Tag Archives: portfolio-strategy

What Is And Isn’t ‘Risk’

It’s a popular thing to bash on measuring the risk of an investment portfolio with standard deviation, the preferred metric of most academic studies. If you skipped stats in college (congratulations, by the way), standard deviation measures how much movement around an average return you might expect in an asset or portfolio. So higher standard deviation = bigger “swings” in, say, annual stock market returns. Of course, standard deviation is far from perfect. Most commonly cited is that no one cares about big swings to the upside – a big up year is hardly perceived as risk by any investor! A popular line from many institutional investors, especially value-oriented stock pickers, is that “the only real risk is the permanent loss of capital.” Such a nice little soundbite. You hear this all the time, including from giants like Seth Klarman and Howard Marks. And for a stock picker, I suppose avoiding the permanent loss of capital is huge. Especially if you run a concentrated portfolio of 20-30 stocks. Right now I wouldn’t want to be the guys managing the Sequoia fund, which at the end of the second quarter had a 28.7% stake in Valeant Pharmaceuticals (NYSE: VRX ). Valeant is down big ($96.65 today from $178 and change less than a week ago) this week after becoming the target of a short-seller accusing the company of massive fraud. I don’t know or particularly care how Valeant shakes out, but if you have a stock that is over 25% of your portfolio, you don’t want it to go bankrupt. There’s no coming back from that. The trouble with the “permanent loss of capital” risk definition for most investors is that it is laughably easy to avoid. Anyone who owns one single diversified index fund has done it. Sure, if you have a fund with 3,000 stocks in it, a few are bound to go bankrupt. But those fractional losses are indistinguishable from the day-to-day 1% swings in the broad market. Any diversified investor has effectively eliminated the permanent loss of capital. So we’re back to other definitions of risk. Despite its imperfections, standard deviation (or volatility, call it what you want) is a pretty decent measurement of risk. No one is shocked to learn that a 90-day T-Bill has less volatility than an emerging market stock. Or that a 30-year Treasury Bond has more volatility than that 90-day T-Bill. And sure, standard deviation measures big swings to the upside right alongside big drawdowns. But the thing is that you can’t find me an asset class that has big upside swings without the big drawdowns. Here’s everybody’s favorite chart: (click to enlarge) Emerging markets stocks were up 66.42% in 1999! Of course they were down 25% the year before that and down 30% the following year. Small-cap growth stocks crushed it in 2009-2010 up 34.47% and 29.09%, but they were down -38.54% in 2008, worse than the S&P 500. Nothing gives you high double-digit gains without the occasional double-digit loss, unless you’re Bernie Madoff. The last argument against using volatility as a measurement of risk is usually, “So what?” Many value stock managers like to act as if huge one-year drawdowns don’t matter in the long run. They don’t want to talk about risk-adjusted returns. Well, maybe they don’t interact with their investors very much, but volatility matters to investors for two very real reasons. Drawdowns are hard to deal with, emotionally. Big losses can make for skittish investors. I don’t care how “experienced” you are as an investor. It is still hard. I remember in 2008-2009 talking to very intelligent, longtime investors who were really convinced (for a myriad of reasons we’ll get into some other time) that this time was worth being scared. Each new bear market is scary. It’s different, the economy is different, your life is different, your portfolio’s behavior is different. Each and every time. Successful investors have to stick to their investment strategy throughout these periods. We are often the greatest risk to our own portfolio, and a very real risk indeed. Drawdowns can be hard to deal with, financially. Warren Buffett is famous for saying that his favorite holding period is “forever,” but you aren’t Warren Buffett. At some point in our lives, most of us will spend money regularly from our investment portfolios. If you’re taking regular distributions, volatility matters a lot. A big drawdown can put a portfolio’s longevity at risk if liquidity is insufficient, withdrawals are too large or the drawdown is too deep. Platitudes about indefinite time horizons are lovely, but real life doesn’t always work that way. Volatility as risk matters to the bottom line of any portfolio funding regular withdrawals.

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.

The ‘Relatively’ Easy Way To Forecast Long-Term Returns

By Andrew Perrins Long-term returns are relatively easy to forecast. Short-term returns are dominated by randomness, but long-term forecasts for most asset classes can, in part, be derived mathematically (give or take some arguing about the assumptions). But why bother with long-term return expectations – for example, 10-year forecasts? For most multi-asset managers or tactical asset allocators, 10 years is an eternity. Investment managers are judged on much shorter time frames. For asset owners or asset managers compiling a strategic asset allocation, however, long-term forecasts are relevant and necessary. When combined with estimates for risk and correlation, these forecasts allow investors to fine-tune their long-term benchmarks and consider trade-offs between asset classes to enhance the implied risk and return profile of the fund. In the following table, I have aggregated the results from three major asset managers – JP Morgan , Northern Trust , and BNY Mellon – that publish their long-term return forecasts for major asset classes. Here are the average expected returns: Average Long-Term Return Forecasts Asset Class Average Forecast (per annum) US inflation 2%-2.5% US cash 2%-2.5% US 10-year bonds 2%-2.5% Commodities 2%-3% Hedge funds 4%-5% US equities 6%-7% Global equities 6%-8% Private equity 8%-9% Let’s think about how these estimates are derived and whether they are realistic. Fixed-income securities are the obvious starting point. If we buy a 10-year Treasury today with a redemption yield of 2.5% and hold it to redemption, we know that the return will be 2.5% per annum (assuming that the US government doesn’t default). The Return from US Equities Now, let’s consider US equities. The simplest expression of the truly long-term return from US equities follows a classical formula, as described by Richard Grinold and Kenneth Krone r: Long-term return from equities = Dividend yield + Inflation + Real earnings growth Long-term return from equities = 2.0% + 2.25% + 2.25% = 6.5% So, at first glance, if you believe the assumptions – that inflation will be around 2.25% and that dividends will grow pretty much in line with long-run GDP expectations – then the forecast above is reasonable. What’s not to like? Let’s unwrap this in more detail. First, should we adjust for buybacks? In reality, the payback to long-term (buy and hold) investors will be both in dividends and in capital return from share buybacks. It’s reasonable to assume that substituting buybacks for dividends makes no substantive difference to total long-term returns, although some of the publications linked in this post explore the building blocks behind this in impressive detail. Second, is it reasonable to assume that dividend growth (or earnings growth) will keep pace with the real economy? Can the profit share of GDP hold at its current level? A recent report from McKinsey & Company is forecasting that more competitive world markets will trigger a 20% fall in global profit share by 2025. Also, even if profit share holds near to recent highs, can the companies that currently make up the index maintain their own profit share as new players and technologies emerge? My personal expectation is that earnings growth will not match real GDP growth in the long run. You may have your own view. Third is the question of equity market valuation. If we are considering a finite time horizon (let’s say 10 years), then our formula above only holds if the dividend yield remains constant. If it is likely to change, we need to make a valuation adjustment. It is for this reason that the estimate of long-term US equity returns from from our fourth research publication is starkly different from those above. Rob Arnott’s team at Research Affiliates forecasts that, over the next 10 years, the valuation of the US equity market (as measured by the Shiller CAPE ratio) will revert halfway back to its long-term average. This implies a valuation adjustment of 2.4% per annum. When added to a dividend yield of 2% and their estimate of dividend growth of 1.4% per annum, this gives a prospective 10-year total return from US equities of just 1.0% per annum. Whom do you believe? 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.