Building a value stock scanner is part engineering project, part investment craft. You are encoding a philosophy into code, then stress testing it against messy market reality. Done right, it can help you find stocks others overlook, surface opportunities with attractive risk and reward, and reduce the noise that swamps most investors. The goal is not to build a machine that spits out the best stocks to buy now every day. The goal is to create a disciplined way to find stocks that merit deeper research.
I have run versions of this project for more than a decade, from a clumsy spreadsheet to a production-grade pipeline. Tools changed, but the core remained the same: define value clearly, source reliable data, clean it, calculate consistent metrics, rank and filter, then review candidates with human judgment. The details below reflect the scars and shortcuts I’ve picked up along the way.
Start with a definition you can defend
Value investors disagree on what “value” means. Some want low price to book, others prefer cash flow yields, some focus on high returns on capital at a modest price. Your scanner will only be as good as the definition it encodes.
When I began, I tried to pack every metric under the sun into one monolithic score. That produced a smooth output that hid important trade-offs. Now I start with a tight core: valuation, quality, and risk. Think of valuation as how cheap a company looks, quality as how durable its profits are, and risk as the chance your thesis breaks.
The mix you choose should fit your strategy and holding period. A deep-value buyer might tolerate lumpy results and heavier cyclicality. A quality-at-a-reasonable-price investor will accept paying up a little for strong margins and clean balance sheets. Write your philosophy down. If you can’t explain it to a colleague in three minutes, simplify it until you can.
Data sources and the cleanup you cannot skip
You need structured fundamentals and prices. For fundamentals, the usual suspects include financial statement databases and APIs that cover income statements, balance sheets, cash flow statements, and company metadata. For prices, choose a source with adjusted historical data that properly accounts for splits and corporate actions. If you can’t pay for commercial data, you can prototype with free or freemium sources, but you’ll spend more time cleaning and validating.
Data hygiene decides whether your scanner surfaces durable bargains or dumpster fires. Watch for:
- Restated financials that shift historical numbers. Tag filing dates and prefer the latest amended data when possible. Outliers and missing values. A single extraordinary gain can make P/E ratios useless for a year. Detect and cap outliers or switch to multi-year medians. Units and currency. Revenue in thousands vs actual units, and reports in EUR alongside USD prices, can produce nonsense if you don’t convert consistently. Survivorship bias. If you backtest, include delisted companies to avoid flattering results. Sector and industry tags that drift. Mergers, spinoffs, and reclassifications happen. Refresh your classifications regularly if you use sector-relative ranks.
I allocate at least a third of the build time to data validation. It feels slow, but nothing burns confidence faster than a list of ideas contaminated by broken tradeideascoupon.com inputs.
Build the skeleton: universe, schedules, and storage
Your stock screener needs a defined universe. Most retail investors start with major exchanges. You can narrow by minimum market cap and liquidity to avoid names that you cannot actually trade. For a value scanner, I like to include mid caps and small caps because that’s where mispricing persists, but I enforce a liquidity floor so execution doesn’t swamp returns.
Set a schedule that matches your metrics. Fundamentals update quarterly, with occasional preannouncements. Prices update daily. Dividends, buybacks, and share counts drift across quarters. In practice, I run a full fundamental refresh after each earnings cycle and a nightly price and market cap update for ranking. If you use rolling multi-year averages, you can update less frequently without losing signal.
As for storage, a relational database keeps things tidy if you are working at scale. For a lean build, a set of versioned parquet files with clear schemas works fine. The key is reproducibility. You should be able to regenerate yesterday’s output with yesterday’s data. That discipline pays off when you debug a ranking anomaly months later.
Choose and define your core metrics
The simplest value metrics measure price against fundamentals. They are blunt instruments, yet they work as a first pass when combined thoughtfully. Below is a set that has served me well across cycles. Each metric includes a nuance that prevents common traps.
Valuation:
- EV to EBIT or EV to EBITDA. EV normalizes for debt, which matters in capital-heavy industries. EBIT is stricter, EBITDA is more forgiving. I prefer EV to EBIT when I can get clean depreciation data and EV to EBITDA when capex is highly variable. Price to free cash flow. Use trailing twelve months and also a three-year median to reduce one-off distortions. Free cash flow is cash from operations minus capital expenditures, not “adjusted” numbers from presentations. Price to tangible book. Useful for asset-heavy sectors like financials or manufacturing. Exclude goodwill to avoid paying for past acquisitions twice.
