The Allocators
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Transcript of The Allocators
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The Allocators
Presented By:Ainsley FuhrMike GabrielNate RozofGraig SaloomGreg Williamson
February 27, 2006
Investigating “Innovation Factors” for Growth
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Agenda
• Introduction
– Objective
– Methodology
• Key Factors
• Screening/Alpha-tests
• Results
– Quintiles
– Heat Maps
– Scoring Strategy
• Closing Thoughts
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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
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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.”
“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.
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Introduction
“Globalization, outsourcing, and the emphasis on innovation and creativity are forcing businesses to shift at a dramatic
rate from tangible to intangible investments.”
“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
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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 2000-2003Data: Corado, Hulten, Siche
Our objective is to determine whether these factors have actually been driving significant asset returns
Investment by Asset Type (Billions of US Dollars)
46%54%
Unmeasured intangibles
Physical capital and software
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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
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Identified “Innovation” Factors
We identified metrics to measure innovation factors highlighted in both reports
Innovation Factor Metric AnalyzedInvestment in Information Assets R&D / (R&D + CapEx)
Return on Investment in Information Assets
Return on Intangible Assets (ROIA)
Productivity 5 Year Sales Growth / # of Employees
Effectiveness of Investment in Information Assets
Sales / Advertising Expense
Investments in Human Capital Metric Unidentifiable
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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)
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Results: Quintiles
Factor: ROIA (Innovation Factor)
Inconsistent linear relationship – Factor Rejected
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Results: Quintiles
Factor: Sales/Advertising (Innovation Factor)
Poor linear relationship – Factor Rejected
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Results: Quintiles
Factor: Sales Growth 5YR per Employee (Innovation Factor)
Promising linear relationship, significant spread – Factor Accepted
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Results: Quintiles
Factor: R&D to Capex Lag 1YR (Innovation Factor)
Mostly linear relationship, significant spread – Factor Accepted
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Results: Quintiles
Factor: ROA (Traditional Factor)
Promising linear relationship, significant spread – Factor Accepted
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Results: Heat Maps
Factor: ROA
Solid indicators in 4 out of 7 years for quintiles 1 and 5.
Annual Returns 1 2 3 4 5 Market1996 51.24% 29.15% 23.70% 32.31% 17.48% 26.27%1997 41.74% 30.47% 32.63% 40.00% 27.19% 26.90%1998 55.39% 22.78% 24.41% 18.55% 19.57% 32.49%1999 59.98% 8.27% 3.29% 6.76% 9.61% 10.35%2000 29.08% 30.97% 34.94% 40.43% 30.10% -0.90%2001 -0.30% 8.04% 3.02% 4.37% -2.82% -16.14%2002 -11.99% -6.91% -12.90% -13.15% -30.47% -23.01%
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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.
Annual Returns 1 2 3 4 5 Market1996 43.56% 29.20% 22.00% 22.30% 22.47% 26.27%1997 40.26% 31.50% 28.69% 33.57% 27.32% 26.90%1998 33.14% 30.16% 17.38% 12.98% 18.02% 32.49%1999 50.89% 16.48% -0.36% 3.23% 1.01% 10.35%2000 58.47% 39.28% 31.05% 22.88% 15.66% -0.90%2001 -1.78% 2.17% 4.24% 3.33% 5.64% -16.14%2002 -16.48% -16.19% -12.55% -14.30% -18.27% -23.01%
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Results: Heat Maps
Factor: R&D to CapEx
Inconsistent indicator – Factor Rejected.
Annual Returns 1 2 3 4 5 Market1996 27.80% 24.97% 27.66% 31.93% 26.26% 26.27%1997 55.18% 30.09% 25.57% 34.56% 45.30% 26.90%1998 92.54% 34.32% 22.29% 13.55% 31.23% 32.49%1999 92.23% 60.58% 44.43% 18.82% 4.40% 10.35%2000 -2.08% 25.45% -4.07% 11.15% 19.18% -0.90%2001 4.45% -3.07% 7.76% 3.87% 20.36% -16.14%2002 -29.69% -21.90% -12.37% -8.85% -7.14% -23.01%
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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)
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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.
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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.
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Results: Scoring Strategy
Out-of-Sample Total Return: 2003-2005
Quintile 1 outperforms market in each year, but fails to outperform all other quintiles.
Annual Returns 1 2 3 4 5 Market2003 49.60% 55.35% 41.47% 51.29% 71.41% 34.55%2004 13.71% 17.00% 12.80% 17.30% 7.98% 6.22%2005 22.90% 30.60% 13.06% 17.53% 12.19% 10.38%
All 5 quintiles beat market return each year. Therefore equal weight strategy likely skewing results.
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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