The Methodology

Build your own ranking system

The TGI methodology isn't a specific list of stocks. It's a framework for ranking any universe of symbols against any criteria that reflect your goals. Here's how to apply it yourself.

Start Here

The engine, not the list

When people discover Total Growth Investing, the first thing they see is the list — 900-plus symbols ranked by criteria Chad has refined over years of trial and error. It's easy to assume the list is the methodology. It isn't.

The list is an output. The methodology is what produces it.

That distinction matters because the methodology — rank any universe of symbols against any criteria that reflect your goals, buy from the top, update regularly, let the data steer — is applicable to any market, any asset class, any investment philosophy. Chad built his universe around US-listed stocks and his criteria around dividend growth and price appreciation because those reflect his goals. Your goals might be different. Your universe might be different. That's not a problem. That's the point.

For anyone starting from scratch, this page describes a methodology for building a watchlist from zero. But most of us are not starting from that level, we already have portfolios, we already have watchlists that feed those portfolios, and we may already have something similar to the criteria that we use to filter that watchlist down into a buy list. When Chad started building the current TGI list he was coming from 10 years of a High Yield Hunt that had resulted in a portfolio that provided an income stream, but was completely stagnant or collapsing in terms of value. The reset in 2020 was after rebuilding the ranking system away from the highest yield he could find and towards a different question, what positions showed the best growth in both dividends AND price performance over time.

This page is for investors who want to build their own ranking system from the ground up, using TGI principles but applied to their own universe, their own criteria, and their own goals. There are three decisions to make.

1

Define your universe

The set of symbols you are going to rank against each other. Could be an index, a sector, a region, a personal watchlist — anything, as long as the symbols are meaningfully comparable.

2

Choose your metrics

The columns in your ranking table. Each metric should reflect something you actually believe predicts long-term performance. Your investment philosophy becomes concrete here.

3

Decide how to combine them

Turn multiple columns of numbers into a single ranking. Simple rank averaging, weighted scoring, proximity targeting — the right method depends on what you're optimizing for.

Decision One

Define your universe

Your universe is the set of symbols you are going to compare against each other. Everything in your ranking system flows from this choice, so it deserves careful thought. A universe can be almost anything:

  • The constituents of a major index (S&P 500, FTSE 100, KOSPI, ASX 200, Nikkei 225)
  • All stocks in a specific sector or industry you know well
  • A personally curated watchlist built up over years of research
  • The holdings of a fund or ETF you want to understand or replicate
  • A region or country you have conviction in for the long term

The only real rule is coherence. Symbols in your universe should be meaningfully comparable to each other. Ranking a Korean semiconductor company against a US utility against a Brazilian bank on identical criteria produces noise, not signal, because the underlying business dynamics, growth rates, and payout cultures are so different that the same number means something entirely different in each context. That doesn't mean you can never cross regions or sectors — Chad's list does — but it means thinking carefully about whether comparison actually makes sense.

Starting small is fine. Chad's list grew to 900-plus symbols over a decade, beginning with the Dividend Kings and CCC lists and expanding from there to anything that beat SPY over the last 1, 3, 5, and 10 years. If you're building a universe focused on Australian dividend-growth stocks, you might start with 50 names and grow from there as you gain confidence in the system. A well-maintained universe of 50 symbols beats a poorly maintained universe of 500.

Your universe is a living thing. Stocks get added and removed over time. A company that used to qualify might stop paying its dividend and fall off. A new company might go public that deserves a spot. Building a universe isn't a one-time event — it's an ongoing research commitment. Plan for that from the beginning.

Define your entry bar and apply it consistently. Chad uses a simple test — does the stock beat SPY over the last 1, 3, 5, and 10 years? That's the bar for entering his watchlist. You don't have to use SPY, but having some entry criterion prevents your universe from becoming an undifferentiated collection of everything that crossed your radar.

Decision Two

Choose your metrics

This is where your investment philosophy becomes concrete. Your metrics are the columns in your ranking table — the specific measurements that determine which symbols rise to the top and which fall to the bottom.

