At Blank Capital Research, we analyze over 7,000 publicly traded stocks using a sophisticated quantitative model that distills decades of academic research into actionable investment insights. Our proprietary 6-factor ranking system combines the most predictive elements of equity returns into a single composite score, helping investors identify opportunities across the entire market spectrum.
This methodology article provides a comprehensive look inside our quantitative engine—explaining the academic foundations, factor construction, weighting rationale, and practical applications of our ranking system. Whether you're a seasoned quantitative investor or new to factor-based strategies, this guide will help you understand how we transform raw financial data into investment intelligence.
The Academic Foundation: Why Factor Investing Works
Factor investing isn't a recent Wall Street innovation—it's the culmination of over 50 years of rigorous academic research into what drives stock returns. The journey began in 1964 with the Capital Asset Pricing Model (CAPM), which suggested that a stock's return could be explained by its sensitivity to market movements (beta). But CAPM left too much unexplained.
The Fama-French Revolution
In 1992, Eugene Fama and Kenneth French published their groundbreaking paper "The Cross-Section of Expected Stock Returns," which fundamentally changed how we think about equity returns. Their research demonstrated that two additional factors—size (market capitalization) and value (book-to-market ratio)—explained stock returns far better than beta alone.
The original Fama-French three-factor model showed that:
- Small-cap stocks historically outperformed large-cap stocks
- Value stocks (high book-to-market) outperformed growth stocks
- Market exposure remained the dominant factor
This wasn't just academic theory—it was empirically robust across decades of data and international markets. The model explained roughly 90% of diversified portfolio returns, compared to just 70% for CAPM.
Expanding the Factor Universe
The success of the three-factor model opened the floodgates for factor research. In 2015, Fama and French expanded their model to five factors, adding:
- Profitability: Companies with higher profitability tend to outperform
- Investment: Companies with conservative investment policies tend to outperform
Meanwhile, other researchers identified additional factors:
- Momentum: Stocks that have performed well recently tend to continue performing well
- Low Volatility: Less risky stocks often deliver higher risk-adjusted returns
- Quality: Companies with strong balance sheets and stable earnings outperform
Institutional Adoption and Market Impact
Today, factor investing manages over $1.8 trillion globally according to Morningstar. Major institutional investors—from pension funds to sovereign wealth funds—have embraced factor-based strategies because they offer:
| Benefit | Description | Evidence |
|---|---|---|
| Systematic Risk Premia | Factors capture persistent sources of return | 70+ years of academic research |
| Diversification | Factors have low correlation to each other | Correlation matrix typically <0.3 |
| Cost Efficiency | Rules-based approach reduces fees | Factor ETFs average 0.2% expense ratios |
| Transparency | Clear methodology and holdings | Full factor exposure reporting |
The proliferation of factor-based ETFs—from iShares' suite of factor funds to Vanguard's multifactor offerings—demonstrates institutional confidence in factor investing's theoretical foundations and practical applications.
Blank Capital's 6-Factor Model: A Detailed Breakdown
Our quantitative model synthesizes the most robust factors from academic literature into a comprehensive ranking system. Each factor is carefully constructed, weighted by its historical predictive power, and combined into a composite score that ranks stocks from 1 (strongest) to 100 (weakest).
Factor 1: Quality/Profitability (30% Weight)
What It Measures: Quality captures a company's fundamental business strength through profitability metrics, balance sheet stability, and earnings consistency.
Key Metrics:
- Return on Equity (ROE)
- Return on Assets (ROA)
- Gross Profit Margin
- Operating Margin
- Debt-to-Equity Ratio
- Current Ratio
- Earnings Quality Score
Academic Evidence: The profitability factor in the Fama-French five-factor model shows that companies with higher operating profitability relative to book equity earn higher returns. Research by Novy-Marx (2013) demonstrates that gross profitability is nearly as powerful as value in predicting cross-sectional returns.
Why It Works: High-quality companies typically have:
- Sustainable competitive advantages (economic moats)
- Predictable cash flows
- Lower bankruptcy risk
- Better management execution
- Pricing power in their markets
Weight Rationale: Quality receives our highest weighting (30%) because it's the most persistent factor over time. While momentum and value can experience extended periods of underperformance, quality companies tend to compound wealth consistently across market cycles.
Factor 2: Momentum (25% Weight)
What It Measures: Momentum captures the tendency for stocks that have outperformed recently to continue outperforming in the near term.
