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

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 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 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.

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

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. Unmeasured intangibles $977* Physical capital and software $1,139 *Billions of dollars; Average for Data: Corado, Hulten, Siche Our objective is to determine whether these factors have actually been driving significant asset returns

7 Introduction Methodology 1.Identify “Innovation” Factors 2.Generate Stock Screens 3.Alpha-test Screens 4.Develop Scoring System 5.Apply Scoring System In Sample 6.Apply Scoring System Out of Sample

8 Identified “Innovation” Factors We identified metrics to measure innovation factors highlighted in both reports Innovation FactorMetric Analyzed Investment in Information AssetsR&D / (R&D + CapEx) Return on Investment in Information Assets Return on Intangible Assets (ROIA) Productivity5 Year Sales Growth / # of Employees Effectiveness of Investment in Information Assets Sales / Advertising Expense Investments in Human CapitalMetric Unidentifiable

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: 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: 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: 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 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 “Innovation” factors are intriguing, but don’t seem to be a compelling driver of above-average returns