How to Screen Stocks Using Quality, Value, and Momentum Factors

Most retail investors screen stocks the same way: they sort by P/E ratio, maybe glance at revenue growth, and call it a day. The result is a portfolio built on one dimension of a multi-dimensional problem. Factor investing offers a more rigorous alternative, one that distills decades of academic research into a repeatable, systematic process.

The core idea is straightforward. Certain measurable characteristics of stocks, called factors, have historically predicted excess returns. The three most robust factors, validated across markets and time periods, are Quality, Value, and Momentum. Used together, they form a powerful composite screen that captures what no single metric can.

What Factor Investing Actually Means

Factor investing sits between passive indexing and active stock picking. Instead of buying the whole market or relying on gut-feel analysis, you tilt your portfolio toward stocks that share characteristics historically associated with higher returns. The intellectual foundation goes back to the early 1990s, when Eugene Fama and Kenneth French demonstrated that small-cap and value stocks systematically outperformed the market over long periods. Since then, researchers have identified dozens of potential factors, but only a handful survive rigorous out-of-sample testing.

Quality, Value, and Momentum are the three that practitioners and academics consistently agree on. Each captures a different dimension of a stock’s attractiveness, and, critically, they tend to be uncorrelated with each other. When value stocks struggle, momentum often picks up the slack. When high-flying momentum names crash, quality holdings provide ballast. That diversification of factor exposure is what makes the combination so powerful.

“It’s far better to buy a wonderful company at a fair price than a fair company at a wonderful price.”

The Quality Factor: Is This a Good Business?

Quality measures the fundamental health and competitive strength of a business. High-quality companies generate strong returns on invested capital, maintain clean balance sheets, and convert accounting profits into real cash flow. The academic case for quality was cemented by Robert Novy-Marx in 2013, who showed that gross profitability (gross profit divided by total assets) predicts returns as well as traditional value metrics.

In practice, a quality screen should examine several dimensions. Return on Invested Capital (ROIC) reveals how efficiently management deploys shareholders’ capital. The Piotroski F-Score, a nine-point checklist covering profitability, leverage changes, and operating efficiency, identifies companies whose financial health is improving. Leverage ratios (debt-to-equity, interest coverage) measure fragility. A company earning 20% ROIC with modest debt is fundamentally different from one earning the same return financed by borrowed money.

The key insight is that quality acts as a risk filter. It keeps you out of companies that look cheap for good reason: those with deteriorating fundamentals, excessive leverage, or accounting that flatters reality. Without a quality overlay, value screens tend to catch falling knives. Lots of them.

The Value Factor: Am I Paying a Fair Price?

Value compares what you pay (market price) to what you get (fundamental anchors like earnings, cash flow, book value, and sales). The premise is old. Benjamin Graham was writing about it in the 1930s. But the evidence is overwhelming: across virtually every equity market studied, cheaper stocks outperform expensive ones over the long term.

The mistake most screeners make is relying on a single valuation metric. Price-to-Earnings (P/E) is the most popular, but it fails for cyclical companies, loss-making growth stocks, and businesses that manipulate earnings through accounting choices. A robust value screen uses multiple ratios: P/E, Price-to-Book, Price-to-Free-Cash-Flow, Price-to-Sales, and EV/EBITDA. Averaging across these metrics smooths out the idiosyncrasies of any single measure.

Critically, valuation must be assessed relative to sector peers. A software company trading at 25x earnings might be cheap for its sector, while a utility at the same multiple is expensive for its sector. Sector-relative percentile scoring solves this by comparing apples to apples. Each stock is ranked against others in its industry, not the entire market.

“Price is what you pay. Value is what you get.”

The Momentum Factor: What Direction Is the Market Voting?

Momentum is the empirical observation that stocks which have been rising tend to continue rising, and stocks which have been falling tend to continue falling. Jegadeesh and Titman documented this effect in 1993, and subsequent research has confirmed it across geographies and asset classes. The explanations vary, from behavioural biases like anchoring and herding to slow information diffusion and institutional constraints. But the pattern persists.

A well-constructed momentum signal looks at returns over 3, 6, and 12 months, typically excluding the most recent month to avoid short-term reversal noise. Earnings momentum matters too: upward analyst estimate revisions and positive earnings surprises are powerful leading indicators of continued price appreciation. Momentum captures what the market already knows, or is in the process of learning, about a stock’s prospects.

The danger of momentum alone is that it can lead you into overvalued names riding a hype cycle. A stock that scores 95 on quality and 12 on value is what happens when everyone else noticed the moat before you did. This is precisely why momentum works best when combined with quality and value. You want momentum behind fundamentally sound, reasonably priced companies, not momentum behind the latest speculative frenzy.

How to Combine the Three Factors

The simplest and often most effective approach is equal weighting: one-third Quality, one-third Value, one-third Momentum. Research by Asness, Frazzini, and Pedersen has shown that multi-factor combinations reduce drawdowns by 30 to 40 percent compared to single-factor strategies. The diversification benefit comes from low or negative correlations between the factors themselves.

The process works in four steps. First, calculate each stock’s raw metrics (ROIC, P/E, 12-month return, etc.). Second, convert these into sector-relative percentile ranks, so every stock is scored 0 to 100 against its peers. Third, compute the composite by averaging the three pillar scores. Fourth, re-percentile the composite to spread the final distribution evenly across the full range. This counteracts the Central Limit Theorem’s tendency to bunch averaged percentiles toward 50.

The output is a single score, a StockRank, that captures the multi-dimensional attractiveness of each stock. A StockRank of 90 means the stock is in the top decile across all three factors within its sector. That’s the whole game. Our methodology page details the exact factor weights and scoring mechanics.

Common Mistakes That Derail Stock Screeners

Screening on absolute thresholds. Setting a hard cutoff like “P/E below 15” eliminates entire sectors (technology, healthcare) regardless of whether individual stocks are cheap relative to their peers. Use percentile ranks, not absolute thresholds.

Ignoring missing data. When a stock lacks a data point, most screeners either exclude it entirely (survivorship bias) or leave the field blank (inflating the average). A disciplined approach assigns a below-median penalty score for missing data, ensuring no stock gets a free pass for incomplete filings.

Over-optimising on historical backtests. It is trivially easy to design a factor screen that performed brilliantly in the past. The market has a way of humbling backtests that looked brilliant on a spreadsheet. The real test is out-of-sample performance: does it work on data it has never seen? Walk-forward decile analysis, where you track whether higher-ranked stocks actually outperform lower-ranked ones in subsequent periods, is the gold standard for validation.

Running a screen once and forgetting it. Scores change daily as prices move and new earnings data arrives. A stock that ranked in the top decile last month may have deteriorated. Markets do not pause while you check in quarterly. Systematic screening means running the model regularly and acting on updated signals, not on stale snapshots.

“In the short run, the market is a voting machine but in the long run, it is a weighing machine.”

Putting It Into Practice

Building a multi-factor screener from scratch is feasible but labour-intensive. You need reliable fundamental data for thousands of stocks, a normalisation framework, and the discipline to run it consistently. MoatMap’s Ranked Stocks does this across 9,000+ stocks in 19 markets, scoring each on Quality, Value, and Momentum daily. You can sort, filter by sector and geography, and drill into any stock’s factor breakdown.

Already have a portfolio you built the old-fashioned way? Portfolio X-Ray lets you upload your holdings and see how they score against the StockRank framework. It’s a quick way to find positions that might be dragging down your overall factor exposure.