Investors the world over chase alpha but struggle to get it. Nevertheless, their zeal for alphas does have one lasting effect: it nourishes an active fund management industry that is worth trillions. In the US alone, mutual funds oversee some $16 trillion of assets, six times more than that managed by ETFs (2017 Investment Company Fact Book). Some lessons, it seem, are hard to learn.
Having made my point that alphas are expensive but betas (broad factor exposures) are cheap, I don’t want to flog a dead horse. Instead, I want to move on and say a bit more about betas. What are the interesting factors driving stock prices? Why should we care? How rewarding are these factor exposures? How to get them? What are the pitfalls?
There is a fundamental difference between alphas and betas that confuses many investors. Alpha is “excess returns over a benchmark”. If I invest in a well diversified Singapore-focused equity mutual fund, a natural benchmark is the FTSE ST All Share Index. If my mutual fund returned 10% after fees last year while the FTSE ST All Share Index made 7%, my fund manager has handed me an alpha of 3%. This is kind of result that every mutual fund investor hopes for, but seldom get on a consistent basis.
What about betas? When you buy an ETF that mirrors the FTSE ST All Share Index, your beta is one because that’s the market’s beta. Unbeknownst to many investors, the market itself is a “factor”, because a factor is defined as a persistent force that drives the returns of assets like stocks or bonds.
There are two main types of factors that have driven stock returns. The first are macroeconomic factors – things like GDP growth, inflation, interest rates, energy prices and the like. As you might imagine, quantifying how stock returns move with these factors is complicated, the job of an economist (and he better be a well-trained one!). Fortunately, there is a short-cut – every day, the movements of a stock market index already aggregate the views of millions of investors, each of whom are trying to make sense of vast amounts of macroeconomic data. That is the beauty of market indices – they already do the job of many economists! If you recall my previous discussions, the near random walk property of stock prices imply that for predicting tomorrow’s or next month’s stock price, its hard to do better than use the price you observed today. This is just another way of saying that stock prices are pretty random because they buffeted by many, many forces.
The other type of factors are style factors. Style factors help to explain risk and returns within asset classes. A good example is the returns of small versus big firms. Another example is the returns of past winners (stocks that performed well over the last 12 months) versus the returns of past losers. And so on.
Do these style factors have anything to do with fundamentals or risk? Some do, some apparently don’t. There is an ongoing debate among academics on which style factor is a reward for bearing risk, and which is “wonky” (unrelated to risk, but more like capturing investor sentiment or psychology).
Style investing is what I will focus on in the rest of this blog. This is partly because interesting styles show up quite persistently in the data and also because now you can buy exposures to some of these style factors through ETFs. Importantly, since betas are what you get when you buy styles, style-ETFs give you exposure beta-driven returns you at the characteristically low cost of ETFs. Compared to mutual funds, investing in a style-ETF is pretty much a passive affair. Unlike alpha hunting, you don’t need an active fund manager to monitor style portfolios 24/7 because with style-ETFs: (1) it is broad characteristics of stocks, not the details of each stock’s fundamentals you want, and (2) if those characteristics can be counted on to give nice, stable returns, it basically runs on autopilot and the less a fund manager meddles with the portfolio, the better!
The style approach described above is now branded by the industry as the Smart Betas approach. The tagline of this approach is that “beta is the new alpha”.
As alluded above, Smart Beta essentially tries to capture high returns by forming portfolios of stocks that have exposures to certain rewarding “factors”. Since this is my last post, I’ll let you on a secret: I employ a core-satellite strategy for my equity investments. In plain English, about 60% of my equity investments (the core) is in ETFs that track broadly market indices like the FTSE ST All Share for Singapore and the S&P 500 for the US. The other 40% is in stocks that have low-volatility and high-dividend yield (I like “boring” styles).
Part of the fun of investing is in discovering new facts about styles and drawing your own list of favourite styles. There is plenty to choose. Academic research, including my own, has established that some factors seem to be anomalies: stocks with exposure to these factors have historically produced higher returns than the market, even after adjusting for risk factors (those which are thought to compensate risks). While the returns to these factors do fluctuate in the short term, they show their true colors over time, thanks to the power of compounding.
There are enough anomalies to fill a Jurassic Park, and I haven’t the time to list all of them. Here are just some of the more prominent ones:
- The Size Effect: small companies tend to have higher average returns than large companies after adjusting for betas.
