The Allocators

1. The Allocators Investigating “Innovation Factors” for Growth February 27, 2006 Presented By: Ainsley Fuhr Mike Gabriel Nate Rozof Graig Saloom Greg Williamson

2. Agenda • Introduction • Objective • Methodology • Key Factors • Screening/Alpha-tests • Results • Quintiles • Heat Maps • Scoring Strategy • Closing Thoughts

3. Introduction Objective: • Investigate recent claims of a shift to “New Economy” drivers of growth: • The Innovation Boom, John Mouldin’s E-Letter, 2/20/06 • Why The Economy is Stronger than You Think, Businessweek, 2/13/06 • Determine whether “innovation” factors can identify excess returns • R&D Expenditures • Intangible Assets • Investments in Human Capital

4. Trends in R&D expenses relative to capital expenditures: They have grown much faster They were unaffected by recessions, mid-cycle slowdown or financial crises The rate of increase, in some cases, is accelerating The trends really diverged in the early 1990s (the beginning of the explosion in the trade deficit) They have led to strong productivity gains. Introduction “Today, less capital is being invested in the expansion of physical capacity and more capital is being invested in the expansion of intellectual capacity.” Source: “The Innovation Boom, John Mouldin’s E-Letter, 2/20/06

5. Traditional Drivers: Focus: Capital Spending Metrics: ROA Capital Expenditures Property, Plant and Equipment Introduction “Globalization, outsourcing, and the emphasis on innovation and creativity are forcing businesses to shift at a dramatic rate from tangible to intangible investments.” New Drivers: • Focus: “Knowledge Spending” • Metrics: • ROIA (return on intangible assets) • R&D expenditures • Investments in Human Capital Source: “Why The Economy is Stronger than You Think”, Businessweek, 2/13/2006

6. Introduction According to BusinessWeek, investment in intangibles such as product development and training is critical for long-term profitability, but is not counted in GDP. Our objective is to determine whether these factors have actually been driving significant asset returns *Billions of dollars; Average for 2000-2003 Data: Corado, Hulten, Siche

7. Introduction Methodology • Identify “Innovation” Factors • Generate Stock Screens • Alpha-test Screens • Develop Scoring System • Apply Scoring System In Sample • Apply Scoring System Out of Sample

8. Identified “Innovation” Factors We identified metrics to measure innovation factors highlighted in both reports

9. Screening/Alpha-tests Screen Parameters • Limit universe to S&P 500 securities • Rebalance portfolios monthly • In-sample period: 1996 – 2002 • Out-of-sample period: 2003 – 2005 Alpha-testing • Quintile analysis for 25 factors (traditional + “innovative”) • Monthly returns vs. Benchmark (S&P500)

10. Results: Quintiles Factor: ROIA (Innovation Factor) Inconsistent linear relationship – Factor Rejected

11. Results: Quintiles Factor: Sales/Advertising (Innovation Factor) Poor linear relationship – Factor Rejected

12. Results: Quintiles Factor: Sales Growth 5YR per Employee (Innovation Factor) Promising linear relationship, significant spread – Factor Accepted

13. Results: Quintiles Factor: R&D to Capex Lag 1YR (Innovation Factor) Mostly linear relationship, significant spread – Factor Accepted

14. Results: Quintiles Factor: ROA (Traditional Factor) Promising linear relationship, significant spread – Factor Accepted

15. Results: Heat Maps Factor: ROA Solid indicators in 4 out of 7 years for quintiles 1 and 5.

16. Results: Heat Maps Factor: Sales Growth 5YR per Employee Solid indicators in 5 out of 7 years for quintile 1, moderate indicator for quintile 5.

17. Results: Heat Maps Factor: R&D to CapEx Inconsistent indicator – Factor Rejected.

18. Results: Scoring Strategy Scoring System • Factor 1: ROA(1) = +5 • Factor 2: ROA(5) = -4 • Factor 3: SalesGrwth/Emp(1) = +4 • Factor 4: SalesGrwth/Emp(5) = -2 Alpha-testing • Quintile analysis for “Total Score” • Monthly returns vs. Benchmark (S&P500)

19. Results: Scoring Strategy In-Sample Total Return: 1996-2002 Solid linear relationship, significant spread – Model Accepted Significant quintile 1 alpha for moderate additional beta risk.

20. Results: Scoring Strategy Out-of-Sample Total Return: 2003-2005 Poor linear relationship indicates the model is not useful for long-short strategy. Alpha/beta relationship appears less attractive.

21. Results: Scoring Strategy Out-of-Sample Total Return: 2003-2005 Quintile 1 outperforms market in each year, but fails to outperform all other quintiles. All 5 quintiles beat market return each year. Therefore equal weight strategy likely skewing results.

22. Closing Thoughts “Innovation” factors are intriguing, but don’t seem to be a compelling driver of above-average returns • Equal weighted sorting strategy compared to value weighted benchmark (S&P 500) produces skewed results • Additional analysis by sector was more promising and deserves further investigation • Model more likely to explain information-based industries • Inclusion of traditional, capital intensive industries and financials clouding results • Long-only strategy in quintile 1 more promising than long-short strategy • Additional data sources of innovation factors, especially in areas of human capital necessary • We believe that new economic indicators such as “innovation” factors likely impact macro economic growth, but have less predictive power on an individual asset level