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This guide provides comprehensive coverage of each factor in the Parallax framework, including the specific metrics used, academic foundations, when each factor works best, and implementation considerations.

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Value Factor

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Academic Foundation

Benjamin Graham and David Dodd’s “Security Analysis” (1934), Fama-French value premium research (1992).

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Key Metrics

Value Score = Percentile Rank based on:
  P/E Ratio      - Earnings valuation vs. sector peers
  P/B Ratio      - Book value relative to peers
  EV/EBITDA      - Cash flow-based valuation
  P/S Ratio      - Revenue multiple analysis
  Dividend Yield - Income component assessment

→ Precision-weighted ensemble of these metrics
→ Combined into single percentile score (0-10)

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When Value Works

Market ConditionValue Performance
Market recovery after declinesStrong
Rising interest rate environmentsStrong
Economic expansion with improving earningsStrong
Periods of reduced speculationStrong

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Implementation Risks

Value Traps: Cheap stocks that remain cheap or decline further due to fundamental issues.Secular Decline: Industries facing permanent disruption may appear cheap but lack recovery potential.Quality Issues: Low prices may reflect legitimate fundamental problems.Timing: Value can underperform for extended periods (e.g., 2017-2020).

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Quality Factor

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Academic Foundation

Warren Buffett’s quality principles, Piotroski F-Score research (2000), Asness, Frazzini, and Pedersen quality factor research (2019).

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Key Metrics

Quality Score = Percentile Rank based on:
  Profitability        - ROE, ROA, profit margins
  Financial Strength   - Balance sheet quality metrics
  Earnings Quality     - Sustainability of reported earnings
  Business Stability   - Revenue predictability and growth
  Management Quality   - Capital allocation efficiency
  Forensic Accounting  - Red flag detection and penalties

→ Precision-weighted ensemble of these metrics
→ Combined into single percentile score (0-10)

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Forensic Accounting Analysis

Our Quality scoring includes forensic accounting checks:
  • Detects earnings manipulation red flags (unusual accruals, revenue recognition issues)
  • Identifies balance sheet warning signs (off-balance sheet items, hidden liabilities)
  • Penalizes scores for accounting irregularities and disclosure quality issues
  • Continuous monitoring of financial statement quality

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Quality Advantages

Defensive Characteristics: Outperforms during market stressConsistency: More predictable performance patternsCompounding: Quality characteristics tend to persist over timeRisk Reduction: Lower volatility and drawdown periods

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Momentum Factor

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Academic Foundation

Jegadeesh and Titman momentum research (1993), behavioral finance herding and under-reaction research.

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Key Metrics

Momentum Score = Percentile Rank based on:
  6-Month Price Return   - Recent performance trajectory
  12-Month Price Return  - Longer-term trend strength
  Earnings Revisions     - Analyst estimate changes
  Revenue Growth         - Sales momentum analysis
  Relative Strength      - Peer comparison metrics

→ Precision-weighted ensemble of these metrics
→ Combined into single percentile score (0-10)

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Implementation Challenges

ChallengeDescription
Reversal RiskStrong trends can reverse quickly, especially in stress periods
VolatilityHigher volatility than other factors
CrowdingPopular momentum trades can become overcrowded
Transaction CostsHigher turnover increases implementation costs

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Defensive Factor

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Academic Foundation

Low volatility anomaly research (Baker, Bradley, Wurgler 2011), minimum variance portfolio theory.

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Key Metrics

Defensive Score = Percentile Rank based on:
  Low Volatility        - Historical price stability
  Low Beta              - Market sensitivity measurement
  Earnings Stability    - Consistency of financial results
  Dividend Quality      - Payment history and sustainability
  Business Defensiveness - Recession resistance

→ Precision-weighted ensemble of these metrics
→ Combined into single percentile score (0-10)

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Defensive Benefits

Downside Protection: Outperforms during market declinesRisk-Adjusted Returns: Often superior Sharpe ratios over full cyclesStability: Lower volatility and more predictable outcomesCrisis Performance: Valuable during uncertain periods

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Size Factor

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Academic Foundation

Banz small firm effect (1981), Fama-French size factor inclusion (1992), international evidence across global markets.

