Quantitative deep dive: Tailor beta exposures with an alpha-focused approach

In a market environment increasingly defined by dispersion, regime shift, and heightened risk scrutiny, family offices and investment counsellors are asking harder questions about how returns are generated, in addition to where they come from. Product proliferation has made access easier than ever, but it has also made it difficult to distinguish between surface-level differentiation and genuinely distinct investment processes.

At BMO Global Asset Management (BMO GAM), the Quantitative Investment team approaches the challenge of creating durable investment processes through a rigorous model that is applied across mandates — a single, systematic investment engine, refined and tested since 2010 — that is deliberately adapted through individual portfolio constraints to deliver a wide range of investment outcomes. 

The result is a set of strategies that are uniquely differentiated (Table 1), yet all draw from the same disciplined core.

Table 1. One methodology, many strategies

Source: BMO Global AM Inc., as of December 31, 2025. 

A philosophy built for consistency

The foundation of BMO GAM’s quantitative platform rests on a simple belief: markets are inefficient, not random. Investor behaviour — anchoring, overreaction, crowding — creates patterns that can persist through time. Exploiting those inefficiencies requires a repeatable framework that can systematically separate signal from noise.

That is why the team combines fundamental insights with quantitative implementation. Company quality, valuation, and earnings sustainability matter — and are expressed through disciplined modelling. This has allowed the process to remain consistent across geographies, market cycles, and investment formats for more than 15 years.

For allocators such as family offices, this consistency is vital, reducing ​“key-person” risk, improving transparency and allowing capital to be deployed across mandates without having to relearn a new process each time.

The alpha engine: core stock selection

Every strategy begins with a clearly defined universe of liquid, investable securities. Depending on the mandate, the starting point may be the S&P/TSX Composite, the S&P 500, or the MSCI EAFE Index. 

This is not a trivial step. Universe definition sets the boundaries for capacity, liquidity, and risk control. From there, only securities that meet liquidity and investability thresholds are included. It is discipline that translates into scalability and alpha generation. 

Each security in the investable universe is evaluated across a diversified set of factors that our research has shown to be associated with long-term outperformance. These include profitability, earnings quality, management quality, valuation, momentum, and investor sentiment. The result is a composite alpha score that ranks companies from most to least attractive.

The Dividend ETF methodology, for example, provides a clear illustration of this in action (Diagram 1). Its multi-faceted alpha screen evaluates companies on fundamental strength and valuation before any income-specific filters are applied. Stocks with the strongest scores emerge as candidates for portfolio inclusion, while those with weaker characteristics are systematically deprioritized. 

Diagram 1. Initial screening process, BMO Dividend ETFs

This same alpha ranking drives selection in other quantitative strategies, including long-short, except in those portfolios, negative signals are monetized rather than ignored. Stocks expected to underperform are sold short, allowing the model to extract value from across the spectrum of market inefficiencies. 

Once stocks are ranked, the process shifts from selection to risk assessment. Risk controls are applied after alpha scores are generated, and evaluated across multiple dimensions, including factor exposure, macro sensitivity, and company-specific fundamentals. The goal is not to eliminate risk, but to ensure that it is intentional and consistent with each mandate’s objectives.

In dividend strategies, for example, risk controls include sector weight limits, security concentration thresholds, and ongoing monitoring of dividend sustainability. These constraints help prevent unintended tilts while preserving the integrity of the alpha signal. 

Portfolio construction: unified methodology, many strategies

Using optimization techniques, the model seeks to maximize expected risk-adjusted returns¹ while controlling tracking error, turnover, and transaction costs. From there, strategy-specific constraints are applied to shape final outcomes:

Core equity strategies target market-like exposures with modest tracking error. Dividend strategies introduce income-focused constraints. Low volatility (low vol) portfolios emphasize beta reduction. Long/​short strategies explicitly cap market exposure at approximately 50%. Diagram 2 below illustrates how this approach is applied to low-vol portfolio construction: 

Diagram 2. Low volatility portfolio construction

The key point is that stock selection remains consistent. What changes are the constraints. For allocators, this means different risk profiles can be combined without introducing competing investment philosophies.

For family offices and investment counsellors, the appeal of this framework lies in its clarity. Alpha is centralized while outcomes are engineered through strategy-specific constraints. This makes it easier to construct portfolios across client objectives — growth, income, defense — without introducing conflicting philosophies. It also enhances due diligence, as each strategy can be evaluated through the same methodological lens.

Since 2010, the Quantitative Investment team has developed and implemented a platform capable of delivering multiple outcomes without compromising discipline, which may be the most compelling feature of all: when markets change — as they always do — strength of process often determines success.

Please contact your BMO institutional sales partner for additional market insights.

^{1 Return (risk-adjusted): A measure of investment performance taking into consideration how much risk/​volatility was assumed to generate it. Consider two investments, both of which return 10% over a given time period. The investment with the greater risk-adjusted return would be the one that experienced less price fluctuation. Two of the most commonly used measures of risk adjusted returns are Sharpe and Sortino ratios.}

^{2 About the Fundata FundGrade A + Rating FundGrade A+® is used with permission from Fundata Canada Inc., all rights reserved. The annual FundGrade A+® Awards are presented by Fundata Canada Inc. to recognize the ​“best of the best” among Canadian investment funds. The FundGrade A+® calculation is supplemental to the monthly FundGrade ratings and is calculated at the end of each calendar year. The FundGrade rating system evaluates funds based on their risk-adjusted performance, measured by Sharpe Ratio, Sortino Ratio, and Information Ratio. The score for each ratio is calculated individually, covering all time periods from 2 to 10 years. The scores are then weighted equally in calculating a monthly FundGrade. The top 10% of funds earn an A Grade; the next 20% of funds earn a B Grade; the next 40% of funds earn a C Grade; the next 20% of funds receive a D Grade; and the lowest 10% of funds receive an E Grade. To be eligible, a fund must have received a FundGrade rating every month in the previous year. The FundGrade A+® uses a GPA-style calculation, where each monthly FundGrade from ​“A” to ​“E” receives a score from 4 to 0, respectively. A fund’s average score for the year determines its GPA. Any fund with a GPA of 3.5 or greater is awarded a FundGrade A+® Award. For more information, see http://www.fundgradeawards.com. Although Fundata makes every effort to ensure the accuracy and reliability of the data contained herein, the accuracy is not guaranteed by Fundata. The FundGrade A+ Awards and the FundGrade Ratings being referenced are calculated based on comparisons of performance of investment funds within a specified category established by the CIFSC.}

ETF performance (%)

Source: BMO Global Asset Management, as of December 31, 2025. Past performance is not indicative of future results. 

Fund performance (%)

Source: BMO Global Asset Management (F Series), as of December 31, 2025. Past performance is not indicative of future results.