Stock Screener

The most useful filters depend on your investment style, but certain filters are universally valuable because they address the core dimensions of any stock: size, valuation, quality, and sector. Here are the filters that matter most and why:

Market capitalization:

• Mega-cap (>$200B) — The largest, most liquid companies. Think Apple, Microsoft, Amazon. These tend to be stable, well-covered by analysts, and move less on any single piece of news.
• Large-cap ($10B–$200B) — Mature companies with established market positions. Good balance of growth potential and stability.
• Mid-cap ($2B–$10B) — Often the sweet spot for investors seeking growth with reasonable risk. Less analyst coverage means more opportunities for mispricing.
• Small-cap ($300M–$2B) — Higher growth potential but higher volatility and risk. Less institutional coverage, thinner trading volumes.
• Micro-cap (<$300M) — The least liquid, most volatile tier. Can be fertile ground for deep value investors willing to do primary research.

Sector and exchange:

• Sector filter — Narrows to a specific industry (Technology, Healthcare, Energy, etc.). Useful when you have a macro thesis or want to build sector expertise.
• Exchange filter — NYSE vs. NASDAQ have different listing standards and company profiles. NASDAQ tends to skew towards technology and growth; NYSE towards industrials and financials.

The best approach is to start with fewer filters and gradually tighten them. If your initial screen returns 500 results, add another constraint. If it returns 3, loosen something. A good screen typically returns 20–50 candidates for further research.

Market capitalization (market cap) is arguably the single most important screener filter because it determines the universe of companies you are working with. Market cap equals the stock price multiplied by total shares outstanding, and it serves as a proxy for company size, liquidity, risk profile, and investor base.

Why market cap matters so much for screening:

• Analyst coverage — Mega-cap and large-cap stocks are covered by dozens of Wall Street analysts, making it harder to find an informational edge. Small-cap and micro-cap stocks may have zero or minimal coverage, creating more opportunities for mispricing but also more risk of unknown negatives.
• Liquidity — Larger companies trade millions of shares per day, meaning you can enter and exit positions easily. Smaller companies may have wide bid-ask spreads and low daily volume, making it costly to build or exit a meaningful position.
• Institutional ownership — Many institutional investors (pension funds, large mutual funds) cannot invest in stocks below a certain market cap due to mandate restrictions. This means small-cap stocks are under-owned by institutions, which can create both opportunity and volatility.
• Growth vs. stability tradeoff — Historically, small-cap stocks have delivered higher returns over long periods but with significantly higher volatility. Large-cap stocks offer lower returns but smoother rides. Your market cap filter should match your risk tolerance.

A common mistake is screening only mega-cap names because they feel "safer." While true in terms of volatility, the most undervalued opportunities tend to cluster in the mid-cap and small-cap space where fewer professional investors are looking. Consider running the same fundamental screen across different market cap ranges to see how results differ.

The terms stock screener and stock scanner are often used interchangeably, but they serve different purposes in the investment workflow. Understanding the distinction helps you pick the right tool for your needs.

Stock screener:

• Fundamental-focused — Screeners typically filter on fundamental data: market cap, P/E ratios, revenue growth, margins, dividend yield, sector, and exchange. They answer the question: "Which stocks have the financial characteristics I am looking for?"
• Longer time horizon — Results from a screener change slowly because fundamentals change quarterly. A value screen might produce the same list for weeks or months until earnings reports shift the data.
• Research pipeline tool — Screeners sit at the top of the research funnel. The output is a candidate list for deeper analysis, not a list of immediate trades.

Stock scanner:

• Technical and real-time focused — Scanners typically filter on price action, volume spikes, moving average crossovers, RSI, MACD, and other technical indicators. They answer the question: "What is moving right now?"
• Shorter time horizon — Scanner results change throughout the trading day. A stock that appears on a volume spike scan at 10 AM may disappear by noon.
• Trading tool — Scanners are used by active traders to find intraday setups, momentum plays, or unusual activity that signals a potential trade.

For fundamental investors building DCF models and long-term positions, a stock screener is the right tool. It helps you identify companies with the right financial profile before you invest the time in a full valuation analysis. This tool is a fundamental screener — it filters on the characteristics that matter for long-term investing.

Passing a stock screen is step one of a multi-step research process. A screen tells you a stock has the right quantitative profile, but it cannot tell you about competitive moats, management quality, industry dynamics, or whether the current price represents a genuine opportunity. Here is how to turn screener output into actionable investment decisions:

Step 1: Triage the list

• Scan company names and sectors. Eliminate companies in industries you do not understand or are unwilling to research. You should be able to explain, in one sentence, how each remaining company makes money.
• Look for names you recognize vs. names you have never heard of. Unknown names are where the mispricing opportunities tend to live — but they also require more work to evaluate.

Step 2: Quick fundamental check

• Use tools like our Financial Health Scorecard and Valuation Snapshot to quickly assess each candidate. Look for companies with strong balance sheets, consistent profitability, and reasonable valuations.
• Compare each stock against its peers using the Peer Ranker to see if its metrics stand out within the industry. A stock that looks cheap on an absolute basis may be expensive relative to better-positioned peers.

