The Evolution of Joel Greenblatt's Strategy: From Gotham to Today

The Evolution of Joel Greenblatt's Strategy: From Gotham to Today

Joel Greenblatt’s investment approach has evolved significantly since his Gotham Asset Management days. His initial Magic Formula strategy focused on quantitative screens to identify undervalued, high-quality stocks. Experienced traders can benefit from unpacking how this method matured and how its principles translate to current market conditions.

Defining the Original Edge

Greenblatt’s Magic Formula ranks stocks based on two key metrics: earnings yield and return on capital (ROC). Earnings yield is EBIT divided by enterprise value, giving preference to companies trading cheap relative to operating profits. ROC is EBIT divided by tangible capital employed, emphasizing efficient use of capital.

The strategy targets the top 20-30 stocks from the combined ranking, typically in a universe of 1000–1500 stocks with market caps above $200 million. Historically, this screen generated an annualized excess return of approximately 15-20% over the long term.

Entry Rules: Precision and Discipline

Initially, the entry rule centered on buying the top-ranked stocks within 30 business days after the quarter’s earnings release. This typically results in a basket of 20-30 names, such as CVS Health (CVS), Franklin Resources (BEN), or Discover Financial (DFS) at different points over the years.

Today, discretionary screening adapts the formula by:

• Increasing the minimum market cap to $1 billion to reduce illiquidity.
• Supplementing EBIT with EBITDAR for cyclical sectors like energy or airlines.
• Employing a 3-month average for EBIT to smooth earnings volatility.

Transactions take place near the market open, ensuring execution prices relative to the day’s range. Entry size per position starts at 3-5% of the total portfolio.

Exit Rules and Stop Placement

Greenblatt originally suggested holding each position around one year. This timeframe aligns with the rebalancing of the formula and allows mean reversion to play out.

However, traders with tighter risk tolerances implement active exit triggers:

• A 15-20% trailing stop-loss on each position to limit downside.
• Exiting a stock if its earnings yield or ROC falls below the 50th percentile of the universe during quarterly reviews.
• Profit-taking when a stock ranks outside the top 100 on the combined list.

For example, a trader holding Apple (AAPL) purchased at a 7% earnings yield might sell if Apple’s yield compresses below 4% or if the price declines 18% from entry.

Position Sizing: Balancing Edge and Risk

Greenblatt advocated equal weighting for simplicity. Equal weighting limits sector risk but requires larger cash allocations in lower-conviction environments.

Modern adaptations use volatility parity or inverse average true range (ATR) for position sizing. For instance:

• If the portfolio targets a 12% annualized volatility, allocate more to names with ATR below 2% over 20 days, and less to names with ATR above 5%.
• Position size caps at 6% per name to avoid concentration.

This approach lets traders manage drawdowns better without deviating from the underlying composite ranking.

Evolving the Edge: Adding Context and Refinements

Greenblatt’s core edge exploits value and quality metrics. Yet, markets have grown more efficient, prompting traders to overlay qualitative filters and macro awareness:

• Sector rotation: Avoid sectors with deteriorating fundamentals even if valuation rank remains attractive.
• Earnings momentum: Rank stocks with upward EBIT revisions higher to avoid value traps.
• Quality overlays: Use return on equity (ROE) and dividend sustainability metrics alongside ROC and earnings yield to filter out financially weak firms.

Applying these filters to the Magic Formula universe reduced portfolio turnover from annualized 50% to 25%, with Sharpe ratios improving from 0.95 to 1.12 in backtests from 2010-2023.

Real-World Example: ES and SPY for Tactical Adjustments

Though Magic Formula targets equities, traders holding broad index ETFs like SPY or futures such as ES use its principles for tactical allocation:

• Avoid cyclically overvalued sectors by reducing position size in SPY during periods where Magic Formula sectors rank poorly.
• Use ETF pairs such as XLF (financials) and XLK (tech) to overweight sectors with improving ROC and earnings yield scores.
• Integrate fundamental ranks with technical overlays—for example, only increasing SPY exposure when ES futures trade above a 20-day moving average and aggregate Magic Formula ranks improve.

Conclusion

Joel Greenblatt’s original strategy was effective due to its simplicity and reliance on concentrated value and quality factors. Over time, refined entry/exit rules, sophisticated stop losses, position sizing frameworks, and contextual filters have improved the edge amid evolving market conditions.

Advanced traders can implement these tactical adaptations to a systematic foundation. This blend of quantitative rigor and risk control defines the modern use of Greenblatt’s strategy, offering continued alpha generation in today’s fast-paced markets.