📊 Objectives 🎯 Overview 🏦 Treasuries 🏛️ Preferred 📈 Equity Pairs ⚡ Futures 🔧 Implementation ⚠️ Risk Management

Investment Objectives

Primary Objective: Generate consistent, low-risk profits by exploiting temporary price discrepancies between related financial instruments, with a focus on market-neutral strategies.

Risk Tolerance: Moderate, accepting controlled volatility while prioritizing comprehensive risk management through hedging and disciplined position sizing.

Time Horizon: Short to medium-term (days to months per trade), optimized for different asset classes and convergence patterns.

Liquidity Requirements: Focus on highly liquid securities including Treasuries, preferred stocks, large-cap equities, and actively traded futures contracts.

1-2%
Target Position Size
30-90
Days Hold Period
2σ
Entry Threshold
0.8+
Min Correlation

Strategy Overview

Relative value arbitrage involves identifying pairs of securities with historically correlated prices that temporarily diverge, then taking strategic long and short positions to profit when their prices converge. Our systematic approach combines statistical analysis with disciplined risk management across four distinct asset classes.

🏦

On-the-Run vs. Off-the-Run Treasuries

Exploiting liquidity premium disparities in Treasury securities

Strategy Description: On-the-run Treasuries are the most recently issued U.S. Treasury bonds or notes of a specific maturity, while off-the-run Treasuries are older issues of the same maturity. On-the-run securities trade at a liquidity premium, creating arbitrage opportunities when spreads widen abnormally.

Implementation Process

  1. Monitor Yield Spreads

    Track yield spreads between on-the-run and off-the-run Treasuries of identical maturity using 6-12 months of historical data to establish statistical baselines.

    Example: 10-year on-the-run yield: 3.5%, off-the-run: 3.7%. Historical mean spread: 0.1%, current spread: 0.2% signals potential opportunity.
  2. Execute Trade Entry

    When spread exceeds historical mean by 2 standard deviations, buy off-the-run Treasury (higher yield, lower price) and short sell on-the-run Treasury (lower yield, higher price).

    Position Size: $100,000 portfolio Long: $10,000 off-the-run 10-year Treasuries Short: $10,000 on-the-run 10-year Treasuries Spread Entry: 0.2% (2σ above 0.1% mean)
  3. Trade Exit Strategy

    Close positions when spread reverts to historical mean or implement time-based exit within 30-60 days. Use stop-loss if spread widens by additional 1 standard deviation.

Risk Management Protocols

  • Limit individual position size to 1-2% of total portfolio capital
  • Monitor Federal Reserve policy changes and yield curve shifts
  • Ensure adequate margin capacity for short Treasury positions
  • Account for bid-ask spreads and transaction costs in profit calculations
Market Inefficiency Driver: Temporary liquidity premiums create pricing disparities that market forces naturally correct over time, providing consistent profit opportunities for patient arbitrageurs.
🏛️

Bank Preferred Tranches

Capitalizing on structural mispricing across preferred stock issuances

Strategy Description: Banks issue multiple preferred stock tranches with varying coupon rates, reset dates, and structural features. Market inefficiencies arise from differences in liquidity, investor demand, and complexity, creating exploitable price disparities between economically similar instruments.

Implementation Process

  1. Tranche Identification & Analysis

    Select pairs of preferred tranches from the same issuing bank with similar risk characteristics, seniority levels, and economic features.

    Example Pair: Bank of America BAC-L (7.25% coupon) vs. BAC-M (6% coupon) - both senior preferreds with similar call features.
  2. Spread Monitoring & Statistical Analysis

    Calculate normalized price spreads and establish statistical parameters using 6-12 months of historical data, accounting for liquidity premiums and reset date volatility.

    Security Current Price Coupon Call Date Spread Component
    BAC-L $25.00 7.25% 2026-01-15 +$1.00
    BAC-M $24.00 6.00% 2026-03-15 -$1.00
  3. Position Execution

    Enter spread trade when price differential exceeds 1-2 standard deviations from historical mean, buying undervalued tranche and shorting overvalued tranche.