Step 3: Build a DCF model

• For your top 3–5 candidates, build a discounted cash flow model to estimate intrinsic value. A DCF model forces you to make explicit assumptions about growth, margins, and risk — which is exactly the discipline that separates good investors from gamblers.
• Use the Reverse DCF tool to see what growth rate the market is already pricing in. If the market implies 5% growth and you believe the company can grow at 15%, you may have found an opportunity.

The entire pipeline — screen, check, compare, model — can be completed in an afternoon. Most investors skip the modeling step, which is exactly why it provides an edge.

Stock screeners are powerful tools, but they can mislead you if used without care. Here are the most common pitfalls and how to avoid them:

Mistake 1: Over-filtering

• Setting too many tight filters at once can eliminate every good stock. A company might score perfectly on 9 out of 10 metrics but fail one filter by a tiny margin. Start with 2–3 core filters and add more only if the results are too broad. The ideal screen returns 20–50 stocks.

Mistake 2: Confusing screening with analysis

• A stock passing your screen does not mean it is a good investment. It means it has the right quantitative profile for further research. The screen is a filter, not a verdict. You still need to understand the business, evaluate management, and model intrinsic value before committing capital.

Mistake 3: Ignoring why a stock looks cheap

• Value screens surface companies with low multiples, but low multiples often exist for a reason: declining revenues, deteriorating margins, legal risk, or industry headwinds. The stock is not necessarily a bargain — it might be cheap because it deserves to be. Always investigate the "why" behind the numbers.

Mistake 4: Screening only once

• Markets change constantly. A stock that did not pass your screen last quarter might pass it this quarter after a price decline or earnings improvement. Run your screens regularly (monthly or quarterly) to catch new opportunities as they emerge.

Mistake 5: Relying on a single data source

• Financial data can contain errors, especially for smaller companies. Cross-check key numbers against SEC filings or a second data provider before making investment decisions based on screener output. Treat screener data as directional, and verify before you invest.

Sector selection is one of the most impactful screener filters because different sectors have fundamentally different financial profiles. A P/E ratio of 30x might be cheap in fast-growing Technology but expensive in slow-growing Utilities. Screening within a sector ensures you are comparing apples to apples.

Key sector characteristics for screening:

• Technology — High growth, high margins, high valuations. P/E ratios of 25–40x are common. Screen for revenue growth and gross margins rather than P/E, since many tech companies reinvest aggressively.
• Healthcare — Split between high-growth biotech (unprofitable, pre-revenue) and stable pharma (high margins, dividend payers). Market cap filter is critical here to separate speculative biotech from established pharma.
• Financial Services — Banks, insurance, and asset managers have unique balance sheets. Traditional metrics like P/E and price-to-book work well, but gross margin is less meaningful.
• Consumer Cyclical — Retailers, automakers, and travel companies. Highly sensitive to economic cycles. Screen during downturns for potential recovery plays.
• Energy — Commodity-driven, so margins and profitability swing with oil and gas prices. Free cash flow yield is more useful than P/E for energy companies.
• Utilities and Real Estate — Stable, dividend-focused sectors. Dividend yield and payout ratio are the key metrics. These sectors attract income investors, not growth investors.

When to use sector filtering: If you have a macro thesis (e.g., "AI will drive technology spending for the next decade"), filter to the relevant sector. If you are sector-agnostic and just want the best fundamental profiles across the market, leave the sector filter set to "Any" and let the numbers speak for themselves.

Combining a stock screener with DCF valuation creates a systematic, repeatable investment process. The screener handles the top-of-funnel filtering, and the DCF model provides the deep, bottom-up valuation that determines whether a stock is actually worth buying at its current price.

The optimal workflow:

• Step 1: Screen — Set your filters (market cap, sector, exchange) and generate a candidate list. Aim for 20–50 results. If you get fewer than 10, loosen your filters. If you get more than 100, tighten them.
• Step 2: Quick check — Click through to each stock's Valuation Snapshot and Financial Health Scorecard. Eliminate companies with obvious red flags: weak balance sheets, declining revenue, negative cash flow, or governance concerns.
• Step 3: Reverse DCF — For your remaining candidates (ideally 5–10), run a Reverse DCF to see what growth the market is pricing in. Focus on stocks where the implied growth is significantly lower than what you believe the company can achieve.
• Step 4: Full DCF model — For your top 3–5 candidates, build a full DCF model with explicit revenue, margin, and reinvestment assumptions. This is where you calculate your own estimate of intrinsic value and compare it to the current market price.
• Step 5: Margin of safety — Only invest in stocks where your DCF fair value is at least 20–30% above the current price. This margin of safety accounts for estimation error and unforeseen risks.

This workflow takes an afternoon, and it gives you a level of conviction that no screener alone can provide. You are not just buying stocks that "look cheap" — you are buying stocks where you have done the math and believe the market is wrong.

Our DCF model builder generates professional Excel models with working formulas, making Step 4 significantly faster than building a model from scratch.