Editorial Note: There is plenty of data showing that a broad index-based approach can beat active management net of fees. However, there is also data to show that selecting companies with some characteristics have historically outperformed their broader markets. How can this be? The answer is by using a rules-based quantitative approach to select for those factors. Not based on a narrative or where you think the economy is going. It must also be operationalized in a way that the costs don’t nullify the expected return premium. It is not a free lunch, but learn more about it to consider whether there is a role for factor-based investing in your portfolio. For disclosure, I use some factor-weighted ETFs, including ZLB, in my portfolio.
This is a paid sponsor post. You will see relatively few of these on The Loonie Doctor because my mission is the priority of this site. Sponsored posts must align with that and the sponsor provides a service or product that I would use myself or recommend to a family member. One that I like. I retain editorial control, and the articles are written to be educational as the primary objective. It is a win-win because you get deep content written by industry experts, but vetted by me. You can read more about why I decided to collaborate with BMO ETFs as a sponsor, but I think you’ll get it after reading this post.
Low Volatility Investing
By: Erin Allen, VP, BMO ETFs, Online Distribution
Quantitative Investing: Beyond Beta
Exposure to “the market” for a long period of time is the most consistent way to capture market returns. That market exposure is called beta, and returns in excess of that are termed alpha. SPIVA and Morningstar reports have shown that active management attempts to achieve alpha (net of fees) fail to do so over 90% of the time. However, there are characteristics of stocks, or “factors”, that have been identified to explain historical returns in excess of Beta. A quantitative approach to overweight exposure to those factors is what underlies factor, “smart-beta”, or quantitative investing.
ETFs as a Tool for Factor Investing
Factor or smart-beta investing is a powerful tool used to meet investing objectives. It is an index construction methodology that differs from traditional market-capitalization weighting. Traditionally, the ability to access factor exposure was attributed to active managers in search of portfolio alpha. The manager would select companies based on positive attributes coupled to their view of future market trends. In contrast, with smart beta ETFs, factors are now accessible with greater transparency as to the factor targeted and at a lower cost.
The complexity and discipline required to consistently select stocks using a quantitative strategy makes it challenging. That is fortunate because if everyone did it, the alpha of those factors would disappear. It is also fortunate because there are ETFs to simplify operationalizing a factor-investing strategy for investors.
ETFs can accurately target a factor by selecting a basket of securities using a unique methodology, an advanced screening process and a rules-based approach. ETFs have the ability to maintain this exposure by rebalancing frequently to ensure the securities continue to align with a given factor.
Finding & Using Factors
What are some of these factors?
Investors can target specific factors based on isolated characteristics with defining attributes.
Factors are identified by analyzing historical market data. Of course, that means that there is a risk of data-mining and identifying “factors” that are meaningless, but fit the dataset used. So, for us to take a factor seriously, it must have an underlying rationale from an asset-pricing standpoint. Plus, it must be pervasive across different data sets and persistent over long time periods.
Examples of widely recognized factors include, Quality, Value, Growth (Dividend), Low Volatility, Size and Momentum. As research in this field progresses, there are some factors that actually contain factors that explain their excess returns. For example, low volatility contains exposure to profitability and conservative investment factors from the Fama-French model. Interestingly, different factors may also interact with each other in complementary ways.
How can we use these factors?
Using factors can also offer different risk-adjusted exposures. Investors can build a portfolio with different factor weightings to change its risk profile or to generate more alpha.
Factors, like all investments, exist on a risk-reward spectrum. Some factors, such as value, provide much greater potential for returns. Value means a low price relative to earnings. That depressed price may appropriately reflect more risk for those future earnings. So, they are often characterized as having greater volatility than the broad market as the future unfolds.
Other factors such as low volatility, carry less market risk. Depending on an investor’s financial goals and market outlook they can choose to tilt their portfolios towards a factor to decrease market risk, generate alpha, or both. It is vital that an investor using a factor tilt understands what they are trying to achieve with it and the time frame.
Factors offer increased potential return, but not a free lunch.
Factors offer a potential premium because they are harder to hold onto at times. For example, holding low-volatility stocks while watching aggressive areas of the market rocket upwards can be challenging. Conversely, during market downturns, low volatility may be easier to hang onto while value and small-sized companies get whacked and are hard to tolerate.
Different factors may outperform or underperform the broader market at different times and for extended periods of time. That offers the potential to diversify amongst different factors. Diversification is the only “free lunch” in investing and using different holdings with lower correlation to each other magnifies the benefit.
For you to be able to use factors effectively, you must understand more about how they work, why, and what to expect. Otherwise, you will struggle to stick to your long-term plan through year or decade-long cycles. So, let’s do a deeper dive into low volatility as a factor.