Key Metrics:
- 12-month price return (excluding most recent month)
- 6-month price return
- 3-month price return
- Earnings revision momentum
- Analyst recommendation changes
- Relative strength vs. sector and market
Academic Evidence: Jegadeesh and Titman's seminal 1993 paper "Returns to Buying Winners and Selling Losers" documented the momentum effect across U.S. stocks from 1965-1989. Subsequent research has confirmed momentum's persistence across international markets and asset classes.
Behavioral Foundation: Momentum exists because of behavioral biases:
- Anchoring: Investors are slow to adjust to new information
- Herding: Success attracts more buyers, creating positive feedback loops
- Confirmation Bias: Investors seek information that confirms existing positions
- Underreaction: Markets initially underreact to earnings surprises and guidance changes
Weight Rationale: Momentum receives a 25% weight because it's the most consistent factor across time periods and geographies. However, momentum can reverse sharply during market stress, so we balance it with mean-reverting factors like value.
Factor 3: Value (15% Weight)
What It Measures: Value identifies stocks trading at discounts to their intrinsic worth based on fundamental metrics.
Key Metrics:
- Price-to-Earnings (P/E) ratio
- Price-to-Book (P/B) ratio
- Price-to-Sales (P/S) ratio
- Price-to-Cash Flow (P/CF) ratio
- Enterprise Value to EBITDA (EV/EBITDA)
- PEG ratio (P/E to Growth)
- Dividend Yield
Academic Evidence: Value is one of the oldest and most studied factors. Fama and French's original 1992 paper showed that high book-to-market stocks (value stocks) outperformed low book-to-market stocks (growth stocks) by 7.6% annually from 1963-1990.
Economic Rationale: Value works because:
- Mean Reversion: Extreme valuations tend to revert to historical norms
- Risk Premium: Value stocks may carry higher fundamental risk, demanding higher returns
- Behavioral Biases: Investors overextrapolate recent trends, creating valuation extremes
- Contrarian Opportunity: Unpopular stocks often have low expectations built into prices
Weight Rationale: Value receives a moderate 15% weight because it has experienced extended periods of underperformance (notably 2010-2020). However, value's long-term track record and recent resurgence justify its inclusion in a diversified factor model.
Factor 4: Investment (10% Weight)
What It Measures: The investment factor captures the relationship between a company's asset growth and future returns, based on the principle that companies with conservative investment policies tend to outperform.
Key Metrics:
- Asset Growth Rate
- Capital Expenditure to Sales
- Working Capital Changes
- Acquisition Activity
- Share Buyback Yield
- Investment-to-Assets Ratio
Academic Evidence: Cooper, Gulen, and Schill (2008) documented that firms with high asset growth subsequently experience poor stock returns. This "investment effect" is now incorporated into the Fama-French five-factor model.
Economic Logic: The investment factor works because:
- Diminishing Returns: Rapid expansion often leads to lower returns on capital
- Management Incentives: Empire-building managers may pursue growth over profitability
- Market Timing: Companies often invest heavily when capital is expensive
- Quality Signal: Conservative investment suggests disciplined capital allocation
Weight Rationale: Investment receives a 10% weight as a complementary factor to quality and value. It helps identify companies with disciplined capital allocation, which often translates to superior long-term returns.
Factor 5: Stability/Low Volatility (10% Weight)
What It Measures: The low volatility factor identifies stocks with below-average price volatility and more stable fundamental characteristics.
Key Metrics:
- Historical Price Volatility (1-year, 3-year)
- Beta vs. Market
- Earnings Volatility
- Revenue Volatility
- Downside Deviation
- Maximum Drawdown
Academic Evidence: The low volatility anomaly, documented by Haugen and Heins (1975) and refined by Ang et al. (2006), shows that low-risk stocks often deliver higher risk-adjusted returns than high-risk stocks—contradicting traditional finance theory.
Why It Works:
- Leverage Constraints: Institutional investors can't use leverage to amplify low-risk returns
- Behavioral Biases: Investors prefer "lottery ticket" stocks with high volatility
- Benchmarking: Fund managers are rewarded for beating benchmarks, not risk-adjusted returns
- Quality Overlap: Stable companies often have stronger fundamentals
Weight Rationale: Low volatility receives a 10% weight as a risk management overlay. It helps reduce portfolio volatility while potentially enhancing returns, especially during market downturns.
Factor 6: Short Interest (10% Weight)
What It Measures: Short interest captures market sentiment and potential technical dynamics around heavily shorted stocks.