- The Value Effect: low priced stocks (relative to book value, earnings, dividends, etc.) also have higher risk-adjusted returns on average than high priced stocks like growth stocks.
- The Profitability Effect: profitable firms have higher risk-adjusted returns on average than unprofitable companies even after accounting for their higher initial share prices.
- Momentum: stocks that performed well in the last 12 months (past “winners”) on average continue to have superior returns in the next 12 to 24 months compared to past “losers”.
- The Low Volatility Effect: low-volatility (boring”) stocks have higher average returns than high-volatility (“exciting”) stocks.
All the above anomalies have been extensively and carefully studied by the finest minds in finance. All are interesting, but the last one will blow you away for it says…Can you believe this? I’m sure a negative risk-return relationship is not what you learned in business school. If true, the low-volatility anomaly turns finance on its head!
Nice, but where’s the proof? The proof, ladies and gentlemen, is in a spreadsheet which I posted earlier in Blog #58. There, I presented the arithmetic mean (AM) and geometric mean (GM) returns of two volatility-sorted portfolios: low-volatility and high-volatility. Each of portfolio consists of stocks on the NYSE-AMEX exchange. They are formed annually by sorting all eligible stocks based on the standard deviation of their daily returns
Several things jump out from the spreadsheet. First, the variance (the square of the standard deviation) of the high-vol portfolio is about 7 times higher than that of the low-vol portfolio, yet their arithmetic means are about the same. Where is the reward for bearing risk? Second, look at the GM and you see that the GM of the two portfolios are oceans apart: 11.3% a year for low-vol versus 7% a year for high-vol. Since the GM formula (a) is a compounding formula and (b) already embeds volatility, the data is saying loud and clear: low risk stocks have higher average compound returns!
I can assure you that this data is not a fluke. Scores of researchers using US and non-US data as well as different sample periods have found the same thing. the low-volatility anomaly in other words, is robust.
How to exploit the low-volatility anomaly? The easiest way is to look for Smart Beta ETFs that target low-volatility stocks. There are none on SGX at the moment but fortunately, there are quite a few in the US. For example, Blackrock, the world’s largest asset management firm, has four:
- iShares Edge MSCI Min Vol USA ETF (USMV)
- ishares Edge MSCI Min Vol EAFE ETF (EFAV)
- ishares Edge MSCI Min Vol Emerging Markets ETF (EEMV)
- ishares Edge MSCI Min Vol Global ETF (ACWV)
- ishares Edge MSCI Min Vol Asia Ex-Japan (
The “Min Vol” label indicates that these ETFs are constructed using an in-house minimum volatility methodology that searches specifically for low-volatility stocks. Typical of ETFs, the expense ratios are low, ranging from 0.15% to 0.35%, with the last ETF having the highest expense ratio. The first four ETFs were started in October 2011 while the last, in June 2014. So it looks like the ETF industry is waking up to the findings of academic research.
The following graphic gives you some idea of the performance of the first four ETFs. The measurement period is from Nov 1, 2011 to Dec 31, 2017. The ETF returns assume that dividends are reinvested but excludes taxes. They (but not the market benchmarks) are net of expense ratios. In all but one case, the min-vol ETFs have higher annualized average returns with lower risk compared to market benchmarks.
Before you send cheques to any of the above ETFs, a word of caution is in order. First, I can’t say for sure whether this or any other anomalies will persist indefinitely. All I can say is most of these anomalies have been quite persistent over the last 50 years or so. The research evidence bears this out.
Secondly, remember that dividends form part of total returns and US-sourced dividends are subject to a withholding tax of 30% for non-U.S. tax residents. A 30% withholding tax reduces every dollar of dividends to 70 cents. The tax effect is worse for high dividend yield stocks than low-yield stocks. This is something to keep in mind when you are investing in the US.
Finally, while expense ratios are lower for ETFs than for mutual funds, they do vary quite a bit across ETFs as shown earlier. As Cliff Asness, co-founder of AQR Capital Management puts it: “There is no investment product so great that a fee cannot make it bad.”
That’s all, folks. Time to say adieu. I hope you’ve enjoyed my ramblings and gotten some useful takeaways in how to manage your personal finance. I’ll keep the blog running for a while so that you can review earlier blogs (ideally, in sequential order). And a big thanks to all who have sent in your comments and corrections to my typo and other mistakes. On this note, I wish you all, Many Happy Returns 🙂