Risk Management Protocols

  • Monitor bank-specific credit events and regulatory changes
  • Account for dividend payment dates and ex-dividend effects
  • Ensure adequate liquidity for both long and short positions
  • Track interest rate sensitivity and duration matching
Market Inefficiency Driver: Preferred tranches from the same issuer share similar credit risk profiles, making persistent price disparities economically irrational and subject to convergence.
📈

Equity Pairs Trading

Statistical arbitrage using highly correlated stock pairs

Strategy Description: This strategy involves trading pairs of highly correlated stocks (correlation > 0.8) within the same sector. When their price relationship deviates significantly from historical norms, we establish opposing positions expecting mean reversion to the established correlation pattern.

Implementation Process

  1. Pair Selection & Correlation Analysis

    Identify stocks with robust historical correlation (>0.8) over 6-12 months, preferably within the same sector or industry group.

    Example Pairs: AAPL/MSFT (Tech), JPM/BAC (Banking), XOM/CVX (Energy) - all showing 0.85+ correlation coefficients.
    # Correlation Calculation Example import pandas as pd import numpy as np # Historical price data aapl_returns = stock_data['AAPL'].pct_change() msft_returns = stock_data['MSFT'].pct_change() correlation = aapl_returns.corr(msft_returns) # Target: correlation > 0.8
  2. Spread Calculation & Normalization

    Calculate normalized price ratios or spreads, accounting for absolute price differences. Establish statistical parameters for mean reversion identification.

    Metric AAPL/MSFT Ratio Statistical Significance Action Trigger
    Historical Mean 1.20 6-month average Baseline
    Standard Deviation 0.05 Historical volatility Entry threshold
    Current Ratio 1.30 2σ deviation Short AAPL/Long MSFT
  3. Trade Execution & Position Management

    Enter market-neutral positions when spread deviates by 2 standard deviations, maintaining equal dollar exposure in both securities.

    Trade Setup: AAPL/MSFT ratio at 1.30 vs. historical 1.20
    Long Position: $10,000 MSFT
    Short Position: $10,000 AAPL
    Target Exit: Ratio return to 1.20

Risk Management Protocols

  • Maintain strict position sizing limits (1-2% of portfolio per pair)
  • Monitor sector-specific risks and regulatory changes
  • Implement stop-loss orders at 1 additional standard deviation
  • Ensure equal dollar exposure for market neutrality
  • Track earnings announcements and company-specific events
Market Inefficiency Driver: Highly correlated stocks tend to revert to their historical price relationships due to similar business fundamentals and market forces, creating predictable convergence opportunities.
⚡

Spread Trading in Futures

Exploiting price differentials in related futures contracts

Strategy Description: Spread trading involves simultaneous buying and selling of related futures contracts, either between different commodities (inter-market spreads) or different expiration months of the same commodity (intra-market/calendar spreads). Profits arise from changes in the price differential rather than absolute price movements.

Implementation Process

  1. Spread Opportunity Identification

    Analyze related futures contracts for statistical anomalies in their price relationships, considering both inter-market and calendar spread opportunities.

    Inter-Market
    Related Commodities
    Calendar
    Different Months
    Inter-Market Example: Crude Oil vs. Natural Gas historical ratio analysis
    Calendar Example: March 2025 vs. June 2025 crude oil futures spread
  2. Statistical Analysis & Entry Signals

    Calculate historical spread relationships and identify entry points based on statistical deviations from established norms.

    Spread Type Current Level Historical Mean Std Dev Z-Score Action
    WTI/NG Ratio 12:1 10:1 0.8 2.5 Buy NG/Sell WTI
    Mar/Jun WTI $6.00 $3.00 $1.20 2.5 Buy Mar/Sell Jun
  3. Trade Execution & Management

    Execute spread trades when statistical thresholds are met, managing positions through convergence or time-based exit strategies.

