Stock Meadow · Model guide

Weighted stock ranking, explained

Weighted ranking is Stock Meadow's default model. It lines the stocks up on every metric you choose, turns each position into a score from 0 to 100, and blends those scores using the weights you set.

The idea: rank, then blend

Investors often judge a stock on several things at once: how cheap it is, how profitable, how fast it is growing, how much debt it carries. Those measures use different units (a multiple, a percentage, a ratio), so they cannot simply be added together.

Ranking solves that. On each measure the stocks are sorted from best to worst, and each stock receives a score based only on its position. The scores all run from 0 to 100, so they can be averaged, with more weight on the things you care about most. Multi-factor ranking of this kind is a common way to build a stock screener or a rules-based portfolio.

How Stock Meadow calculates the score

  1. Choose metrics and weights. Pick the metrics to score and give each a weight from 1 to 10.
  2. Rank each metric. Stocks that pass your filters are sorted from best to worst. For lower-is-better metrics such as P/E, the order is reversed.
  3. Convert position to a percentile. The best stock scores 100, the worst 0, and the rest are spread evenly by position. Stocks with equal values share the midpoint of their positions.
  4. Blend. The overall score is the weighted average: the sum of (metric score × weight) divided by the sum of the weights.

Losses rank last, not first. Price multiples such as P/E, price / free cash flow, EV / EBITDA and price / book are ranked by their inverse, a yield. A positive multiple keeps its order, and a loss or negative figure ranks below every positive one, instead of looking cheapest.

Target-range metrics are not ranked. Payout ratio and RSI score 100 inside a healthy range (payout 25–65%, RSI 50–70) and lose points the further they stray. They are scored on that fixed scale rather than against other stocks.

A worked example with a fictional company

Assume ten fictional companies pass the same filters and all have complete data. Company A is ranked against the other nine. The figures below are made up for illustration and do not describe any listed company.

Fictional weighted ranking for Company A — illustrative figures
MetricA's valueRank of 10PercentileWeightPercentile × weight
P/E (lower is better)12377.82155.6
ROIC (higher is better)18%288.93266.7
Revenue growth (higher is better)6%555.6155.6

Company A is third cheapest on P/E, giving (10 − 3) ÷ 9 × 100 = 77.8. It has the second-highest ROIC (88.9) and the fifth-highest revenue growth (55.6). The weights are 2, 3 and 1, a total of 6.

Weighted score = (77.8 × 2 + 88.9 × 3 + 55.6 × 1) ÷ 6 = 79.6 out of 100. The score describes A's position among these ten companies. It is not a forecast of A's return.

Using weighted ranking in Stock Meadow

  1. Choose Weighted ranking in the scoring-model picker (it is the default).
  2. Turn on the metrics to score and set a weight for each. The style presets (Balanced, Value, Quality, Growth, Momentum) all use this model, and Custom is your own mix.
  3. Add filters for the stocks you want to consider. Filters run before scoring.
  4. Open a stock's breakdown to see each metric's score and its share of the total.

Complete data is required. A stock missing a figure for any metric you score is left out rather than guessed, and the page says how many were left out and why. Removing that metric lets the stock be ranked. At least ten stocks must remain, and fewer than thirty brings a warning that percentile ranks may be unstable.

Compare within each sector. You can rank each stock against its own sector. A sector with fewer than five eligible stocks falls back to the whole list. Equal overall scores are ordered by ticker.

See the full methodology for the shared filtering and ranking rules.

What the ranking leaves out

  • Only the order counts. A stock far ahead of the pack earns about the same as one narrowly ahead.
  • Scores are relative. If every stock in your screen is expensive, the cheapest of them still scores 100 on valuation.
  • The weights are your judgement. Different weights give different rankings, and none is objectively right.
  • A high score can hide a weak area. Strength on heavily weighted metrics can offset a poor result elsewhere. The Balanced model is built to prevent that.

Stock Meadow's scores are research tools based on reported financial data. They do not predict returns or recommend buying a particular stock.

Metrics this model uses

Each measure has its own page with a formula, how to read it and common pitfalls.

Frequently asked questions

What is a percentile rank for stocks?
A percentile rank turns a stock's position in a list into a score from 0 to 100. The best stock on a metric gets 100, the worst gets 0, and the others are spread evenly in between according to their order.
Does weighted ranking reward a stock that is far ahead of the others?
No. Only the order matters. A stock that is much cheaper than every other stock scores about the same on that metric as one that is only slightly cheaper. If you want distance to count, use the Z-score composite.
How do weights work in a weighted stock ranking?
Each metric's score is multiplied by its weight, from 1 to 10, and the results are divided by the total of all the weights. A metric with weight 3 counts three times as much as one with weight 1.
Why can the same stock get a different score in a different screen?
Scores are relative. Each stock is ranked against the other stocks that pass your filters, so changing the filters, the sector comparison or the stocks in the list can change its percentile on every metric.

Explore the model in Stock Meadow

Open the stock screener, choose Weighted ranking, and inspect a stock's score breakdown. Free during the preview.

Open stock screener