Low Volatility Factor Investing
Volatility & Risk/Return
Most investors understand that return is related to risk. All else being equal, riskier stocks should deliver higher returns in aggregate. The inverse is that less risky investments are generally considered defensive, with an expectation of more modest gains. Yet lower volatility (or “low vol”) investing directly challenges that conventional thinking. Volatility is commonly thought of as a marker of risk. Yet, low-volatility stocks have had higher returns relative to higher-risk stocks.1
There’s a strong argument that low-vol investing is an overlooked opportunity to earn excess returns. You don’t hear it shouted from the rooftops, but low-volatility stocks have, in fact, out-performed over the past 90 years. It has been persistent. Studies have also shown that this trend holds true across different global markets. It is pervasive. Two of our criteria for a factor to be believable. But, what is the underlying rationale?
Rationale for Low Volatility as a Factor
There is a good behavioral rationale for why low volatility may persist as a factor beyond Beta. As alluded to already, it is harder to hold when markets are hot. Fear of missing out is powerful. However, that emotional drive also can lead to mispricing for those buying speculative assets.
Investors, or perhaps more accurately, traders, often chase higher-risk stocks, which to their detriment often don’t live up to expectations. The recent meme-stock behaviour, where a social-media organized group chased returns on various small-cap stocks, in the often misguided “shoot for the moon” is a great recent example. Humans are drawn to those skewed outcomes. Even though the most likely outcome is a loss, and it is irrational. It is why lotteries, gambling, and speculative trading persist.
Low volatility investing is the opposite of meme-stock investing. It’s about winning by not losing. Batting for singles and doubles, but not going for home runs and striking out. A good low-volatility strategy can deliver the benefits of equity investing over the long run. While also providing better portfolio protection when the markets reprice during a downturn.
It’s beyond the scope of this article to get into the plethora of academic research around the low volatility anomaly, here is a good review article.
Factor ETF Implementation
Academic Observation to Real-Life Implementation
Low volatility investing is not a new concept. It has been around since 1972 through the work of famed Wall Street economist Fischer Black. However, it has captured more interest since the Great Financial Crisis (GFC) in 2008-09.
As broader indices like the S&P 500 fell sharply, less volatile stocks within the index better preserved their valuations — a trend that has remained largely intact since the GFC. This is an approach that attempts to achieve the benefits of equity investing (upside return), while mitigating the inherent downside volatility.
Even when a factor is recognized as important, capturing it may not be easy. Academic research often uses a long/short strategy. For low volatility, that would be long low-vol stocks and actively shorting high-vol stocks. Shorting carries extra risk and costs in real life. So does the cost required to screen, identify, and trade stocks. So, it is important to understand how a low-volatility ETF is constructed.
Constructing a Low-Volatility ETF
Targeting a factor is a quantitative approach. So, the methodology matters. A soundly constructed low volatility Exchange Traded Fund (ETF) will generally achieve this by overweighting defensive stocks (using some measurement of risk – commonly standard deviation or beta) as well as overweighting traditionally defensive sectors such as Consumer Staples and Utilities, while underweighting more aggressive stocks and volatile sectors, such as Energy and Materials.
Generally, investors recognize two measures of volatility, standard deviation, and beta. These measures have little to do with typical fundamental analysis of the company’s financial situation or valuation, and instead, look at past performance to determine how volatile the security has been.
Beta is a risk metric that measures an investment’s sensitivity to fluctuations in the broad market (market sensitivity). The broad market is assigned a beta value of 1.00, an investment with a beta less than 1.00 indicates the investment is less risky relative to the broad market. [I’ll make a diagram for this]
While beta measures a securities sensitivity to the market, standard deviation measures the standalone risk and can be used to compare stocks against each other in relation to their level of volatility.
It is best practice within low volatility strategies to look at a longer-term measure of volatility (5 years rather than 1 year). Volatility itself is volatile. This ensures that the low volatility characteristics the stock is exhibiting have been persistent. That decreases unnecessary trading within the ETF. One of the problems with implementing factor-based strategies is to keep costs below the expected factor premium. A well-designed ETF is one of the best ways to do that.
BMO’s Low Volatility ETF Strategies
The BMO ETF Low Volatility Strategy uses beta numbers as the primary investment selection and weighting criteria.
The beta used for weighting purposes is calculated using 5 years of market data, with 25% weight placed on the most recent year, 22.5%, 20%, 17.5% and 15% in prior successive years. The portfolios are rebalanced semi-annually, where security weights of the existing holdings are adjusted to reflect changes in their beta.
Even with factor investing, diversification is still important. To ensure a well-diversified portfolio and to prevent unintended biases occurring from high concentrations, the BMO ETF Low Volatility Strategy limits exposure to any one security to 10%. Furthermore, sector exposures in ZLB are limited to 35%, ZLU / ZLU.U / ZLH, ZLI / ZLD and ZLE are limited to 25%. In addition, ZLI/ZLD and ZLE also limits the exposure to any one country to 25%.
Evaluating a Low Volatility ETF
Different ETF providers will have different methodologies. There are also different factor models to evaluate factor loading. Even when you are transparent about strategy, it is important to see how it translates into action.