    # Example Trade Execution Inter-Market Spread: - Long: 1 Natural Gas contract - Short: 1 Crude Oil contract - Entry Ratio: 12:1 (2.5σ above mean) - Target: Ratio convergence to 10:1 Calendar Spread: - Long: March 2025 WTI - Short: June 2025 WTI - Entry Spread: $6.00 - Target: Convergence to $3.00

Risk Management Protocols

  • Account for margin requirements and leverage effects
  • Monitor roll costs and expiration dates for calendar spreads
  • Track supply/demand fundamentals and geopolitical events
  • Ensure adequate liquidity in both contract months
  • Implement position limits based on volatility and correlation
Market Inefficiency Driver: Futures spreads revert to fundamental relationships driven by storage costs, convenience yields, and supply/demand dynamics, providing systematic profit opportunities.

Implementation Framework

Technology & Data Infrastructure

📊
Data Sources
🔧
Analysis Tools
💼
Execution Platform
⚡
Monitoring Systems

Essential Data Sources

  • 🏦 Treasuries: TreasuryDirect.gov, Bloomberg Terminal, Federal Reserve Economic Data (FRED)
  • 🏛️ Preferred Stocks: FINRA Market Data, Bank investor relations pages, Preferred Stock Channel
  • 📈 Equities: Yahoo Finance API, Alpha Vantage, Quandl, IEX Cloud
  • ⚡ Futures: CME Group, Interactive Brokers, Barchart, TradingView

Execution Requirements

Component Requirement Recommended Platform Key Features
Brokerage Margin + Short Selling Interactive Brokers Global markets, low costs
Data Feed Real-time pricing Alpha Vantage Pro API access, historical data
Analytics Statistical computing Python/R Pandas, NumPy, SciPy
Monitoring Automated alerts TradingView Custom indicators, alerts

Comprehensive Risk Management

🛡️ Multi-Layer Risk Framework

1-2%
Max Position Size
1σ
Stop Loss Trigger
4-6
Max Concurrent Pairs
Daily
Monitoring Frequency

Risk Categories & Mitigation Strategies

  • Model Risk: Regular backtesting, out-of-sample validation, parameter sensitivity analysis
  • Execution Risk: Slippage monitoring, market impact assessment, optimal order sizing
  • Liquidity Risk: Minimum volume requirements, bid-ask spread limits, market depth analysis
  • Correlation Risk: Rolling correlation monitoring, relationship stability tests, regime detection
  • Event Risk: News monitoring, earnings calendar tracking, regulatory change assessment
  • Financing Risk: Margin requirement monitoring, borrowing cost tracking, collateral management

Example Portfolio Application (July 2025)

Strategy Assets Position Size Entry Signal Target Exit Risk Level
Treasury Spread 10Y On/Off-Run $10,000 each 20bp vs 10bp mean Revert to 10bp 🟢 Low
Bank Preferred BAC-L/BAC-M $10,000 each $1.50 vs $0.50 mean Revert to $0.50 🟡 Medium
Equity Pairs AAPL/MSFT $10,000 each 1.30 vs 1.20 ratio Revert to 1.20 🟡 Medium
Futures Spread WTI Near/Far $10,000 equiv $6 vs $3 spread Revert to $3 🟠 High

Strategic Implementation Roadmap

These relative value arbitrage strategies provide a systematic framework for capturing pricing inefficiencies across multiple asset classes. Success requires disciplined execution, robust risk management, and continuous monitoring of statistical relationships. By combining quantitative analysis with market intuition, investors can build a diversified portfolio of market-neutral strategies designed for consistent returns.

Next Steps: Begin with paper trading to validate models, establish data feeds and analytical infrastructure, then gradually deploy capital across strategies as confidence and expertise develop. Regular backtesting and performance attribution analysis will ensure strategy effectiveness remains robust across changing market conditions.