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- 1 - © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform.

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Presentation on theme: "- 1 - © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform."— Presentation transcript:

1 - 1 - © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform AGENDA History, Application, and Examples of Value Charts Including Analysts EPS to Produce Forecasted Valuations Tracking Errors as Measures of Model Accuracy Traditional Multi-Period and Capitalization DCF Valuations The Cash Economic Return (CER) Fade Concept – Regression toward the Mean –Reflects empirical basis for competitive reaction and its likely impact on future cash flows of the firm Option Pricing Functions to Describe Fade Capitalization DCF Valuations Value Charts and Summaries of Tracking Errors to Measure the Accuracy of Multiple Models Back Tests on Predictive Capability of Model as Price Migrates toward Intrinsic Value over several Quarters –Consistent with contrarian strategies related to behavior finance psychological herd tendencies Stable Paretian versus Gaussian Normal Distributions of Price Change and % Under (Over) Valuation –Application of alpha peakedness parameter of the Stable Paretian Distribution as a risk measure to assure proper diversification Provide the author your address to receive a link to the LCRT web site for this presentation and other material or

2 - 2 - © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform PRESENTATION CONCLUSIONS Suggests two empirical research measurement methodologies to improve DCF models –Value Charts with tracking errors for individual companies (based on capitalization methods using only historical information with minimal analyst intervention) –Cumulative Tracking errors for large sample of companies Fading Cash Economic Returns provides a conceptual and empirical basis for dealing effectively with competitive reaction and its likely impact on the future cash flows of the firm Back tests suggest excess investment returns result from prices migrating toward intrinsic values over several quarters –More accurate models are more predictive The Stable Paretian Alpha Peakedness parameter provides one replacement risk measure for traditional mean variance CAPM beta, as it identifies regions of the universe where the tails of the distribution become so fat that the mean becomes indeterminate

3 - 3 - © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform HISTORY OF VALUE CHARTS Value Line began employing Value Charts in the 1930s to display its capitalization of cash flow (income + depreciation) as their valuation model In 1984, the author suggested Callard employ this visual technique to show CMA valuation model results Subsequently, CMA Offshoots - HOLT Planning, HOLT Value, The Boston Consulting Group, Applied Financial Group, CSFB HOLT, Ativo, Lafferty, and LCRT illustrated their models with Value Charts In 2001, the author began illustrating results of multiple models with Value Charts White Bars depict high / low trading range of fiscal year prices Small hollow circle represent closing price at Fiscal Year + 3 Months Red line connects single period estimates produced by the valuation model each year Takeaway … The Value Chart represents a powerful research tool for illustrating the historical tracking of valuation models against actual price data. Robert Shiller (1981) compares prices for the market to an intrinsic value derived from a dividend discount model. He observes that prices are much more volatile than the intrinsic values, as we discern above for this individual firm.

4 - 4 - © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform INCLUDING ANALYSTS EPS ESTIMATES EXTENDS THE VALUE LINE INTO THE FUTURE Assuming constant non-earnings margin and capital turnover extends the Value Line into the Future Decrease in EPS for current 2005 before rebounding in 2006 translates to a decline in intrinsic value in 2005 Takeaway … History provides a Baseline to judge a Valuation Model, before extending its results into the future. More accurate models help pick under valued stocks for investment. Thanks to Tom Copeland for suggesting that this methodology effectively separates the migration of price toward intrinsic value based purely on history from the migration of price toward analysts forecasts.

5 - 5 - © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform THE LCRT RESEARCH MODEL TRACKS BIOTECH START-UPS WHEN NO OTHER MODELS CALCULATE A SENSIBLE VALUE Takeaway … Start-Ups represent one class of firms where traditional models require a multi- year forecast, but option pricing suggests an alternative approach, illustrated later.

6 - 6 - © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform DATA FROM VALUE CHARTS PROVIDE TRACKING ERRORS TO MEASURE GOODNESS OF FIT OF THE MODEL TO ACTUAL PRICES Takeaway … Tracking Errors provide a quantitative way to compare the accuracy of several models and the accuracy of a model applied to one firms common stock.