Bloomberg Risk Model
Below shows a chart with the current exposures to the main factors (using the Bloomberg risk model) for ZLB. As you see ZLB is clearly overweight the low volatility factor. It accomplishes its objective. Additionally, it underweights the size factor. This makes sense as the strategy is currently underweight the largest holdings in the Canadian large-cap index comparator. The other largest underweight is momentum. This can be attributed to the underweight in technology which has been in favour, and defensives being relatively eschewed over the past year. Momentum as a factor cuts in both directions, up and down, and it may shift very quickly.
Fama-French Five-Factor Weighting
There are different factor models that you may use or have access to. The Fama-French five-factor model is another famous one. Below is an analysis of the factor loading of ZLU (Low Volatility US Equity) vs the US market since inception to the time of writing this article done using portfoliovisualizer.com. As you can see, it has had significant market risk, profitability, and conservative management factor loading. That is unsurprising because profitability and conservative investment are associated with low volatility as a factor. It is functioning as intended.
Overall, that translated into an annualized alpha of 1%/yr over the time period (p-value=ns).2 It is important to re-iterate that past performance does not predict the future and that factors can shift in and out of favor over long cycles. [LD: Also of note, I used portfoliovisualizer, but different tools may come up with different loadings and alpha].3
Definitions: Rf=risk free rate of return at time; Rm=total market portfolio return at time; SMB=size premium (small minus big); HML=value premium (high minus low); RMW = profitability factor (Robust Minus Weak); CMA = investment factor – The difference between the returns on diversified portfolios of the stocks of low and high-investment firms; Standard Error – the standard deviation of its sampling distribution; t-stat – The ratio of the difference in a number’s estimated value from its assumed value to its standard error; p-value – measures the probability of obtaining the observed results, assuming that the null hypothesis is true.
Role of a Low Vol ETF in a Portfolio
As mentioned earlier, factor weighting can be used to change the risk-return profile of a portfolio. Loading with factors may increase risk-adjusted return, but it is not a free lunch. In the case of a low-volatility ETF, there is a risk when low-volatility stocks are trailing the broader market. You will be tempted to deviate from your plan, and if you do, miss when low volatility rotates back into favor. A low volatility strategy also tends to be overweight interest rate-sensitive stocks. Similar to bonds, they may have a headwind in a rising rate environment and a tailwind when rates drop.
In a market downturn, a low-volatility ETF may shine while other holdings are feeling more downside pressure. Dampening that downward volatility may help you to stick with your plan for the eventual ride back up. Another underappreciated aspect of low-volatility stocks is that they often pay higher dividends than the broader market. That tangible income stream may be psychologically soothing for investors waiting out a market drawdown. In the meantime, a low-volatility equity ETF still captures equity market risk exposure during the good times.
A well-designed portfolio contains a variety of holdings with different risks. You can diversify to minimize uncompensated risks. In addition to the standard diversification between asset classes, like stocks and bonds, diversification with different factor exposures may add a layer beyond simple market exposure. Low volatility is one of those factors, capturing some equity upside while dampening the downside. Not only returning more than market risk would suggest historically, but often appearing when you behaviorally need some psychological soothing the most. A well-constructed low-volatility ETF simplifies putting that strategy into practice.
ETF Market Insights Video
Footnotes
- Haugen and Heins (H&H) Study from 1926 to 1971, 1972. ↩︎
- ZLU annualized performance as of Mar 29 2024: 1 Year: 6.73%, 3 Year: 9.91%, 10 Year 12,26%, Since Inception: 13.64%. ↩︎
- One of my readers has a model that calculated an alpha of 0.29%/yr. Their methodology is here ↩︎
BMO Disclaimer
This article is sponsored by BMO ETFs.
Commissions, management fees and expenses all may be associated with investments in exchange traded funds. Please read the ETF Facts or prospectus of the BMO ETFs before investing. The indicated rates of return are the historical annual compounded total returns including changes in unit value and reinvestment of all dividends or distributions and do not take into account sales, redemption, distribution or optional charges or income taxes payable by any unitholder that would have reduced returns. Exchange traded funds are not guaranteed, their values change frequently and past performance may not be repeated.
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This article is for information purposes. The information contained herein is not, and should not be construed as, investment, tax or legal advice to any party. Particular investments and/or trading strategies should be evaluated relative to the individual’s investment objectives and professional advice should be obtained with respect to any circumstance.
This is great info. It seems a good strategy would be to augment an asset allocation ETF with a factor ETF. For example, using a core holding of 80-90% in a 60/40 asset allocation ETF like ZBAL or VBAL then tilting with a 10-20% holding of ZLB. One would have to be mindful that this would bump up the Canadian equity allocation.
Is this your approach or do you replace your entire Canadian equity portion with ZLB (which would necessitate constructing your own portfolio rather than using an asset allocation ETF)?
I tilt by by keeping the same geographic exposure. For example, I reduce some of my ZCN to use bit of ZLB. Similarly, I use some AVUV (US small cap value) AVDV (non-North America) and regular ETFs for the core. ZLB was the best one I found for Canada – the others are US-listed. So, adds a layer of complexity compared to using an all-in-one. If using an all-in-one, you could use a Canadian, US, and non-US ETF in a similar proportion to you desired Can/US/non-NA mix, but still more complex than an all-in-one on its own.
-LD