Quality:
- Return on invested capital. Define it consistently: NOPAT divided by average invested capital. If you cannot compute NOPAT precisely, use operating income after taxes as a proxy. High ROIC with stability year over year is a strong positive. Gross margin and its trend. Gross margin compressions can be early warnings of competitive pressure. I flag names with three-year gross margin declines. Accruals ratio. Cash earnings are sturdier than accrual-based profits. Higher accruals often precede write-downs or disappointments.
Risk and durability:
- Leverage ratios, such as net debt to EBITDA and interest coverage. I prefer interest coverage because it reflects prevailing rates. Watch for capitalized leases that inflate EBITDA. Revenue concentration if data allows. Companies with one or two dominant customers carry hidden cliff risk. Share count trend. Persistent dilution can erase per-share gains even when revenue grows.
Momentum, used carefully, acts as a timing guardrail. A pure value list can be full of falling knives. A light momentum overlay, like excluding names with catastrophic three-month relative performance, often reduces the number of companies still digesting bad news.
How to compute ranks that make sense
Once you calculate metrics, convert them into ranks. Raw ratios vary by sector and scale. Ranking creates comparability. I do two rounds of ranking: sector-relative and absolute. Sector-relative ranks prevent the scanner from tilting too heavily into structurally cheap industries like utilities or small banks. Absolute ranks ensure you still surface exceptional cases in any sector.
Within each pillar, normalize direction. For valuation, cheaper is better, so lower ratios get higher ranks. For quality, higher ROIC and margins get higher ranks. For risk, lower leverage gets higher ranks. Combine ranks within each pillar using a simple average. Then combine pillar scores using weights that reflect your philosophy. A deep-value build might set valuation at 50 percent, quality at 30 percent, and risk at 20 percent. A quality-first build might flip the first two.
Weights are not sacred. I routinely test ranges rather than a single number. If the top names change dramatically when you move valuation from 40 to 45 percent, the model is fragile. Stable leaders across reasonable weighting bands usually have merit.
Guardrails: exclusions and sanity checks
A scanner that does not filter junk will waste your time. I add hard exclusions before ranking:
- Minimum liquidity measured by average daily dollar volume across three months. Minimum price per share to avoid micro-lot noise and delisting risk, unless you deliberately hunt in that pond. Exclude companies with auditor going-concern warnings if your data allows it. Remove names with negative equity if you rely on price to book.
After ranking, I add soft guardrails that flag candidates rather than remove them. For example, if a company has a net operating loss carryforward that inflates near-term cash flows, I annotate the result but keep it in the list. This is where the scanner helps you find stocks worth a look, not rubber stamp them.
Building the pipeline step by step
You can build this in spreadsheets if you must, but a small script is more durable and transparent. The pipeline has five phases: ingest, clean, compute, rank, and report.
- Ingest: Pull fundamentals and prices. Log timestamps and versions. Store raw files unchanged. Clean: Fix units, convert currencies to a base currency, handle missing values with explicit flags, not silent imputations. Compute: Derive metrics like EV, free cash flow, ROIC, accruals, and leverage. Cache intermediate results so you can reuse them. Rank: Create sector-relative and absolute ranks, combine into pillar scores, apply weights, and produce a composite. Report: Output a candidate list with metrics, notes, and links to filings or investor relations pages. If you track a watchlist, append changes since last run.
Even a minimal version of this pipeline adapts over time. Treat it like a product, not a one-off script. Version your formulas. Keep change logs. When a metric breaks during a restatement cycle, you will be glad you can roll back.
Backtesting without fooling yourself
It is tempting to backtest and declare victory when a top-decile portfolio outperforms. Resist shortcuts that inflate results. To avoid look-ahead bias, lag fundamentals by at least one reporting delay. If a company filed its quarter on May 5, do not let your model use that data on May 1. I lag by a conservative one month after the official filing date when running simulations.
