he does well. Would make a ton of sense. Why doesn’t it happen? Why doesn’t at least buy-and-hold happen? It doesn’t happen because there is an Asset-Liability mismatch. It doesn’t matter what the retail investors say their time horizon is, the truth is it is very short. If you underperform for less than a few years, they yank funds. The poetic justice is that they yank the funds just as the performance is about to turn. Practically, the time horizon of an average investor in mutual funds is inversely proportional to the volatility of the funds they invest in. It takes a certain amount of outperformance (whether relative or absolute) to get them in, and a certain amount of underperformance to get them out. The more volatile the fund, the more rapidly that happens. And Ken Heebner is so volatile that the only thing faster than his clients coming and going, is how rapidly he turns the portfolio over, which is once every 4-5 months. Pretty astounding I think. This highlights two main facts about retail investing that can’t be denied. Asset prices move a lot more than fundamentals, and Most investors chase performance These two factors lie behind most of the losses that retail investors suffer over the long run, not active management fees. Remember as well that passive investing does not protect retail investors from themselves. I have done the same analyses with passive portfolios – the results are the same, proportionate to volatility. I know buy-and-hold gets a bad rap, and it is not deserved. Take a few of my pieces from the past: If you are a retail investor, the best thing you can do is set an asset allocation between risky and safe assets. If you want a spit-in-the-wind estimate use 120 minus your age for the percentage in risky assets, and the rest in safe assets. Rebalance to those percentages yearly. If you do that, you will not get caught in the cycle of greed and panic, and you will benefit from the madness of strangers who get greedy and panic with abandon. (Why 120? End of the mortality table. Take it from an investment actuary. We’re the best-kept secret in the financial markets.) Okay, gotta close this off. This is not the last of this series. I will do more dollar-weighted returns. As far as retail investing goes, it is the most important issue. Period. Disclosure: None

Where The Smart Money Is Investing

When it comes to investing, there are no bonus points for originality. Returns are returns, regardless of whether the trade was your idea or a hot tip from your brother-in-law. The good news is that the SEC makes available far better trading moves than those of your brother-in-law. Large institutional investors are required to disclose their portfolio holdings at least quarterly, giving the investing public a chance to look over their shoulders. You don’t want to mindlessly ape another investor’s moves because you have no way of knowing their rationale for buying or selling. But it never hurts to see how your own portfolio stacks up against some of the best in the business. So with that said, let’s take a look at the asset allocations of three managers that have left the competition in the dust over their long careers. I’ll start with Baupost Capital’s Seth Klarman, a man whose reputation in value investor circles makes him close to demigod status. Klarman runs a multi-billion-dollar portfolio with just 40 stocks in it. That’s how confident he is in his picks. So, what is Mr. Klarman betting on? Try energy. Lots of energy. 39% of his portfolio was invested in energy as of quarter end with nearly half of that amount in a single stock. It’s worth noting here that Klarman isn’t betting on the price of oil rising or on “Big Oil” stocks in general. His bet is a targeted one on liquefied natural gas exportation. But it goes to show that, even in a full-blown crisis, there can be pockets of opportunity. Next, let’s take a look at Dan Loeb, principal of hedge fund Third Point. Loeb is not a passive investor. He’s a notorious activist investor known for taking large stakes in companies and then agitating for major change. You and I don’t have that kind of power, but we can still take a peek over his shoulder and see where he sees the most value. Today, it’s in healthcare. About 40% of his portfolio is currently invested in health and biotech stocks. I don’t have the stomach to invest 40% of my portfolio in the volatile biotech sector. But my good friend Ben Benoy is something of an expert on the matter. And finally, we get to Mohnish Pabrai , a well-respected value investor and the author of one of my favorite books on investing, The Dhandho Investor . Pabrai runs the most concentrated portfolio I have ever seen among large managers. He has just seven stocks in his portfolio, and global auto stocks make up nearly 70% of the total. Longer term, autos are a bad bet. Demographic trends suggest that, at least in the US and Europe, auto sales are looking at a major reduction in demand. But any stock can be an interesting short-term opportunity if priced right, and Pabrai is currently showing a handsome profit on the trade. So, what’s the takeaway here? Buy energy, biotech and auto stocks? Not exactly. For all we know, these superinvestors might dump these stocks tomorrow… if they haven’t already (we typically get the ownership data on a 45-day lag). No, the takeaway is that it’s fine to bet big on a high-conviction trade if your system or research tells you to. You should have an exit strategy, of course, and you should be prepared to sell if your investing thesis fails to pan out. But don’t be afraid to bet big when the odds are in your favor. This article first appeared on Sizemore Insights as Where the Smart Money is Investing. Disclaimer: This article is for informational purposes only and should not be considered specific investment advice or as a solicitation to buy or sell any securities. Sizemore Capital personnel and clients will often have an interest in the securities mentioned. There is risk in any investment in traded securities, and all Sizemore Capital investment strategies have the possibility of loss. Past performance is no guarantee of future results. Original Post