Key Metrics:
- Short Interest as % of Float
- Days to Cover Ratio
- Change in Short Interest
- Short Interest Rank vs. Historical Levels
- Institutional Ownership Changes
Academic Evidence: Research by Asquith, Pathak, and Ritter (2005) shows that heavily shorted stocks tend to underperform, while Dechow et al. (2001) demonstrate that short sellers are skilled at identifying overvalued stocks.
Market Dynamics: Short interest matters because:
- Information Signal: Short sellers often identify fundamental problems early
- Technical Pressure: Heavy shorting creates downward price pressure
- Squeeze Potential: Extremely high short interest can lead to short squeezes
- Sentiment Indicator: Short interest reflects professional skepticism
Weight Rationale: Short interest receives a 10% weight as a sentiment and technical indicator. While not as fundamental as quality or value, it provides valuable insight into professional investor positioning and potential technical catalysts.
Computing Composite Scores and Quintile Ratings
Our ranking system transforms raw factor scores into actionable investment ratings through a systematic process:
Step 1: Factor Score Calculation
For each factor, we:
- Collect Raw Data: Gather fundamental and price data for all stocks
- Calculate Metrics: Compute individual metrics within each factor
- Normalize Scores: Convert metrics to percentile ranks (0-100)
- Combine Metrics: Weight and average metrics within each factor
- Sector Adjust: Apply sector-neutral adjustments where appropriate
Step 2: Composite Score Construction
The composite score combines all six factors using our research-based weights:
| Factor | Weight | Rationale |
|---|---|---|
| Quality/Profitability | 30% | Most persistent factor across time periods |
| Momentum | 25% | Strongest short-term predictive power |
| Value | 15% | Long-term mean reversion tendency |
| Investment | 10% | Capital allocation discipline signal |
| Stability/Low Vol | 10% | Risk management and quality overlap |
| Short Interest | 10% | Sentiment and technical dynamics |
Composite Score Formula:
Composite Score = (0.30 × Quality) + (0.25 × Momentum) + (0.15 × Value) + (0.10 × Investment) + (0.10 × Stability) + (0.10 × Short Interest)
Step 3: Quintile Rating Assignment
We convert composite scores into intuitive quintile ratings:
| Rating | Percentile Range | Description | Expected Characteristics |
|---|---|---|---|
| Strong Buy | 80-100th percentile | Top 20% of stocks | High quality, strong momentum, reasonable valuation |
| Buy | 60-80th percentile | Above-average prospects | Good fundamentals with some positive catalysts |
| Hold | 40-60th percentile | Neutral/mixed signals | Balanced factor exposure, no clear direction |
| Sell | 20-40th percentile | Below-average prospects | Weakening fundamentals or negative momentum |
| Strong Sell | 0-20th percentile | Bottom 20% of stocks | Poor quality, negative momentum, overvalued |
Dynamic Rebalancing
Our rankings update monthly to reflect:
- New earnings reports and financial data
- Price momentum changes
- Valuation metric updates
- Short interest fluctuations
- Sector rotation effects
This ensures our rankings remain current and actionable for investment decisions.
Using Blank Capital Rankings: A Practical Guide
Our stock rankings are designed to be intuitive yet powerful. Here's how to maximize their value:
Screening and Discovery
Top-Down Approach:
- Start with our overall rankings to identify Strong Buy candidates
- Filter by sector or market cap to match your investment style
- Review individual stock pages for detailed factor breakdowns
- Conduct fundamental analysis on shortlisted names
Factor-Specific Screening:
- Quality Focus: Screen for high ROE, low debt, stable earnings
- Momentum Play: Look for recent price strength and earnings revisions
- Value Hunting: Filter for low P/E, P/B ratios with quality overlays
- Defensive Positioning: Emphasize low volatility and high stability scores
Portfolio Construction
Core Holdings: Focus on Strong Buy and Buy rated stocks with:
- High composite scores (80th+ percentile)
- Strong quality metrics (top quartile ROE, margins)
- Positive momentum trends
- Reasonable valuations
Diversification Guidelines:
- Spread holdings across sectors to avoid concentration risk
- Balance factor exposures (don't over-concentrate in momentum)
- Consider market cap diversification
- Monitor correlation between holdings
Risk Management:
- Avoid Strong Sell rated stocks unless contrarian conviction is high
- Monitor factor score changes for existing holdings
- Use stop-losses on momentum-driven positions
- Rebalance quarterly based on updated rankings
Sector Rotation Strategy
Our rankings excel at identifying sector rotation opportunities:
| Market Phase | Favored Factors | Sector Focus | Strategy |
|---|---|---|---|
| Early Recovery | Momentum, Value | Cyclicals, Financials | Buy beaten-down quality names |
| Mid-Cycle Growth | Quality, Momentum | Technology, Consumer | Focus on earnings growth |
| Late Cycle | Quality, Stability | Defensives, Utilities | Emphasize balance sheet strength |