Rebalance frequency matters. Value signals do not change daily. Monthly or quarterly rebalances reduce turnover and friction. Include transaction costs in your backtest, even if small. For thinly traded names, add slippage. The point is not to find the best possible number. It is to find a process that works in the messy middle where portfolios live.
When I first backtested a deep-value screen in small caps, the results looked spectacular. Then I added realistic slippage and the edge shrank to a level I could stomach. It still beat the market by a few points a year, but, more importantly, it delivered stocks with tangible reasons to own them. That is the win.
Handling the ugly edges: cyclicals, financials, and one-offs
Value models struggle in a few places. Commodity producers swing from riches to losses on the back of prices. If you rank them on trailing earnings, you tend to buy at the top of the cycle. A more sensible approach is to normalize, using multi-year averages for margins and cash flows. I use a three to five year median for EV to EBITDA in cyclicals and require balance sheet strength to weather downturns.
Financials deserve their own playbook. Book value, net interest margins, and capital ratios carry more weight than EBITDA. Credit quality matters. For banks, I substitute price to tangible book and return on equity for EV to EBIT, and I add nonperforming asset ratios if available. A generalist model that blindly applies industrial metrics to banks will return noise.
One-off events, from legal settlements to asset sales, contaminate metrics. You can improve matters by adding a simple flag: if non-operating income plus unusual items exceed, say, 20 percent of net income, route the company to a manual queue. Over time, you will learn which sectors spawn the most adjustments and adjust your rules accordingly.
Balancing automation with human review
A scanner is a filter, not a decision engine. Every strong candidate still needs a read of the footnotes, a check on strategy, and a sense of competitive dynamics. The machine does not talk to customers, but you can. The machine does not notice that a CEO changes compensation targets right before a big stock grant vests. You might.
I usually set aside time each week to review the top decile by composite score and pick a handful to read deeply. I look for coherence. Do the numbers tell a story that matches the filings and the reality of the industry? If a company earns high marks on value and quality but has a new, aggressive accounting policy, I either pass or demand a larger margin of safety.
From watchlist to action: turning scans into decisions
The scanner helps you find stocks, but a portfolio needs position sizing and exit rules. For sizing, I tie initial weights to conviction and risk. A company with low leverage, stable free cash flow, and a discount to fair value can deserve a larger weight than a hairier turnaround trading even cheaper. For exits, I gravitate toward a three-part rule: if the valuation rerates to fair, if the thesis breaks, or if better opportunities crowd it out, I trim or sell. A scanner can trigger a “rerate alert” by tracking how far a valuation metric has moved relative to its three-year average or your estimate of fair value.
I also keep a “graveyard” of ideas I passed on, with a note explaining why. It keeps me honest. If the stock later rallies for reasons that contradict my concern, I revisit my logic. The scanner will surface that name again, and I will be less likely to dismiss it out of habit.
Practical considerations when you want to ship something real
This is where craft matters more than code. A few choices make the difference between a toy and a tool that helps you with buying stocks over years rather than weeks.
- Don’t chase precision you can’t monitor. A sophisticated ROIC computation with dozens of adjustments sounds great until a single field changes in your data source and silently corrupts the metric. Simpler, consistent definitions beat fragile elegance. Expose intermediate values in your reports. When you look at a candidate, you should see the building blocks: EBIT, EV, FCF, leverage, margins, and the data periods used. Transparency reduces false confidence. Put a human-friendly front end on it. Even a simple dashboard that lets you sort by composite score, filter by sector, and open links to 10-Ks saves time. The less friction you face, the more you will use the tool. Let the model say no. The scanner should produce an empty list sometimes. If it always finds the “best stocks to buy now,” you are overfitting or running the bar too low.
An example run: what a weekly cycle looks like
On Monday morning, the pipeline refreshes prices and market caps, leaving fundamentals unchanged unless new reports arrived. The scanner updates ranks. A short summary shows changes at the top. One week last spring, a mid-cap industrial with quietly rising gross margins and a three-year median EV to EBIT near 8 slipped into the top ten after a 12 percent drawdown. It passed liquidity and leverage checks. Notes flagged a recent plant consolidation that compressed margins for two quarters, now reversing. The model didn’t know the plant manager, but the numbers lined up with the story management told three quarters earlier. That went on the read-now list, and two hours with the filings pushed it to the buy list with a small starter position.