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Updated Role

The size factor has evolved from a standalone return premium to a signal amplifier that enhances other factor effectiveness:
RoleDescription
Attention GapsSmaller companies receive less analyst coverage
Uninformed FlowsPassive funds typically prefer large/mega-cap stocks
Factor InteractionSize amplifies value, momentum, and quality signals
Market InefficienciesLess efficient pricing in smaller company segments

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Implementation Considerations

Higher Volatility: Small-cap stocks typically more volatileLiquidity Risk: Lower liquidity impacts trading costsQuality Variation: Wide range of quality among small companiesEconomic Sensitivity: Greater exposure to economic cycles
Important: Size is NOT included in the Overall Score calculation. It is accounted for during portfolio construction to manage liquidity, capacity, and factor amplification.

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Tactical Factor

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Academic Foundation

The Tactical factor captures short-term opportunities arising from temporary supply-demand imbalances and liquidity dislocations:
  • Kyle Model (1985): Informed trader behavior and price impact
  • Glosten-Milgrom Model (1985): Market maker pricing and informed trading probability
  • Campbell, Grossman & Wang (1993): Non-fundamental trading and return reversals

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Signal Categories

  • Institutional Flow Analysis: Fund flows creating mechanical pressure
  • Insider Trading Patterns: Corporate insider activity indicating information advantages
  • Smart Money Tracking: Following institutional trades with superior information
  • Forced Selling Events: Margin calls and liquidations creating opportunities
  • Gap Analysis: Price gaps that may over/under-react to information
  • Volume Anomalies: Unusual volume indicating informed or forced trading
  • Relative Strength Divergences: Temporary dislocations vs. sector/market
  • Options Flow: Large positions indicating directional bets or hedging
  • Earnings Reactions: Post-announcement drift and overreaction patterns
  • Index Changes: Addition/removal creating predictable flows
  • Corporate Actions: Spin-offs and mergers generating forced trading
  • Calendar Effects: End-of-period and rebalancing patterns

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Tactical Implementation Process

1

Signal Identification

Continuous scanning of volume, price, and flow patterns with ML models identifying anomalous trading behavior
2

Signal Validation

Backtesting across multiple time periods, out-of-sample testing, and decay analysis to understand signal half-life
3

Execution

Optimal entry/exit timing, transaction cost minimization, and risk-adjusted position sizing

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Factor Interactions and Combinations

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Complementary Factors

Value + Quality

High-quality companies trading at reasonable prices reduce value trap risk while maintaining value exposure

Quality + Momentum

Strong companies with positive trends often exhibit sustained outperformance

Defensive + Value

Low-volatility stocks with value screening provide attractive risk-adjusted returns

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Market Cycle Relationships

Market PhaseFavored Factors
Early Bull MarketMomentum and Size often outperform
Mid-Bull MarketQuality and growth factors typically strong
Late Bull MarketDefensive factors gain importance
Bear MarketDefensive and Quality provide protection

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Economic Cycle Patterns

Economic PhaseFavored Factors
RecessionDefensive and Quality outperform
Early RecoveryValue and small-cap often lead
Growth PhaseMomentum and Quality excel
Late CycleDefensive positioning becomes important

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Practical Application

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Using Factor Scores

Individual Security Analysis:
  • Review factor scores (0-10 scale) for each holding
  • Identify factor concentrations and gaps in portfolio
  • Compare scores to sector and market averages
  • Track score changes over time for trend identification
Portfolio-Level Factor Analysis:
  • Aggregate individual scores to portfolio level
  • Identify unintended factor bets and concentrations
  • Assess factor balance and diversification effectiveness
  • Monitor factor drift over time