Scoring Methodology - Parallax API

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.

Score Calculation Process

Individual Factor Scores (0-10 Scale)

Scores represent rounded percentiles within the investment universe:

Score Range	Percentile	Interpretation
8-10	Top quartile	Strong to exceptional performance
6-7	Above median	Moderately attractive
4-5	Below median	Neutral to weak
0-3	Bottom quartile	Poor 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.

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

ML Optimization Process

Predict Returns

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

Estimate Covariances

Predicts how factors will correlate in the near-term

Optimize Weights

Allocates to factors with best risk-adjusted return forecasts

Enforce Limits

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

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.

Size Factor Treatment

The Size factor has a unique role in the framework:

Aspect	Treatment
Overall Score	Size does NOT contribute to individual stock recommendations
Portfolio Construction	Size is systematically incorporated during portfolio building
Liquidity Management	Ensures adequate trading capacity and minimizes market impact
Capacity Constraints	Helps manage strategy scalability and position sizing
Factor Amplification	Small-cap exposure can amplify other factor signals

Recommendation Generation

Overall scores translate directly into investment recommendations:

Score Range	Recommendation
8.5-10.0	STRONG BUY
6.5-8.4	BUY
3.5-6.4	HOLD
1.5-3.4	SELL
0.0-1.4	STRONG SELL

Score Validation and Quality Control

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

Dynamic Adjustment via ML Model

Adjustment Type	Description
Market Regime Detection	Identifies changing market conditions (bull, bear, high-vol, low-vol)
Adaptive Factor Weights	ML model adjusts Overall Score weights periodically within 10-40% bounds
Covariance Forecasting	Predicts how factors will correlate in current regime
Return Prediction	Estimates near-term factor performance based on market environment
Sector-Specific Adjustments	Scoring refined for industry-specific characteristics
Event-Driven Modifications	Earnings season and corporate action adjustments

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%

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

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

​Score Calculation Process

​Individual Factor Scores (0-10 Scale)

​Overall Parallax Score Integration

​ML Optimization Process

​Size Factor Treatment

​Recommendation Generation

​Score Validation and Quality Control

​Multi-Source Verification

​Dynamic Adjustment via ML Model

​Performance Attribution

​Factor Risk Analysis

​Continuous Improvement Process

Monthly Reviews

Quarterly Updates

Annual Review

Score Calculation Process

Individual Factor Scores (0-10 Scale)

Overall Parallax Score Integration

ML Optimization Process

Size Factor Treatment

Recommendation Generation

Score Validation and Quality Control

Multi-Source Verification

Dynamic Adjustment via ML Model

Performance Attribution

Factor Risk Analysis

Continuous Improvement Process