The same report also showed a small bank with outstanding value ratios. A look at the footnotes revealed it held a large book of long-duration securities purchased near the top of the rate cycle, with unrealized losses that could crimp capital ratios if deposit pressure persisted. The scanner had done its job. It surfaced a cheap stock that needed scrutiny, and human judgment decided to pass for now.
Integrating alternative clues without drowning in data
Not all useful inputs live in the financial statements. You can add lightweight alternative data without building a data science shop. Insider buying often aligns with value, especially when purchases are sizable relative to compensation. Share repurchases at low multiples are another positive signal, while buybacks at high multiples should count for less. These are not mandatory, but in my experience, a simple insider-buying flag improves the hit rate of a value screen.
News and sentiment can help you avoid value traps when headlines signal structural problems, not just temporary disappointments. Avoid adding sentiment as a scoring pillar unless you have robust, clean inputs. A simpler approach is to route any company with fresh, severe negative news to a manual review queue, delaying action until the dust settles.
Common pitfalls and realistic expectations
The biggest mistake is confusing a scanner with a crystal ball. A good stock scanner narrows the field. It does not remove uncertainty. Value investing, even with a strong stock screener, deals in probabilities and ranges, not certainties.
Two other traps recur. First, letting the model drift into market timing, swapping value metrics for price momentum because a few months get rough. Second, failing to maintain the pipeline. Data sources change. APIs deprecate fields. If you don’t monitor, you will wake up to a list of junk and not know when it went off the rails.
Set expectations. A solid value scanner may produce only a handful of truly compelling ideas in a given quarter. That is fine. It is better to buy three good stocks a quarter than to jam twenty mediocre ones just to stay busy. Over a year, a patient process can build a concentrated portfolio with a defensible edge.
Tuning for your purpose: personal investor vs small team
A solo investor can keep things lean. Use a reliable data source, implement a compact set of metrics, and run weekly. Spend your energy on reading filings and understanding industries. For a small team, specialize. One person can own the pipeline and data integrity. Another can own qualitative research and calls with management or customers. A third can maintain risk and portfolio analytics. The scanner anchors the process, but human specialization compounds your advantage.
If you publish findings or run a small fund, add a simple audit layer. Track differences between model output and actual decisions. When you override the scanner, record why. Over time, you will learn whether your overrides add value or subtract it. That feedback loop is gold.
Where keywords belong and where they don’t
People often ask how a value scanner compares to an off-the-shelf stock screener or stock scanner at a broker. Those tools are excellent for fast filtering, and I use them when I travel. They shine when you need to find stocks that meet a few simple criteria on the fly. Your custom build goes deeper, incorporates your philosophy, and gives you confidence when markets get noisy. It is not about beating a website. It is about owning your process for buying stocks with discipline, rather than chasing lists that promise the best stocks to buy now without context.
A closing checklist you can keep on your desk
Here is a compact list I keep nearby when I review candidates from my scanner. It prevents drift and forces me to touch the key points before I move from shortlist to buy.
- Are the valuation metrics cheap on both trailing and multi-year median bases, and do they make sense for this sector? Do quality signals, especially ROIC and gross margin trends, support the idea that earnings power is durable? Is balance sheet risk manageable, with interest coverage that holds even in a stress case? Do any one-off items or accounting quirks distort the recent numbers, and have I read the relevant footnotes? What is the simple thesis in one or two sentences, and what would disprove it?
Keep that list short and honest. It has saved me from more mistakes than any single metric.
The durable advantage of a homebuilt process
A value stock scanner built from scratch teaches you as it helps you. You learn where data breaks, which metrics matter in which industries, and how to blend rules with judgment. You also gain an internal compass. When volatility spikes and feeds fill with hot takes, you have a calm way to find stocks that match your criteria, evaluate them on your timeline, and act with clarity.
Tools don’t have to be complex to be powerful. A clean dataset, a handful of well-defined metrics, and a disciplined loop of review and refinement will outperform scattered hunches. Build the scanner, let it evolve, and make it yours. That ownership is the quiet edge that endures long after the latest screen of the day fades from view.