10 Factors Inside The Rational Formula Ranking System

And What Makes a Ranking System More Than the Sum of Its Parts

Over the years, I've benefited enormously from people willing to share what they knew. They could have kept their insights to themselves, but chose to share. When I've tried to thank the mentors who helped me most, in investing or elsewhere, they’ve all said some version of the same thing: pay it forward.

This post is one attempt to do that.

One of the best parts of investing, especially in microcap value, is how many people put their thinking out there. There’s a collaborative, intellectually honest streak in the community, and I’ve learned far more from that than from formal education.

Investing is a demanding, dynamic challenge. The money matters, but it functions more like a score in a complex strategy game, it forces clarity and adds just enough pressure to make it worth obsessing over. It’s a puzzle with real-world stakes, made better by the fact that others are solving it too, often while helping each other along the way.

This post shares a handful of factors from my main ranking system—the one behind the weekly list. What makes it work isn’t any single insight, but how the pieces reinforce each other. Even with the full blueprint, it would still take months of testing and refinement to make it your own. The hardest part isn’t building it, it’s trusting it. You need to see it work across cycles, across different kinds of companies, and through your own psychological reactions.

The full system took thousands of hours to build and still costs thousands annually in data, tools, and research. But I like sharing. And if a post like this is enough to kill the edge, I never really had one.

The goal here is to be helpful without making it copy-pasteable. It’s for people who are willing to think, experiment, and refine their own process.

Whether you’re new to systematic investing or have been at it for years, I think you’ll find something useful here. Even a simple system can be surprisingly effective, especially in underfollowed corners of the market.

And even if you’re not a quant, some of the same ideas might sharpen your discretionary analysis.

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How the System Thinks

The Rational Formula ranking system is built to identify companies with multiple signs of strength—fundamental improvement, attractive valuation, healthy balance sheets, and favorable price behavior. Some rise in the rankings because earnings or revenue are accelerating. Others are inexpensive relative to cash flow, or showing better debt coverage or margin stability than the market seems to notice. The system weighs more than 90 factors across growth, value, quality, sentiment, and technicals, using ranks rather than hard cutoffs. That allows it to prioritize companies with compelling traits, even if they’re uneven, rather than excluding anything that doesn’t hit a predefined screen.

That structure gives it flexibility across market environments. Some factors lag during certain regimes, while others lead. Rather than trying to time that rotation, I’ve tried to blend them in a way that holds up over time, with small tune-ups rather than constant overhauls.

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The Main Building Blocks

Most factor-based systems draw from a familiar set of themes: growth, value, quality, sentiment, and technical behavior. Mine does too. I also incorporate things like stability, insider and institutional activity, volatility, and size, but those play more of a supporting role. The core of the system is built around those five main categories.

Growth
I’m not looking for smooth long-term compounding necessarily. I’m looking for acceleration. That means earnings, sales, or operating income that are picking up pace or improving across multiple timeframes.

Value
I look at a broad mix of valuation measures, both trailing and forward-looking. Some are based on earnings or cash flow; others use sales, EBITDA, or book value depending on the context. The goal is to find companies that are priced attractively relative to what they’re producing, especially those with business models that can sustain owner’s earnings over time.

Quality
This category includes a wide range of metrics, from return on capital and margin consistency to interest coverage and capital allocation behavior. I’m especially interested in companies that generate strong, repeatable returns on capital—whether that’s equity, assets, or invested cash. I’m prioritizing reliability and predictability: businesses that operate efficiently, cover their obligations comfortably, and show signs of durability even through weaker periods.

Sentiment
The ranking system includes a few sentiment-based factors—things like estimate revisions, analyst upgrades, and price targets (price targets are pretty weak). These are useful in more widely followed names, where analyst activity can move price and signal shifting expectations. In microcaps, though, there’s often no coverage at all. In those cases, I still try to get a sense of sentiment manually—by reading management commentary, following investor conversations, reading independent analysts, or tracking how tone changes over time. That part isn’t built into the system, but it still informs how I think about opportunities.

Technicals
The system includes a few components that look at price momentum, volume behavior, and share turnover. I don’t chase, but I do want to favor situations that are already starting to move, without emphazising it too much. These factors act as a kind of confirmation: are the fundamentals starting to show up in market behavior? When company performance aligns with improving price and volume trends, outcomes tend to improve. That combination has historically outperformed relying on fundamentals or technicals alone.

These categories aren’t siloed. The ranking system gives each company a composite score, which means a standout in one area can still rise to the top, even if it’s weaker in another. That’s the kind of nuance basic screeners miss.

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10 Factors That Actually Work

Most of the factors I use are custom built but a large minority are not, and many are layered with things like looped comparisons, medians, or time-based logic that’s too much to explain in this post. But I’ve picked a sample of core ideas—some simple, some more advanced—that should be useful whether you’re experimenting with your own system or just trying to sharpen your analysis of individual stocks. I’ll likely cover more, or go into more detail, in the future but for now here are two from each main category that I think are illustrative.

Growth

• EPS Acceleration
  One of my primary factors looks at how earnings growth is changing over time. For example, if trailing twelve-month EPS growth is improving versus five-year averages, or if recent quarter-over-quarter changes look better than the full-year trend. I calculate this as a percent change in growth rate, scaled by the size of the earlier growth number to normalize for magnitude.

• Sales Acceleration
  Similar logic, but applied to revenue. This is particularly helpful for spotting companies in early expansion phases or with newly successful products.