| Recession | Quality, Low Vol | Staples, Healthcare | Capital preservation focus |
Integration with Fundamental Analysis
Our quantitative rankings work best when combined with qualitative research:
Quantitative Screening + Fundamental Validation:
- Use rankings to identify candidates
- Read recent earnings calls and SEC filings
- Analyze competitive positioning and industry trends
- Assess management quality and capital allocation
- Consider ESG factors and regulatory risks
Red Flags to Watch:
- High composite score but deteriorating fundamentals
- Strong momentum but unsustainable business model
- Cheap valuation but secular headwinds
- Low volatility but declining market share
Model Limitations and Important Disclaimers
While our 6-factor model incorporates decades of academic research and empirical testing, no quantitative system is perfect. Understanding these limitations is crucial for successful implementation:
Factor Timing and Cyclicality
Performance Cycles: Individual factors experience periods of outperformance and underperformance:
- Value underperformed growth from 2010-2020 but has resurged since 2021
- Momentum can reverse sharply during market crashes
- Quality may underperform during speculative bubbles
- Low Volatility tends to lag in strong bull markets
Implication: Factor diversification is essential—no single factor works all the time.
Market Structure Changes
Evolving Markets: Factor effectiveness can change due to:
- Increased Factor Investing: Popular factors may become overcrowded
- Algorithmic Trading: Faster price discovery may reduce factor persistence
- Market Concentration: Mega-cap dominance can skew factor performance
- Regulatory Changes: New rules may impact factor dynamics
Data Quality and Survivorship Bias
Historical Limitations:
- Backtest results may not reflect real-world trading costs
- Survivorship bias in historical data (delisted companies excluded)
- Look-ahead bias in factor construction
- Point-in-time data availability challenges
Sector and Style Biases
Inherent Biases: Our model may have implicit biases toward:
- Large-cap stocks with more stable data
- Certain sectors that align with factor definitions
- Established companies over emerging growth stories
- U.S. market dynamics vs. international markets
Black Swan Events
Unpredictable Risks: Quantitative models struggle with:
- Pandemic-like disruptions (COVID-19)
- Geopolitical crises (wars, trade disputes)
- Regulatory shocks (sudden rule changes)
- Technology disruptions (AI, blockchain)
- Accounting scandals or fraud
Important Investment Disclaimers
Not Investment Advice: This article and our stock rankings are for informational and educational purposes only. They should not be construed as personalized investment advice or recommendations to buy or sell specific securities.
Past Performance: Historical factor performance does not guarantee future results. All investments carry risk of loss, and you should never invest more than you can afford to lose.
Do Your Own Research: Our rankings are starting points for further analysis, not complete investment solutions. Always conduct your own fundamental research and consider your personal financial situation, risk tolerance, and investment objectives.
Professional Advice: Consider consulting with a qualified financial advisor before making investment decisions, especially for substantial portfolio allocations.
Model Evolution: We continuously refine our methodology based on new research and market developments. Factor weights and definitions may change over time.
Conclusion: Putting It All Together
Blank Capital's 6-factor quantitative model represents the synthesis of decades of academic research into a practical investment tool. By combining Quality, Momentum, Value, Investment, Stability, and Short Interest factors, we create a comprehensive view of each stock's investment attractiveness.
The model's strength lies in its diversification across multiple sources of return and its systematic approach to stock selection. Rather than relying on gut instinct or single metrics, our rankings provide a data-driven foundation for investment decisions.
However, successful investing requires more than just quantitative rankings. The best results come from combining our systematic approach with fundamental analysis, risk management, and a clear understanding of your investment objectives.
Whether you're building a core equity portfolio, seeking tactical opportunities, or conducting investment research, our rankings provide a powerful starting point. Explore our current rankings and individual stock analyses to see how quantitative investing can enhance your investment process.
Remember: in the complex world of equity markets, having a systematic, research-based approach isn't just an advantage—it's essential for long-term investment success.
This article is for informational purposes only and should not be construed as investment advice. Please read our full disclaimers and consider your personal financial situation before making investment decisions.