- 1We rank approximately 3,000 U.S. stocks monthly using a multi-factor model
- 2Our methodology is based on 46 peer-reviewed academic papers
- 3Every factor and weight is disclosed—complete transparency
- 4Statistical validation shows our rankings predict future returns
#Our Philosophy
We believe stock selection should be:
- 1Evidence-based – Grounded in peer-reviewed research, not opinions
- 2Transparent – Every calculation disclosed
- 3Consistent – Same methodology applied regardless of market conditions
- 4Humble – We acknowledge limitations and don't oversell results
This isn't a black box. Everything we do is documented and backed by academic citations you can verify yourself.
#The Six Factors We Use
We evaluate every stock across six characteristics that academic research has shown predict returns:
| Factor | Weight | What It Measures |
|---|---|---|
| Profitability | 30% | Cash-based operating profitability |
| Momentum | 25% | 12-month price momentum (skip most recent month) |
| Value | 15% | Earnings, cash flow, and book value relative to price |
| Low Volatility | 10% | Price stability over past 60 days |
| Investment | 10% | Conservative asset growth |
| Short Interest | 10% | Low short selling activity |
Why These Weights?
Our weights reflect the strength of academic evidence:
- Profitability gets the highest weight (30%) because research shows it's the most consistent factor across markets and time periods. Novy-Marx (2013) called profitability "the other side of value"—it works even when value struggles.
- Momentum gets 25% because it's one of the most robust anomalies in finance. The original researchers confirmed it still works after 30 years (Jegadeesh & Titman, 2023).
- Value, Low Volatility, Investment, and Short Interest share the remaining 45% at lower weights due to either partial redundancy with quality (value), post-publication decay (investment), or data availability concerns (short interest).
#The Ranking Process
Step 1: Universe Construction
We start with all U.S. stocks meeting minimum criteria: - Market cap > $100 million - Average daily volume > $500,000 - At least 1 year of price history - Sufficient financial data coverage
This yields approximately 3,000 stocks.
Step 2: Factor Calculation
For each stock, we calculate raw factor scores using exact academic methodologies:
Profitability (Ball et al., 2016):
``
Cash-Based Operating Profit =
(Revenue - COGS - SG&A - Working Capital Changes) / Average Assets
``
Momentum (Jegadeesh & Titman, 1993):
``
12-month return, excluding most recent month
``
Value (Arnott et al., 2021):
``
Composite of: EBIT/EV + FCF/EV + Sales/EV + Book/Market
``
Step 3: Standardization
Raw scores are converted to z-scores (standard deviations from the mean). This ensures factors are comparable—a profitability score of +2 means the same thing as a momentum score of +2.
We also apply winsorization at the 1st and 99th percentiles to limit the influence of extreme outliers.
Step 4: Weighted Combination
Factor z-scores are combined using our fixed weights:
Composite = 0.30×Profitability + 0.25×Momentum + 0.15×Value
+ 0.10×LowVol + 0.10×Investment + 0.10×ShortInterestImportant: We do NOT re-rank after combining. The composite score preserves the meaningful differences between stocks. A stock with a composite of +3.0 is genuinely stronger than one at +1.5.
Step 5: Star Ratings
For easier interpretation, we assign star ratings using AQR-style fixed score thresholds — stars reflect absolute score quality, not forced distribution:
| Stars | Score Threshold | Meaning |
|---|---|---|
| ★★★★★ | Score >= 75 | Strong Buy |
| ★★★★☆ | Score >= 65 | Buy |
| ★★★☆☆ | Score >= 50 | Hold |
| ★★☆☆☆ | Score >= 40 | Reduce |
| ★☆☆☆☆ | Score < 40 | Avoid |
#Statistical Validation
We don't just claim our rankings work—we prove it:
Information Coefficient (IC)
The IC measures correlation between our rankings and subsequent returns. Our IC of 0.044 means higher-ranked stocks tend to outperform lower-ranked stocks.
Statistical Significance
Our IC t-statistic of 9.84 far exceeds the 2.0 threshold for statistical significance. In fact, Harvey et al. (2016) recommend a threshold of 3.0 for factor research—we exceed that by more than 3x.
Score Spread
Stocks scoring 75+ (5-star) outperform those scoring below 40 (1-star) by approximately 18% annually. This spread has been consistent across different market conditions.
#What We Don't Do
We Don't Time Factors
Research shows factor timing typically fails (Asness, 2016). We maintain constant weights regardless of market conditions.
We Don't Chase New "Factors"
Of 452 proposed anomalies, most don't replicate (Hou et al., 2020). We stick to the six with strongest evidence.
We Don't Hide Our Methods
Every calculation is documented. Every citation is real. You can verify our sources.
We Don't Guarantee Returns
Past performance doesn't guarantee future results. Factors can underperform for years. We provide information, not investment advice.
#Academic Foundation
Our methodology draws on 46 peer-reviewed studies, including:
Core Factor Research: - Ball et al. (2016) – Cash-based operating profitability - Jegadeesh & Titman (1993, 2023) – Momentum - Arnott et al. (2021) – Intangible-adjusted value - Fama & French (2015) – Five-factor model
Validation Research: - Harvey, Liu & Zhu (2016) – Statistical standards - Hou, Xue & Zhang (2020) – Replication study - Grinold & Kahn (2000) – IC framework
Critical Perspectives: - Novy-Marx & Velikov (2022) – Critiques of BAB - Arnott et al. (2016) – Factor timing challenges
Including critical research isn't weakness—it's intellectual honesty.
#The Bottom Line
Stock ranking shouldn't be mysterious. We use six factors validated by decades of research, apply them consistently, and disclose everything.
Whether you're a serious investor doing due diligence or just curious how quantitative investing works, we've built this to be completely transparent.
For complete academic citations, see our full bibliography.
Last updated: February 1, 2026