Tag Archives: etfs

Are Portfolio Decisions Feeding Volatility?

By Brian Brugman and Martin Atkin Markets had been unusually calm until risk surged in late-August. Bigger portfolio shifts when volatility is rising may be magnifying the spikes, making markets harder to navigate. We think the answer is focusing on more than risk. It’s true that volatility has moderated a bit, but is still higher than it was before August, and policy makers have taken note of these sudden shifts in risk. In fact, it was one reason the U.S. Federal Reserve decided to hold off on raising interest rates in September. To avoid being whipsawed, investors should take a holistic view of their portfolios. The focus should be on more than risk signals – return signals matter, too. Reactions to Market Volatility Amplify It Our research indicates that risk factors – and oversimplified asset-allocation decisions based largely on volatility measures – can create a painful cycle. The very trigger that prompts an allocation shift away from equities is itself influenced by the resulting sale. And volatility begins to feed on itself. There’s evidence that more managers are making decisions based largely on changes in market volatility. We looked at allocation changes over time, based on the implied equity exposure across different mutual fund categories, examining both high-risk and low-risk environments. We found that reductions in equity exposure have become noticeably larger since the Global Financial Crisis of 2008 ( Display 1 ). In fact, the downward shifts for tactical allocation strategies have almost doubled in size. It’s not surprising that tactical strategies make adjustments, but the bigger moves today are notable. Even world allocation strategies, which largely left their equity allocations alone pre-crisis, have begun to make significant equity reductions. Our analysis also suggests that portfolio shifts aren’t just bigger than before, but they’re also happening faster when volatility rises. This helps make volatility spikes more pronounced. The August episode confirmed this: selling pressure due to a collective decision to de-risk likely made the first few days more severe. Before August 24, when risk was below average, the group of strategies we isolated for this analysis had an average overweight to equity of 9%. Shortly after the spike in risk, they were significantly underweight, averaging 15% less equity exposure than is typical ( Display 2 ). The Problem of Volatility Tunnel Vision One likely reason for the rush for the exits is that many risk-managed strategies exclusively use volatility gauges as a simplified trigger for making allocation changes. Because this systematic approach is so common, it creates significant selling momentum in equities when risk starts to rise and the signal turns red. This risk “tunnel vision” can lead to even sharper moves in the very metrics used to determine portfolio positioning. We don’t think these types of asset-allocation triggers are robust enough. It’s important to determine if a sudden change in the risk environment is temporary or long-lasting. That knowledge can make a portfolio manager less likely to make the classic mistake: trend-following and selling into distress at a market trough. A Holistic Process Must Integrate More than Risk Signals One way to tackle this problem is to include both expected risk and expected return across asset classes in quantitative analysis. It’s also important not to leave the fundamental judgement behind, and to consider how technical factors in the market impact the asset-allocation equation. All things considered, we think it makes sense to be modestly underweight equities in the current environment. Volatility is above average, but we think the initial spike may have been exacerbated by indiscriminate selling from risk-managed strategies. Stalling growth in emerging markets and falling commodity demand may not be as much of a spillover risk for developed economies as some investors may think. In turbulent times like these, the ability to be dynamic in shifting equity beta can be very helpful. And volatility is a valuable signal that helps inform that decision. The key is to make sure that the trigger for shifting beta isn’t overly sensitive to changes in volatility alone. The views expressed herein do not constitute research, investment advice or trade recommendations and do not necessarily represent the views of all AB portfolio-management teams. Brian T. Brugman, Portfolio Manager – Multi-Asset Martin Atkin, Head of U.S. Client Solutions – AllianceBernstein Multi-Asset Solutions Group; Investment Director – Dynamic Asset Allocation; and National Managing Director – Bernstein Global Wealth Management