7 - 7 - © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform TRADITIONAL DCF RESIDES AT THE VERY HEART OF VALUATION Different analysts using DCF can honestly arrive at divergent company values using the same set of information Most appraisers and analysts employ a multi-period model Analysts employ a Capitalization Method as the terminal value when the company reaches stability in its growth of revenues, earnings, and cash flow at a consistent rate (Gordon Growth Model represents one single state DCF) Theoretically, both capitalization and multi- period models should return the same value, but frequently do not Net Free cash flow contains well publicized faults – greatest risk is reliance on subjective analyst input on 20 or more assumptions (sales growth, margins, capital turns, capital structure, etc.) Author suggests a baseline model, formed from Value Charts as one empirical way to evaluate DCF output for reasonableness A baseline value model uses historical financial information to determine a companys value with minimal analyst intervention Net Income 204,104 + Depreciation +22,772 + Working Capital Decreases +51,587 - Capital Expenditures -34,809 = Net Free Cash Flow 243,654 Takeaway … Very wide acceptance of DCF by practitioners may have produced complacency in modeling applications, failing to ask how empirical research may test to improve the model.

8 - 8 - © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform COMPARISON OF TRADITIONAL VALUATION TO OFFSHOOTS OF CALLARD, MADDEN (CMA) (1) Selecting and applying public information for private company and business unit valuation represents accepted practice Traditional appraisal valuations usually employ industry as the primary screen for comparables In contrast, Offshoots of CMA choose companies based on economics alone –Cash Flow Return on Investment (CFROI ® ) or Cash Economic Return (CER) –Sustainable Growth Rate –Size –Leverage –Asset Life and Age –Inflation Effects –Asset Mix between depreciating and non-depreciation assets The CFROI and CER build on the work of Solomon, Salaman, Ijiri, and Madden to create an annual economic return measure for the whole company (explained later) –Eliminates cash, accounting, and inflation distortions to traditional measures on depreciated book assets –Reflects the cash investment into the companys operations from the investors point of view, adjusted for units of common purchasing power –Equals the real internal rate of return of all the projects in place CFROI ® is a registered Trademark of CSFB HOLT

9 - 9 - © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform COMPARISON OF TRADITIONAL VALUATION TO OFFSHOOTS OF CALLARD, MADDEN (CMA) (2) Offshoots of CMA employ a capitalization model produced from company economic returns for only a single period instead of using several future periods, as traditionally done in multi period models –Substitute fade in place of discrete forecast periods to obtain normalized structure and cash flow over time –Of great research significance, employing a single period model enables extensive empirical testing of several models applied to thousands of companies over a decade –Fade represents the single most important tool that permits the analyst to utilize a single period model rather than a multi period forecasting model –As a mathematical measure of competitive regression toward the mean, fade adjusts abnormal economic returns, positive or negative, to a normalized return over time

10 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform ADVANCED LCRT RESEARCH: REPRESENTATIVE CASH ECONOMIC RETURN FADE PATTERNS Takeaway … Fade based on proprietary uniform empirical adjustments to reflect market expectations so 50% of firms are under valued and 50% are over valued in every region of the universe.

11 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform NUMERIC EXAMPLE ILLUSTRATES THE FADE CONCEPT APPLIED TO ASSET GROWTH RATES In 2004, the company employs constant dollar gross investment of $21,779 Million Its sustainable growth rate is 5.67% Fading the 5.67% growth rate at an 80% rate toward the 3.0% economic growth rate produces a 3.54% growth rate 3.54 = 0.8 * (5.67 – 3.00) Applying the 3.54% to 21,770 investment produces a $22, investment Constant FutureDollar GrowthGross YearRateInvestment , ,549 Takeaway … The fade pattern represents market expected growth rates from sustainable growth. It also represents the single most important procedure to explain how a capitalized intrinsic value model can replace an analyst multi-period model.

12 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform NUMERIC EXAMPLE ILLUSTRATES THE FADE CONCEPT APPLIED TO CASH ECONOMIC RETURN (CER) The company achieves a 20.17% Cash Economic Return in 2004 Fading the 20.17% CER at a 50% rate to an empirically derived 16.56% fade-to produces a 16.56% CER in = 0.5 * (20.17 – 12.57) Applying the 16.56% to the 22, investment produces 5,977 in gross cash flow (net income + depreciation) Constant Dollar Gross Cash Investment Increases 770Constant DollarCashDollar GrossEconomicGross CashReturnCash YearInvestment(CER)Flow , , , ,977 Increase 770 Takeaway … The fade pattern represents market expected Cash Economic Returns from competitive pressures. It also represents the single most important procedure to explain how a capitalized intrinsic value model can replace an analyst multi-period model.

