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Recent market volatility and macroeconomic uncertainty have led many investors to consider low-volatility strategies. The goal is to minimize the impact of performance swings and downside risk on the portfolio.
To achieve this, investors should focus on overall portfolio risk and not narrow their focus on heuristic-based volatility, such as beta and standard deviation. These strategies typically help reduce the drawdowns that accompany volatility but tend to underperform during market rallies.
The optimal portfolio risk approach should consider tail risk, including how investments perform relative to their benchmarks during both good times and bad.
There are two traditional portfolio construction approaches to implementing low-volatility investing strategies: heuristic and optimization-based.
Traditional low-vol approaches |
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Heuristic-based strategies |
Optimization-based strategies |
Mackenzie low-vol quant strategy |
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• Rank the universe based on standard deviation or beta. |
• Use a numerical optimizer to develop a portfolio with the lowest total risk using an estimated security covariance matrix. |
• Based on a proprietary data warehouse, the manager constructs an investable universe covering large and mid-cap companies. |
The pitfalls of single dimension ETF methodologies
Investors might be tempted to opt for low standard deviation or beta solutions that invest in low-volatility stocks because of their straightforwardness and downside protection potential. However, these single-dimensional low-volatility strategies are typically weak diversifiers and underperform the market over the long term.
Mackenzie’s low-vol quantitative strategy seeks enhanced minimum volatility. Using a proprietary data warehouse, the Mackenzie Global Quantitative Equity (GQE) Team constructs an investable universe covering large and mid-cap companies. These stocks are then grouped into comparable sub-groups, such as sector, industry, etc.
A proprietary alpha selection model is applied to rank the companies based on individual factors that suggest they may outperform their peers. Constraints are applied to neutralize extraneous exposures to sector, country, market cap and style. Portfolio weights are allocated according to each company’s estimated alpha and risk minimization potential within the portfolio.
This strategy is designed to avoid the pitfalls of single-dimension methodologies.
Pitfalls of single dimension low volatility approach |
Mackenzie’s quant low volatility approach |
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1 |
Single-dimensional view of risk (individual stocks risk) |
Multi-dimensional risk approach (risk at the portfolio’s level) |
2 |
Exposure tilted towards defensive sectors |
Constraints on sector deviation from the benchmark |
3 |
Analysis relies solely on historical statistical risk factor (backward looking) |
Ability to generate alpha is considered in the model (forward looking) |
4 |
Limited up-capture |
Aims for up-capture higher than the low beta and standard deviation strategies but slightly lower than the broad market |
Single dimension low volatilitySingle-factor view of risk The low beta and standard deviation methods have a one-dimensional view of risk, focusing only on individual securities’ volatility. The correlation between holdings and the total portfolio’s volatility is an important risk dimension that is not considered in the single-dimension volatility methods. |
Mackenzie approachMulti-dimension view of risk Mackenzie GQE team follows a multi-dimensional minimum volatility approach, which accounts for both volatility and correlation between the individual holdings to minimize the portfolio’s overall risk. |
Single dimension low volatilityExposure tilted towards defensive sectors Low beta and standard deviation screens typically result in overweight exposure in defensive sectors (consumer staples and utilities), heightening the concentration risk and lowering the long-term return as these sectors are not performance drivers. |
Mackenzie approachConstraints on sector deviation from the benchmark Mackenzie GQE team mitigates concentration risk by limiting the sector weights relative to the benchmark (+/- 3% for MWLV, +/- 2% for MCLV, and +/- 2% for MULV). |
Single dimension low volatilityAnalysis relies solely on historical statistical risk factor (backward looking) The low beta and standard deviation approaches rely solely on past statistical volatility data. |
Mackenzie approachAbility to generate alpha is considered in the model (forward looking) Mackenzie GQE low volatility approach combines low volatility and a proprietary alpha selection model is applied to rank the companies based on individual factors that outperform their peers. |
Single dimension low volatilityLimited up-capture The low beta and standard deviation stocks have inherently high exposure to defensive sectors (such as utilities, staples, and real estate), which typically underperform the broad market during sharp upturns. This results in a relatively low up-capture ratio. |
Mackenzie approachAims for up-capture higher than the low beta and standard deviation strategies but slightly lower than the broad market Mackenzie's portfolio optimization process combines a proprietary alpha selection model with risk constraints to deliver reduced volatility without sacrificing long-term growth. |
Pitfalls and solutions
ETF name |
Ticker |
Management fee |
Mackenzie World Low Volatility ETF |
MWLV |
0.50% |
Mackenzie Canada Low Volatility ETF |
MCLV |
0.45% |
Mackenzie US Low Volatility ETF |
MULV |
0.45% |
ETF managed by: Mackenzie Global Quantitative Equity Team
Arup Datta, MBA, CFA
SVP, Portfolio Manager, Head of Team
Nicholas Tham, CFA
VP, Portfolio Manager
Denis Suvorov, CFA
VP, Portfolio Manager
Haijie Chen, PhD, CFA
VP, Portfolio Manager
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