Insight From Quant Research Part 1: Quality Minus Junk

Summary Renowned investors like Warren Buffett proclaim the attractiveness of “high quality” stocks. But what evidence is there that these really outperform across the board? Prominent researcher / hedge fund manager Cliff Asness investigated this question. Background Even for those that consider themselves purely bottoms-up, fundamental investors, there is a lot of valuable insight to be gleaned from the large volume of quantitative research available in the academic literature. One of the most noteworthy pieces over the past few years, “Quality Minus Junk” , is an emblematic example. This paper was written by Cliff Asness (one of the best known quantitative investors / hedge fund managers today), along with Andrea Frazzini and Lasse Pedersen from AQR Investments, and studies the tendency for “high quality” stocks to generate alpha relative to “low quality” stocks. In this article, I’ll walk through the key findings and why they’re valuable for us. Research Methodology In order to test the hypothesis about whether high-quality stocks do in fact outperform, Asness et al. first had to decide how to define “quality.” They ultimately decided to adopt a broad definition, by taking the average of four different proxies: Profitability : They measured profits (per unit of book value) in several ways, including gross profits, margins, earnings, accruals and cash flows. Growth : This was calculated over the period spanning from the prior five years in each of their profitability measures. Safety : They assessed both return-based measures of safety (e.g., market beta and volatility) and fundamental-based measures of safety (e.g., stocks with low leverage, low volatility of profitability, and low credit risk). Payout : The payout ratio is the fraction of profits paid out to shareholders, and can be seen as a measure of shareholder friendliness. The particular metrics they used were equity and debt net issuance and total net payout over profits. They then computed a quality score based on this definition for 39,308 stocks, covering 24 developed market countries between June 1951 and December 2012. Finally, for each of the U.S. and the global basket of developed market countries, they calculated the historical-return series resulting from buying the top 30% high-quality stocks and shorting the bottom 30%. Here is what these series look like. Key Findings As the visuals above would suggest, this ‘quality minus junk’, or QMJ, factor delivered positive returns in 23 out of 24 countries that they studied and highly statistically significant risk-adjusted returns both in the U.S. and abroad. This reflects the researchers’ observation that although higher-quality firms have exhibited higher prices on average, they have still been sufficiently undervalued relative to low-quality firms to deliver meaningful excess returns. Upon digging in deeper, there are also a couple of additional noteworthy findings from this analysis. Importantly, beyond looking just at the raw returns of their QMJ series, they also calculated its alpha by running regressions on the four standard Fama-French risk factors (market beta, small-minus-big, high-minus-low book value, and up-minus-down – i.e., momentum). The purpose was to demonstrate whether there is indeed statistically significant alpha beyond what can be explained by the standard risk factors. As shown below, they found that there was, with 0.5%+ of monthly alpha in most geographies. Finally, they evaluated QMJ’s alpha in different types of market environments, shown below. Interestingly, they found that the alpha was particularly strong during recessions, which they attribute to a “flight to quality” among investors during these periods of time. In other words, in addition to offering positive returns, QMJ could also reduce a portfolio’s market risk. This characteristic is particularly notable given that it seems to clearly contradict the critical underpinning of the efficient market hypothesis that investors can only be rewarded with excess returns for taking additional market risk. Conclusion Many renowned investors (most famously, Warren Buffett) have proclaimed the attractiveness of long-term investing in high-quality businesses, particularly when prices are relatively low. Investors can take more comfort in these assertions given that they are in fact backed up by a relatively large body of historical data from around the world.