13 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform CMA OFFSHOOTS EMPLOY DIFFERENT DRIVERS TO PRODUCE VALUATION Instead of traditional Sales growth rates, margins and capital turns as drivers, CMA Offshoots employ fading growth rates and CER to produce net free cash flows Subtracting replacement and growth investments form $3,134 in net constant dollar cash flows Gross Cash Flows+5,977 Replacement Investments-1,973 Growth Investments- 770 Constant Dollar Net Free Cash Flow+3,134 Takeaway … CMA Offshoots ultimately produce Net Free Cash Flow, but unlike traditional DCF models it is constant dollar and derived from CFROI or CER and gross asset growth rates as value drivers instead of the traditional sales growth rates, margins, and capital turns.

14 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform INTRINSIC VALUES PER SHARE RESULT FROM TRADITIONAL CALCULATIONS Present Value of constant dollar net cash flows forms the 80,516 enterprise value Adding non-operating cash, subtracting debt and dividing by 2,911 shares outstanding produces the spot intrinsic value per share Present Value of Cash Flows+80,516 Cash Less Debt+ 3,687 Equity Intrinsic Value+84,203 Number of Shares Outstanding 2,911 Equity Intrinsic Value Per Share 28.93

15 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform CMA OFFSHOOTS EMPLOY CFROI ® OR CER AND GROSS ASSET GROWTH RATES AS PRIMARY VALUE DRIVERS The top panel compares CER to the discount rate for HPQ The second panel compares gross asset growth rates to sustainable growth rates

16 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform Income$206 A: Eliminate Non-OperatingSpecial Extraordinary Items After Tax33 Items(-) Non-operating Expense After-Tax(16) B: Translate to CashNon-Cash Charges333 C: Restate for InflationInflation Gain on Non-Fixed Assets14 D: Eliminate LeverageAfter-Tax Interest (Debt and Operating Leases)134$781 $206Rentals – Principal Payments77Current Dollar IncomeE: Capitalize Expenses(-) Advertising and R & D After Tax(0)Gross Cash Flow AssetsTotal Assets$5,825Current Dollar $5,825A: Eliminate Non-Operating(-) Non-Operating Assets(137)Investor Gross Items(-) Purchase Goodwill(1,531)Cash Receivables Reserve23Investment B: Translate to Cash Invest.LIFO Reserve141$5,704 Accumulated Depreciation1,580 C: Restate for Inflation Inflation Adjustments to Land, Gross Plant and Deferred Taxes249 D: Eliminate LeverageGross Leased Property from Operating Leases1,202 E: Capitalize ExpensesCapitalized Advertising, R & D0 F: Capital Owner Cash Invest. (-) Operating Non-Interest Bearing Liabilities(1,648) CASH ECONOMIC RETURN EXAMPLE: ACCOUNTING TO CASH SUPERVALU– 2001 ($Millions)

17 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform CASH ECONOMIC RETURN EXAMPLE: CASH TO ECONOMICS SUPERVALU– 2001 ($ MILLIONS) Current Dollar Gross Cash Flow $781 Non-Depreciating Asset Release $727 ($5,704) Current Dollar Investor Gross Cash Investment Economic Life: Years Cash Economic Return - IRR: 9.09% YearsIRR

18 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform CASH ECONOMIC RETURN REFLECTS THE AVERAGE INTERNAL RATE OF RETURN OF ALL THE PROJECTS IN PLACE Cash Economic Return Existing Projects Operating Net Income + Depreciation - Inflation Adjustments Working Capital + Land Net Operating Assets + Accumulated Depreciation + Inflation Adjustment

19 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform ADVANCED LCRT RESEARCH: CASH ECONOMIC RETURN FADE TOS RELY ON SMALL FIRM PUT AND MEDIUM SIZE STRADDLE FUNCTIONS Smallest Start-Up Firms Smallest Start-Up Firms Largest and Smallest Firms

20 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform ADVANCED LCRT RESEARCH: CASH ECONOMIC RETURN FADE RATES RELY ON PUT FUNCTIONS