Value

• Free Cash Flow to Enterprise Value (forward-preferred)
  I use a version of free cash flow yield that relies on forward estimates when available and defaults to trailing values when they’re not. The idea is to measure how much unlevered cash a business is likely to generate relative to its total valuation. Forward estimates of free cash flow are less commonly available than earnings or revenue, especially in small caps, but they’re worth the effort. If you or someone you trust can come up with a reasonable forward estimate, even within a range, that can dramatically improve your assessment. You don’t need to be a formal buy-side analyst to do that well. It’s a valuable skill to build or find.

• Gross Profit to Enterprise Value
  This is a surprisingly strong valuation metric (kind of a combo between quality and value really), and often works better than traditional return-based measures. Overall I’ve found that Gross Profit is usually a stronger quality factor than ROIC or related metrics.
  GrossProfitTTM / EV

Quality

• Return on Capital Consistency
  Rather than use a single ROIC or ROE figure, I measure how consistent those returns have been over time. This includes looped averages and comparisons over 5-year windows.

• Interest Coverage, 5-Year Sum
  I calculate the ratio of operating income to interest expense, summed over 20 quarters. This helps separate stable businesses from those at financial risk, even if short-term metrics look good.

Momentum / Technical

• Evaluated Momentum
  I use a group of momentum factors that emphasize medium-term strength (3–9 months), while avoiding short-term noise and adjusting for volume. Many of these are structured to avoid stocks with unsustainable spikes. The idea is to reward stocks showing persistent upward movement on meaningful volume, not flash-in-the-pan moves.

• Volume Increase
  I use simple volume spike formulas like AvgVol(5) / AvgVol(20) but also volume-based metrics that look at short-term spikes relative to both medium- and longer-term averages. That helps find names where interest is building in a more sustained way, rather than just reacting to a single day’s move

Sentiment

• Estimate Revisions
  I include factors that compare current analyst earnings or revenue estimates to those from 4 or 13 weeks ago. Upward revisions tend to precede price moves.

• Short Interest
  I use short interest in several ways, both absolute and relative to sectors and industries. The key idea is that heavily shorted names tend to underperform, especially in microcaps where professional short sellers target structurally weak businesses. I wouldn’t think a factor this simple and easy to use would be as useful as it is, but it turns out betting against dying companies is a pretty durable theme (might have something to do with Soros’s Reflexivity)

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A few of the factors I use are counterintuitive, metrics that would look like red flags on their own but under the right conditions can become useful predictors. Some draw on ideas around mean reversion or the market's typical reaction to deteriorating results. Others reflect how certain "bad" numbers might signal something positive about other aspects of the business, like operating leverage or changes in capital efficiency. On their own, these metrics don't hold up well. But layered with other factors, they can help point to inflection points or emerging strength.

I'm not mentioning this to be coy. I bring it up because it's a good reminder that judgment still matters. Even in a systematic process, you want to leave room for nuance, especially when combining factors that might cancel each other out or reveal something deeper together than they would apart.

Even the intuitive factors tend to be limited on their own and work better when combined with others. For instance, a simple valuation ratio too often points you toward companies that are cheap for a reason. But if you add quality metrics—like decent returns on capital, or even just exclude the most heavily shorted stocks, the overall results improve dramatically. You end up filtering out businesses that probably deserve a low multiple and narrowing in on ones that might be misjudged. That synergy between factors is where much of the returns come from.

Where to Go from Here

If you’re interested in building or refining a system of your own, you can absolutely do it. You don’t need to be a quant or a professional programmer to start thinking systematically and implementing this stuff. If you’re not already doing it you might think about applying some of these metrics to your company analysis manually.

If you want to go further—testing combinations, ranking ideas, running backtests—you'll want a dedicated platform. I've tried many of them, and Portfolio123 is the one I've stuck with. One of the biggest advantages of using a ranking system is that it lets you test what actually works, instead of relying on investing dogma or generalizations that may not hold up.

What makes Portfolio123 especially helpful is that it comes with dozens of prebuilt systems you can run, study, and modify. You can see how others have structured their models, experiment with variations, and get a much faster feel for what matters. You don't have to start from scratch.

But if you insist on starting from scratch, I admire the commitment. Just know you'll hate yourself for the first 500 backtests.

Another underappreciated benefit: access to FactSet data. That alone can be worth the subscription, especially in microcaps, where free data is often missing or wrong. I’ve seen simple errors in things like share count that completely distort the valuation. Having reliable numbers matters more than people realize.

When I first got started, I didn’t know what I was doing. But I started running tests with pre-built ranking systems, adapting models from other users, reading forums, academic papers, and recommended materials. It wasn’t long before that process started to pay off. You don’t have to be obsessed to benefit from it. Even light tinkering can go a long way. Though if you do get obsessed, welcome to the club.

If you want to try Portfolio123, there’s a 35-day free trial through my affiliate link and use the code “rationalresearch”. The trial you get from my link is two weeks longer than what you’ll get on the public site. I do get a discount on my membership if you sign up through it, but sign up however you want. I’ve used the platform for many years, and it’s easily been the most useful tool in my investing kit.

In future posts, I’ll go deeper into how to think about ranking systems, how to build your own, how to test, and additional factors. I’ll also share more about how I use AI tools to support the process. If that sounds useful, feel free to subscribe. That’s the best way to follow along, and to let me know this kind of content is worth continuing.

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Nothing in this post or publication is investment advice.