Making Sense Of Long-Term Returns

By Michael Batnick, CFA All advisers face the same challenge: How can we best help investors understand what sort of long-term returns they can rationally expect? This is an extremely important topic. It forms the basis of Social Security projections, pension estimates, and determining how much a household needs to save to retire comfortably. What’s often absent from a discussion on stock returns is the many ways in which returns can be measured. There are a lot of questions: What is the appropriate time period? Does one year make more sense than three years? What about a rolling return versus an annual return? When do we start measuring? Should we include the Great Depression or look at post World War II numbers? If you can’t see the importance of this conversation yet, it may be time for a quick reminder. Let’s go over a couple of different ways that we could measure the return of the S&P 500 Index. Remember as you’re reading this that it’s our job to make sure investors understand these nuances. Price Return vs. Total Return If you invested one dollar in the S&P 500 in 1928 (no, this was not possible at the time), it would have been worth ~$109 by the end of August 2015. If you were to measure the total return, however, that $1 jumps from $109 to $3,362! Nominal Return vs. Real Return It’s always important to account for inflation. If we do that, our $1 invested in 1928 becomes $342 in 2015. Compounding at 6.8% after inflation is still an impressive long-term return, even if it is just a tenth of what the total return looks like before inflation is accounted for. Average Return vs. Compound Return The S&P 500 (total return) has averaged nearly 12% a year since the mid-1920s, however, investors’ wealth would have compounded at just under 10%. The reason there is such a large gap between arithmetic and compound returns is because the 12% average returns are not earned in a straight line. There were years like 2008, when the index fell 37%. Once stocks lose 37%, they need to gain 58% to get back to even. As we often find ourselves explaining to the investing public, there are major differences between average annual returns and the returns of any individual year. In the chart below, you’ll notice that the average return of 7.5% (price only) was rarely seen in any one year. Only about 5% of the time did investors generate returns even close to the average. S&P 500 (Price Only) Perhaps a better way to present this data is the distribution of returns. S&P 500 Distribution of Annual Returns (Price Only) This can provide investors with a better idea of what the range of possibilities is. Expecting an average return of X% over a 20-year period is one thing, but being prepared for the outlier years and surviving them is something else entirely. And, of course, these outlier years can happen one after another. How does it change the way that you look at the world if you learned about markets during a year when they performed terribly? It’s a helpful exercise to break returns into different time periods to demonstrate the life-cycle experience an investor might have had. The chart below shows “bull” (green) and “bear” (red) market regimes throughout history. S&P 500 (Log Scale) People born in 1900 would probably count the Great Depression as the formative experience of their investing life cycle. It’s hard to imagine that living and working through it would not leave an indelible impression. Although every period in history is unique, one thing we can say with certainty is that bull and bear markets are permanent features of investing. Take a look at the returns in the table below. In the last 90 years, there were several periods of time when investors’ wealth compounded at very low rates. Pointing to average historical returns is little comfort to investors in the depths of a protracted bear market. Likewise, when markets get overextended, people tend to throw caution to the wind, learning nothing from history. Of course, we have to consider the reliability of the data itself. In an eye-opening paper published in The Journal of Investing, entitled ” The Myth of 1926: How much Do We Know about Long-Term Returns on US Stocks ?” Edward McQuarrie looks at the Center for Research in Security Prices (CRSP) database , which many argue is the gold standard for historical stock returns. He writes: “1) The CRSP time frame, which begins in 1926, excludes more than 50% of the historical record of widespread, large-scale stock trading in the United States, which goes back almost 200 years; and 2) for more than 50% of its time frame, the CRSP dataset excludes the majority of stocks trading in the United States, especially the smaller and more vulnerable enterprises. Putting these two facts together, we may say that CRSP provides comprehensive price series data for less than 20% of the total US stock trading record, aggregating across time period and type of stock.” McQuarrie shares some interesting insights about the way we think about historical stock returns. While not suggesting that the CRSP has failed in its due diligence, he makes the point that there are listing requirements that have undoubtedly omitted stocks from the database. We have seen that different starting periods and different ways of measuring returns can have significant implications for investors. So what if anything can we conclude and suggest to our clients? Here are a few things to remember: Past performance is absolutely not predictive of future results. Data can be manipulated! Sticking with an investment plan during a bad year (or a series of bad years) is what will make them successful. The results of diversification are predictable even if the results of an investment are not. Having a command of these issues and laying them out for our clients beforehand will make for a much more enlightening – and realistic – presentation. Disclaimer: Please note that the content of this site should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute.