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Our scoring system transforms complex financial data into clear, actionable investment insights. Built on Fama-French factor research and other academic foundations, our models incorporate real-time data and contemporary measures.

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Score Calculation Process

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Individual Factor Scores (0-10 Scale)

Scores represent rounded percentiles within the investment universe:
Score RangePercentileInterpretation
8-10Top quartileStrong to exceptional performance
6-7Above medianModerately attractive
4-5Below medianNeutral to weak
0-3Bottom quartilePoor to very poor performance
Percentile Interpretation: A Value score of 10 means the stock is in the top 10% cheapest relative to fundamentals. A Quality score of 3 means it’s in the bottom 20% for business quality metrics.

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Overall Parallax Score Integration

The Overall Score uses a machine learning model that dynamically adapts to changing market environments: 
Overall Score = ML-Optimized Combination of:
  Value Factor     (10-40% dynamic weight)
  Quality Factor   (10-40% dynamic weight)
  Momentum Factor  (10-40% dynamic weight)
  Defensive Factor (10-40% dynamic weight)
  Tactical Factor  (10-40% dynamic weight)

Note: Size is NOT included in Overall Score calculation

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ML Optimization Process

1

Predict Returns

Models forecast expected returns for each factor based on current market regime
2

Estimate Covariances

Predicts how factors will correlate in the near-term
3

Optimize Weights

Allocates to factors with best risk-adjusted return forecasts
4

Enforce Limits

Each factor weight constrained between 10% (minimum) and 40% (maximum)
5

Periodic Updates

Weights recalculated periodically as market conditions evolve
Example: During high-volatility periods, the ML model might increase Defensive factor weight to 35% and reduce Momentum to 15%, automatically adapting your strategy to market conditions.

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Size Factor Treatment

The Size factor has a unique role in the framework:
AspectTreatment
Overall ScoreSize does NOT contribute to individual stock recommendations
Portfolio ConstructionSize is systematically incorporated during portfolio building
Liquidity ManagementEnsures adequate trading capacity and minimizes market impact
Capacity ConstraintsHelps manage strategy scalability and position sizing
Factor AmplificationSmall-cap exposure can amplify other factor signals

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Recommendation Generation

Overall scores translate directly into investment recommendations:
Score RangeRecommendation
8.5-10.0STRONG BUY
6.5-8.4BUY
3.5-6.4HOLD
1.5-3.4SELL
0.0-1.4STRONG SELL

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Score Validation and Quality Control

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Multi-Source Verification

  • Cross-validation across fundamental, technical, and alternative data
  • Peer comparison within sector and market cap categories
  • Historical score performance tracking
  • Outlier detection and manual review processes

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Dynamic Adjustment via ML Model

Adjustment TypeDescription
Market Regime DetectionIdentifies changing market conditions (bull, bear, high-vol, low-vol)
Adaptive Factor WeightsML model adjusts Overall Score weights periodically within 10-40% bounds
Covariance ForecastingPredicts how factors will correlate in current regime
Return PredictionEstimates near-term factor performance based on market environment
Sector-Specific AdjustmentsScoring refined for industry-specific characteristics
Event-Driven ModificationsEarnings season and corporate action adjustments

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Performance Attribution

Understanding where returns come from through factor decomposition: 
Example 6-Month Performance Attribution:
Total Return: +8.3%
  Market Beta (1.1):      +5.2%
  Value Factor (0.4):     -0.8%
  Momentum Factor (0.6):  +2.1%
  Quality Factor (0.5):   +1.4%
  Size Factor (-0.2):     -0.3%
  Tactical Factor (0.2):  +0.4%
  Security Selection:     +0.3%

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Factor Risk Analysis

Understanding portfolio risk sources through factor decomposition:
  • Systematic Risk: Market and factor exposures
  • Specific Risk: Individual security risk
  • Concentration Risk: Factor and sector concentrations
  • Correlation Risk: How holdings relate during stress periods

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Continuous Improvement Process

Monthly Reviews

Individual factor performance analysis, interaction assessment, and scoring accuracy validation

Quarterly Updates

Factor weight optimization, new data source integration, and model refinement

Annual Review

Complete framework assessment, academic research updates, and technology enhancements