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Monitors a 'leader' stock and trades a 'lagger' stock based on leader movements. When the leader moves significantly, trades the lagger expecting it to follow within a few days.
This strategy exploits time delays in how information propagates through related stocks: 1. Relationship Discovery: Analyze pairs of stocks to find lead-lag relationships using cross-correlation 2. Statistical Validation: For each potential pair, test if leader moves reliably predict lagger moves …
Best for: Traders interested in exploiting information flow patterns between related securities. Works best for stocks with clear economic relationships (supplier-customer, same industry). Requires ongoing monitoring and …
Techniques Used
Cross-correlation
Time-series lag analysis
Signal following
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Trades two correlated stocks based on spread z-score. Goes long the underperformer and short the outperformer when spread diverges, profiting as they converge back.
Pairs trading exploits temporary mispricings between related stocks: 1. Pair Selection: Identify highly correlated stock pairs within the same sector 2. Cointegration Testing: Verify the pair has a stable, mean-reverting spread (Engle-Granger test) 3. Spread Calculation: Compute the price ratio …
Best for: Market-neutral traders seeking steady returns uncorrelated with overall market direction. Works well for those comfortable with short selling. Requires capital for both long and short …
Techniques Used
Cointegration testing
Spread trading
Mean reversion
Dollar-neutral
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Analyzes correlations between different asset classes (stocks and cryptocurrencies) to find trading opportunities. Uses moves in one asset class to predict moves in correlated assets in the other class.
This strategy exploits correlations between different asset classes: 1. Correlation Calculation: Calculate rolling correlations between stocks and crypto 2. Relationship Identification: Find stock-crypto pairs with high correlation 3. Lead-Lag Analysis: Determine if one asset class tends to move before the …
Best for: Traders who follow multiple asset classes and want to exploit cross-market information flow. Works best during periods of stable cross-asset correlations. Requires monitoring both stock …
Techniques Used
Cross-asset analysis
Alternative data correlation
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Recognizes that correlations between assets change based on market regime (bull, bear, high volatility). Adapts correlation trading strategies dynamically as market conditions shift, using the VIX volatility index and other indicators to detect regime changes.
This strategy accounts for how correlations change across market conditions: 1. Regime Detection: Use VIX, market breadth, and trend indicators to classify current regime 2. Correlation Recalibration: Calculate regime-specific correlation matrices - Bull regime: Use standard correlations from recent bull …
Best for: Sophisticated traders who understand that correlations are dynamic, not static. Ideal for those who've experienced correlation strategy failures during market stress. Requires ongoing monitoring and …
Techniques Used
Regime detection
Dynamic correlation
Risk adjustment
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Trades the difference between implied correlation (from index options) and realized correlation (from stock returns). When implied correlation is high relative to realized, sells index volatility and buys component volatility, or vice versa.
Dispersion trading exploits the relationship between index and component volatilities: 1. Implied Correlation Calculation: From index option prices, back out the implied correlation between components Formula: Index Variance = Sum(Weight² × Component Variance) + 2 × Sum(Weight_i × Weight_j × …
Best for: Professional options traders and volatility funds with sophisticated infrastructure. Requires options trading capability and understanding of volatility surface dynamics. Not suitable for beginners or those …
Techniques Used
Dispersion analysis
Implied vs realized correlation
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