Relative Strength (RS) Strategies – Summary Notes from Zoe’s Talk (STA Event)

🧠 Relative Strength (RS) Strategies – Summary Notes from Zoe’s Talk (STA Event)

🏛️ 1. Background & Research Origins

📚 Academic Foundations

  • Few academic papers explore RS despite its long-standing use by practitioners.

  • Key papers discussed:

    • George Chestnutt (1968): Early RS rotation models.

    • Robert Levy (1968, 1971): RS as a predictor of future performance.

    • James O’Shaughnessy: “What Works on Wall Street” – ranks RS highly among factors.

    • Fosback’s work and IBD (Investor’s Business Daily): More modern interpretations with momentum emphasis.

    • Perry Kaufman: RS concepts via trend following, volatility-based exits.

📈 2. Core RS Model Structures

Model Characteristics
Chestnutt Sector-aware, chooses top stocks from top-performing sectors. Slow-moving.
Levy Focuses on long-term past performance (6–12 months). More academic and slower to react.
IBD Shorter-term, front-weighted, aggressive ranking. High turnover.
Fosback Smoother, medium-term model with lower turnover. Strong long-term trends but needs hedging.

🧮 3. RS Calculation Methods

  • Most RS systems use rate-of-change (ROC): comparing a stock’s current price to its past over a lookback period.

  • RS is typically calculated as:

    • Stock vs. index (e.g., S&P 500).

    • Or stock vs. its own history (as used in Zoe’s presentation).

  • Volatility/beta adjustments help penalize high-volatility names in rankings.

🧰 4. Portfolio Construction & Implementation

⚖️ Diversification

  • Over-diversification can dilute returns and increase market correlation.

  • Some successful strategies:

    • Running RS within sector universes (e.g., Tech-only RS).

    • Using sector ETFs as proxies for ranking.

🔃 Rebalancing & Rotation

  • RS portfolios thrive on rotation into strength.

  • Rebalancing frequency (weekly/monthly) affects responsiveness vs. turnover.

⚠️ Managing Volatility

  • High-RS stocks today see 15–20% daily moves (vs. ~6% in 2018).

  • Likely influenced by algorithmic/risk-on risk-off flows.

  • Beta-adjusted RS scores reduce overexposure to volatile names.

📉 5. Drawdowns & Hedging

🧷 Hedging Techniques

  • Set rules before live trading (e.g., moving average crossovers, inverse ETFs).

  • Don’t rely on intuition—follow the model.

💡 Diversifying Systems

  • Combine RS with:

    • Trend following (e.g., breakout-breakdown)

    • Mean reversion

    • Long-short RS

  • This helps smooth equity curve and perform across market regimes.

🛠️ 6. Practical Observations & Lessons

⚖️ Trade-offs in RS Models

  • Fosback: Lower turnover, but slower to exit – needs a hedge.

  • IBD-style: Fast-reacting, but more volatile – may need smoothing.

  • Most real-world implementations are customized hybrids of these core ideas.

👀 Psychological Challenges

  • Temptation to override model (e.g., not selling a consolidating stock) is common.

  • Discipline is crucial—model often outperforms human judgment.

📈 Winners Drive Returns

  • Small number of holdings often account for the majority of gains.

  • Thus, diversification within RS helps capture winners while rotating out of laggards.

📊 7. Testing & Timeframes

🕒 Daily vs. Monthly Data

  • Monthly RS models worked well historically but underperform in recent years due to faster-moving markets.

  • Consider using monthly bars with short lookbacks as a compromise.

  • Ideal modern RS periods: 63-day or 126-day ROC (~3–6 months).

🧪 Testing Advice

  • Always backtest your RS strategy across:

    • Different timeframes

    • Market regimes

    • Portfolio sizes

    • Volatility levels

  • Simplicity often wins—the more complex the model, the more fragile it may be.

💼 8. Institutional & Personal Use

Ballinger Capital RS Strategy:

  • Zoe runs RS strategies for ESG-focused portfolios and personal accounts.

  • Plans to offer RS products to clients later this year, tailored for acceptable drawdown levels.

  • Emphasizes client tolerance: what’s acceptable for a PM may be too volatile for an investor.

ETF-Based RS Portfolios:

  • RS works well across:

    • Industry/sector ETFs

    • Asset class/style rotation (growth vs. value, etc.)

  • Works if ETF universe is broad enough for meaningful rotation.

🔚 Final Thoughts

  • Relative Strength remains one of the most powerful and robust factors when implemented with discipline.

  • Use classic models (Levy, Chestnutt, Fosback, IBD) as starting points, then adjust based on:

    • Volatility

    • Market speed

    • Portfolio constraints

  • Diversify not just by assets, but by strategy (RS, mean reversion, trend following).

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