What Is Quantitative Investing? A Beginner's Guide.

What Is Quantitative Investing?

Quantitative investing is what it sounds like - using measurable data and a predefined methodology to decide what to buy, how much of it, and when to change it. No forecasting, no "I have a good feeling about this sector." Just criteria applied the same way to every security in the universe, every time.

People hear "quant" and think algorithms and AI, but the concept is older and simpler than that. A spreadsheet with a ranking formula is quantitative investing. So is a hedge fund's multi-factor model. The complexity varies, the underlying idea doesn't - the strategy evaluates things like earnings growth, valuation ratios, volatility, or profitability, and it does it consistently instead of case-by-case.

How it actually works

Every quant strategy starts by defining a universe - S&P 500, Canadian equities, dividend payers, whatever the strategy is built around. Then it pulls data for everything in that universe: prices, financials, earnings, whatever the methodology needs. The methodology itself decides who qualifies - maybe it's ranking on valuation, maybe momentum, maybe quality metrics - and because the criteria are fixed in advance, every security gets judged the same way. No exceptions made because a company has a good story.

From there the strategy builds the actual portfolio: what's in, how many holdings, how it's weighted, any diversification limits. And it gets reviewed on a schedule - monthly, quarterly, annually, however the methodology defines it - applying the same rules again with fresh data. That's really the whole loop: define, measure, apply, construct, review, repeat.

Quant vs. fundamental investing

Fundamental investing runs on human judgment - someone's assessing management quality, competitive position, industry outlook, and two analysts can look at the same company and land in different places. That's not a flaw, it's just how discretionary analysis works.

Quant strips that out. The methodology evaluates everything the same way regardless of who's looking at it. Neither approach is inherently better - plenty of managers blend both - but they're solving the "how do I decide" problem completely differently.

The common strategy types, briefly

Value strategies rank on valuation - P/E, P/B, that kind of thing. Momentum strategies favor stocks with strong recent price performance. Quality strategies favor consistent profitability and strong balance sheets. Low-vol strategies favor stocks that have historically moved less than the market. Equal-weight strategies just split allocation evenly instead of weighting by market cap. Multifactor strategies blend several of these into one methodology. None of these are mutually exclusive - a lot of real-world strategies combine two or three.

What quant gets you, and where it falls short

The upside is consistency and transparency - the same rules apply no matter what the market's doing or how anyone's feeling that week, and because the criteria are defined up front, you can actually see and evaluate what the strategy is doing instead of trusting a black box of opinions. It scales too - a methodology built for 50 stocks works the same way applied to 500.

The catch is it's only as good as the data feeding it, and historical relationships don't always hold going forward - a factor that worked well for a decade can go quiet or even reverse. And obviously no methodology, however rigorous, guarantees it'll beat the market. It can underperform just like anything else.

Worth separating from two things people often conflate it with: quant investing is about how decisions get made, not how trades get executed - that's algorithmic trading, a different layer entirely. And it's not synonymous with AI either - most quant strategies run on straightforward statistical models and financial screens, no machine learning involved.

How Persistfolio uses this

Persistfolio's model portfolios are built through a proprietary rules-based methodology - the universe gets screened against predefined quantitative criteria, and what comes out is published the same way for every subscriber. No discretionary calls, no personalization based on individual circumstances.

And we don't just show you a backtest and call it a day - every published model gets tracked prospectively against the S&P 500, so what you're seeing is what actually happened after publication.

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