Choosing metrics is the most personal part of building a ranking system, because the right metrics depend entirely on what you believe matters for long-term performance and what your actual investment goal is. There is no universal set of correct metrics. There is only the set that reflects your thesis.

Four principles for choosing metrics

Consistently available. A metric that only exists for 60% of your symbols isn't useful — it creates gaps that distort rankings and force judgment calls about missing data. Before committing to a metric, verify you can actually get it for most or all of your universe.

Clear directional preference. For each metric, you need to know: is higher better, is lower better, or is "closer to a specific target" better? All three are valid, but you need to know which applies before you can rank on it. Dividend growth rate — higher is better. Payout ratio — lower is better. A target yield — closer to 4% is better in either direction.

Independent of each other. If two of your metrics are highly correlated — they move together and tell you the same thing — you're essentially double-counting that factor. Aim for metrics that each capture something distinct about the business or the stock.

Fewer is usually better. When too many factors are combined into a single composite score, they begin to cancel each other out and produce noise rather than signal. Four to six strong, independent metrics tends to outperform ten loosely related ones. Start with fewer and add only when you have a genuine reason to believe the new metric adds information the others don't capture.

Metrics by investment goal

Goal

Dividend Growth

  • Dividend CAGR at 1, 3, 5, 10 years
  • Price CAGR at the same intervals
  • Payout ratio as a sustainability check
  • Higher is better for growth rates; lower is better for payout ratio
Goal

Targeted Income Yield

  • Proximity to target yield (e.g. 4%)
  • Dividend growth rate to confirm sustainability
  • Price growth rate to confirm the company isn't shrinking
  • Closer to target is better — both sides of the target score poorly if far away
Goal

Earnings Quality

  • EPS growth rate over multiple timeframes
  • Free cash flow margin
  • Return on equity
  • Debt-to-equity ratio
  • Higher is better for growth/margins/ROE; lower for debt
Goal

Value

  • Price-to-earnings ratio
  • Price-to-book ratio
  • EV/EBITDA
  • Lower is generally better — but combine with a quality filter to avoid value traps
Goal

Momentum

  • Price growth at 1 month, 3 months, 6 months, 1 year
  • Weight toward more recent periods
  • Requires more frequent updates and has higher turnover than growth or income strategies
Goal

Sector-Specific

  • For REITs: substitute FFO for EPS — net income accounting distorts the picture for real estate
  • For capital-intensive industries: debt structure matters more than for software
  • For ETFs: expense ratio substitutes for payout ratio
Decision Three

Scoring and combining your metrics

Once you have your universe and your metrics, you need a method for turning multiple columns of numbers into a single ranking. There are several approaches, and they make different tradeoffs.

Simple rank average — the TGI approach

For each metric, rank every symbol in your universe from best to worst. The best-performing symbol on that metric gets rank 1, the next gets rank 2, and so on. Then average those individual ranks across all of your metrics. The symbol with the lowest average rank is your top-ranked name.

The elegance here is automatic normalization. A dividend growth rate of 15% and a payout ratio of 35% live on completely different scales, but once both are converted to ranks of 1 through 900, they combine cleanly. Extreme outliers don't blow up the composite — the worst stock on any metric gets rank 900, not rank 50,000. Simple, transparent, hard to manipulate.

Weighted rank average

The same as simple rank average, but before averaging you multiply each metric's rank by a weight that reflects how much you believe that metric matters. If dividend growth rate is twice as important as price growth rate in your thesis, give it a weight of 2 while price growth gets a weight of 1.

Be honest about where your weights come from. If you're assigning them based on what would have produced the best historical rankings, you're fitting to the past, not expressing a forward-looking thesis. Start with weights derived from your actual beliefs and adjust only when you have a principled reason to.

Proximity scoring

For any metric where you have a target value rather than a directional preference, rank by distance from the target rather than by raw value.

The practical example from TGI is the 4-year average yield in the Healthy Income Investing system. The target is approximately 4%. A stock yielding 3.8% and a stock yielding 4.2% should score similarly well. A stock yielding 0.8% and a stock yielding 12% should both score poorly — they're far from the target in opposite directions.

To implement: calculate the absolute difference between each symbol's value and your target, then rank those differences from smallest (rank 1) to largest (rank 900). That rank becomes one of your inputs into the composite score.