21 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform LCRT ADVANCED RESEARCH: LCRT PLACES LEVERAGE RELATED RISK IN THE CASH FLOWS INSTEAD OF THE DISCOUNT RATE IN ORDER TO EMPLOY A UNIFORM DISCOUNT RATE FOR ALL FIRMS IN THE SUPER SECTOR EACH YEAR Deadweight Financial Distress Costs of Higher Leverage [0,1] Function of Equity Put for ANY Debt Call Functions

22 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform PRESENTATION WOULD NOT BE COMPLETE WITHOUT COMPARING THREE MODELS Net Free Cash Flow based on specifications by Dan Van Vleet (while at Willamette) –Growing net free cash flows for T years –Net Free Cash Flow = income after taxes + depreciation & amortization – non- operating items after tax – normalized capital expenditures – working capital additions –Terminal years cash flow capitalized by median industry CAPM nominal discount rate less nominal growth rate LCRT Model (18.0%) 8 X EBITDA (30.7%) Net Free Cash Flow (37.4%) (Absolute Tracking Error) Takeaways … A single company by no means represents a sufficient sample for empirical testing, but remains useful for portfolio investment decisions. Comparisons represent an objective empirical research process for testing models and improving DCF valuations for individual firms.

23 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform A CUMULATIVE TRACKING ERROR CHART SUMMARIZES 5,500 FIRMS FOR ABOUT 30,000 COMPANY-YEARS Median Absolute Tracking Errors Net Free Cash Flow 166% 8 X EBITDA 86% LCRT Model 51% Results may help to explain why security analysts and portfolio managers prefer simple multiples over DCF net free cash flow valuation models More accurate models may be more predictive Cumulative % of Universe LOG 2 of % Absolute Model Tracking Error versus Actual Price – Fiscal Year +3 Months to reflect Disclosure Lag ,500 Industrials LCRT Model 8 X EBITDA Net Free Cash Flow Takeaways … Comparisons represent an objective empirical research process for testing models and improving DCF valuations for large samples of firms. More accurate models are up and to the left. Less accurate models are down and to the right.

24 © 2006 LifeCycle Returns, Inc. All Rights Reserved LCRT BACKTESTS Annual Quantile Quarterly

25 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform THE LCRT RESEARCH DCF MODEL SEPARATES WINNERS AND LOSERS CONSISTENTLY THROUGH MOST YEARS Source: Industrial Firms , % Debt to Debt Capacity < 62%; Hemscott Data, LCRT Platform Calculations Annual Rebalancing Purchase at Fiscal Year + 3 Months Sale at Fiscal Year + 15 Months No Transaction or Price Pressure Costs Included Equal Weighted Past performance of a back test is no guarantee of future performance.

26 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform LCRTS RESEARCH DCF MODEL SEPARATES THE UNIVERSE INTO WINNERS & LOSERS Source: Industrial Firms , % Debt to Debt Capacity < 62%; Hemscott Data, LCRT Platform Calculations No Transaction or Price Pressure Costs Included Equal Weighted

27 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform THE LCRT DCF RESEARCH MODEL SEPARATES WINNERS AND LOSERS CONSISTENTLY THROUGH QUARTERS FROM ANNUAL DATA Source: Industrial Firms , % Debt to Debt Capacity < 62%; Hemscott Data, LCRT Platform Calculations No Transaction or Price Pressure Costs Included Equal Weighted

28 © 2006 LifeCycle Returns, Inc. All Rights Reserved Risk Metrics in Portfolio Construction Implications of Intrinsic Valuation Research By Rawley Thomas President LifeCycle Returns, Inc. January 6, 2006

29 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform INTRODUCTIONINTRODUCTION Our research into intrinsic equity valuations reveals the existence of fat tailed distributions in % under/over valuations and therefore suggests that the use of traditional risk measures may need to be reassessed Based on this empirical evidence, portfolio managers may wish to reconsider the use of CAPM Beta as a primary risk metric The research suggests a possible replacement risk measure, displayed in the empirical research contained in the next slides

30 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform TWO TRADITIONAL PORTFOLIO CONSTRUCTION AND DIVERSIFICATION APPROACHES (PORTFOLIO RISK BELIEFS) Sector Neutral –Pick stocks so each sector is represented proportional to its market cap –May overweight or underweight within constraints Mean Variance (Markowitz) –Pick stocks to target an average CAPM Beta for the portfolio Takeaway … Are these approaches to portfolio risk adequate and appropriate when faced with fat tailed distributions?

