1. Long/Short Trading Strategy Cam’s Crazies Global Asset Allocation February 2005

2. Agenda Methodology Factors UniVariate Models  Book to Price  Dividend Yield  FY2 Earnings Yield to Growth BiVariate Model Scoring Model Why Results Might Be Wrong Follow-up Research Conclusion

3. Methodology Objective: To develop a quantitative long/short model that generates positive and consistent returns with no market exposure (beta zero). Universe: For the sake of liquidity and availability of historical data, we limited our screening universe to the 500 largest market capitalization companies listed in the U.S. Rebalancing: To limit turnover and transaction expenses, we resort and rebalance annually. Weights: For simplicity and prevention of outlier performance, we chose an equal weight strategy, both in the number of stocks ‘per bucket’ and in the amount invested or shorted per stock.

4. Factors  Book to Price  Earnings Yield (FY1/FY2)  FY1/FY2 Earnings Yield to Growth  Earnings Growth  Dividend Yield  3 Year EPS Growth

5. Factors: Book to Price Go long stocks with high B2P ratios (low Price to Book) and short stocks with low B2P ratios. Average annual return of 9% and.53 Sharpe ratio. High volatility of returns (+48% and -23%). The screen did not perform well during the 1998-1999 valuation bubble, producing -21% and -11% returns, respectively.

6. Factors: Dividend Yield Go long high dividend yield stocks short low/no dividend yield stocks. Some sector weight and value vs. growth concerns. Best screen: 16.7% beta-neutral average returns, 1.11 sharpe ratio, lowest turnover of all factors. Alphas for fractiles 1 and 5 11.6% and -7.7%, respectively. Most consistent results: fractile 1 was top performer all but 2 years. Bottom fractile nearly always 4 or 5. Consecutive losses in 1998 and 1999, but positive returns in all other years.

7. Factors: FY2 Earnings Yield to Growth Forward looking: uses fiscal earnings estimates for two years out scaled by consensus long term growth expectations. Go long high EY to growth fractile, short low EY to growth fractile. A beta neutral long/short strategy resulted in an average return of 10% with a range of -11% to +37%. High Sharpe ratio of.78. There were four years of negative returns over the 20-year time period. Consecutive losses in 1998 and 1999.

8. BiVariate Model Factor 1 Dividend Yield – fundamental data Factor 2 FY2 Earnings Yield to Growth – expectational data Two sorts produced 25 fractiles with ~20 companies each – long top fractile, short bottom Trouble sorting in later years damages credibility of data. Average beta-weighted return of 29% and sharpe ratio of 1.58 are exceptionally high. Returns in bubble years range from 32%-58%. 2004 only year of negative returns (-8%).

9. Scoring Model Selected Book to Price, Dividend Yield, Earnings Yield (FY2) and Earnings Yield (FY2) to growth. These factors combine both fundamental and expectational variables. We subjectively chose scores, ranging from -5 to 5, for fractiles 1 and 5. Highest possible Score is 12, lowest possible score is -9. Beta-neutral average returns of 12%, sharpe ratio of.81; not as good as stand-alone dividend yield.

10. Why Might Results Be Wrong? Model depends on FactSet – both the accuracy of the data and Alpha Tester program. Data could be subject to:  errors  lags  survivorship bias  outliers  high minus low  sector bets

11. Follow-Up Research Detailed review of the accuracy of the FactSet historical data and the Alpha Tester model.  Suspicious beta screen Model Specific:  more frequent rebalancing  a larger universe of companies/ international markets  a more exact estimation of the impact of turnover, management fees, and short sale restrictions  optimization of weights in Scoring model  ex-ante application of the model on a real time basis

12. Beta as a Factor? Persistent Characteristic: Long low beta portfolio, Short high beta portfolio. Decided to run screen based on low-to-high Beta Fractiles. Screen generated about 8.0% of Alpha although heat map suggests inconsistency.

13. Conclusion Results were very impressive Further research is needed to gain confidence in models How long will excess returns persist before identified and competed away? That said, we can’t help but wonder … What if we’re right??