Threshold filters plus ranking

Before ranking anything, apply filters to eliminate symbols that don't meet baseline requirements. Only the survivors get ranked.

Examples: must have paid a dividend for at least five consecutive years, must have positive earnings per share, must have a market cap above a minimum size.

Thresholds should be principled, not engineered. If a filter eliminates 30% of your universe, ask whether those eliminations make sense given your investment thesis — or whether you've accidentally excluded names you'd actually want to own.

Building the Foundation

Where to get the data

The methodology transfers cleanly to any universe. The data infrastructure doesn't always follow. For US-listed stocks using TGI-style metrics, the landscape is well-developed. For non-US universes, the situation is more variable.

US markets

Seeking Alpha Pre-calculated dividend CAGRs and payout ratios for most US-listed names. The primary source for TGI metric inputs.
Stockanalysis stockanalysis.com — free multi-timeframe price performance including 10 and 15-year columns. Restores discovery research previously locked behind paywalls.
Alpha Vantage Full historical price series via API. The free tier (25 requests/day) covers most research needs using the TIME_SERIES_MONTHLY_ADJUSTED endpoint.
Macrotrends Split-adjusted price history CSV downloads. Useful for building your own CAGR calculations from raw data.

Non-US markets

Europe Eulerpool is a strong source for European-listed companies, including dividend history and financial metrics.
Australia Stockanalysis.com covers many ASX listings. For detailed dividend history and payout data, supplement with broker-provided data or local sources.
Asia Data quality and accessibility varies. Korean, Japanese, and Singaporean stocks appear on some global platforms, but pre-calculated multi-year growth rates may require more manual calculation from raw price and dividend history.
Global Macrotrends provides historical price and dividend data for many international names. Morningstar and Bloomberg offer comprehensive global coverage at a cost.

The honest reality: building a ranking system for a non-US universe will likely require more manual data work than building one for US stocks, at least initially. That's a solvable problem — the data exists — but it means more spreadsheet work and possibly scripts to pull and clean raw data before you can rank. Whatever sources you use, document them. Your ranking system is only as trustworthy as the data that feeds it.

Keeping It Alive

Maintaining your system

A ranking list that was built once and never updated isn't a methodology. It's a snapshot. The value of a ranking system comes from regular maintenance — updating metrics as new data comes in, adding symbols that qualify for your universe, removing symbols that no longer do, and watching the rankings move over time.

How often you update depends on your metrics. Price-based metrics change daily. Dividend growth rates and payout ratios update quarterly with earnings releases. Annual metrics like 10-year price CAGR update slowly and don't need constant attention.

Chad updates 10 to 20 symbols per day on a rolling cycle, which means his full universe of 900-plus gets refreshed approximately every 90 days — aligned with the quarterly reporting cadence for most US companies. The update isn't uniform, though. Stocks near the top of the buy list get reviewed more frequently, especially before new capital from a paycheck is ready to deploy. You want to confirm a stock still belongs at the top before putting money into it.

For a smaller universe, the update burden is lighter. A universe of 100 symbols updated quarterly is a very manageable research commitment — a few hours per week.

The most important maintenance habit is consistency. Update everything on the same schedule, using the same sources, using the same definitions. Inconsistency in your input data produces inconsistency in your rankings, which makes it hard to trust the signal when the list changes.

The Core

What transfers, what changes

If you are building on TGI principles, certain things carry over regardless of which universe you use or which metrics you choose. Others are personal decisions that belong to you.

What carries over regardless of universe

What belongs to you

The orchard is the point.

Whatever universe you choose, whatever metrics you believe in, the goal is the same: build assets that produce without being consumed. Tend them patiently. Let compounding do its work over time. The orchard you grow will look different from Chad's — different soil, different trees, watered by different data. But the principle is the same, and the result, if you stay disciplined, will be the same too.

Discipline builds. 🌱

Want to go deeper on the TGI methodology?

The full framework, the mistakes that shaped it, and the philosophy behind it are documented across the site — and in the book, free to read or available on Kindle.

How TGI Works Read the Book Free Explore the Tools