31 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform ADVANCED PORTFOLIO CONSTRUCTION AND DIVERSIFICATION Our observations are based on combining the Stable Paretian fat tailed distribution insights from Benoit Mandelbrot and J. Huston McCulloch with our research on the distributions of under/over valuation –Benoit Mandelbrot, The Variation of Certain Speculative Prices, in Paul Cootner, The Random Character of Stock Market Prices, MIT Press, 1964, pp –Benoit Mandelbrot and Richard L. Hudson, The (Mis)Behavior of Markets: A Fractal View of Risk, Ruin, and Reward, Basic Books, –J. Huston McCulloch, Simple Consistent Estimators of Stable Distribution Parameters, Commun. Statist. – Simula., 15(4), 1986, pp (Programmed with the help of Paul Kettler and Terry Heiland) –A literature search will produce articles and books by other authors in the field – Frank Fabozzi, Aleksander Janiski, Hartmut Jurgens, Christian Menn, Edward Ott, Heinz-Otto Peitgen, Edgar Peters, Svetlozar Rachev, Gennady Samorodnitsky, Dietmar Saupe, Tim Sauer, Jacky So, Dietrich Stoyan, Helga Stoyan, Murad Taqqu, Aleksander Weron, and James Yorke

32 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform STABLE PARETIAN DISTRIBUTION PROPERTIES (1) The Gaussian Normal Distribution (the Bell Shaped Curve) is a special case of Stable Paretian where the alpha peakedness parameter = 2.00 The variance of distributions with alpha peakedness parameters < 2.00 is infinite Most all value-performance data we analyzed showed fat tailed distributions with alpha peakedness parameters significantly less than 2.00 with infinite variances Therefore, risk measures relying on variance, covariance, and standard deviation are indeterminate –This includes CAPM Beta Consequently, portfolio managers should consider replacement measures of portfolio risk and diversification

33 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform A FAT TAILED STABLE PARETIAN DISTRIBUTION DISPLAYS A BETTER VISUAL FIT TO TOTAL SHAREHOLDER RETURN DATA THAN DOES GAUSSIAN NORMAL Sources: Industrial Firms , Total Shareholder Return (TSR) from FY+3 to +15 Months Relative to S&P 500, Hemscott Data, LCRT Platform Calculations, J. Huston McCulloch, Simple Consistent Estimators of Stable Distribution Parameters, Commun. Statist. – Simula., 15(4), 1986, pp Takeaway … This suggests potential for the use of non-traditional measures of risk based on fat tailed Stable instead of Gaussian distributions

34 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform THE 1.39 ALPHA PEAKEDNESS STATISTICAL RESULTS CONFIRM THE TSR DISTRIBUTION IS 41.4 STANDARD ERRORS AWAY FROM GAUSSIAN NORMAL (Where Alpha Peakedness = 2.00) ResultsValueStd. Errort-Statistic alpha ("peakedness") Difference from 2.00 beta ("skewness") Difference from 0.00 c ("dispersion") ,205.23Difference from 0.00 delta ("location" or "average") Difference from 0.00 Sources: 5,500 Industrial Firms , Total Shareholder Return (TSR) from FY+3 to +15 Months Relative to S&P 500, Hemscott Data, LCRT Platform Calculations, J. Huston McCulloch, Simple Consistent Estimators of Stable Distribution Parameters, Commun. Statist. – Simula., 15(4), 1986, pp Takeaway … This suggests limitations in the appropriate use of CAPM Beta as a risk measure, since CAPM Beta relies on the existence of the indeterminate covariance statistic

35 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform A FAT TAILED STABLE PARETIAN DISTRIBUTION DISPLAYS A BETTER VISUAL FIT TO LN OF TOTAL SHAREHOLDER RETURN DATA THAN 2.00 FOR GAUSSIAN NORMAL Sources: 5,500 Industrial Firms , Total Shareholder Return (TSR) from FY+3 to +15 Months Relative to S&P 500, Hemscott Data, LCRT Platform Calculations, J. Huston McCulloch, Simple Consistent Estimators of Stable Distribution Parameters, Commun. Statist. – Simula., 15(4), 1986, pp Takeaway … This suggests the LN transform or assuming a log normal distribution is inadequate to fix the fit problem.

36 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform THE 1.48 ALPHA PEAKEDNESS STATISTICAL RESULTS CONFIRM THE LN OF TSR DISTRIBUTION IS 43.4 STANDARD ERRORS AWAY FROM 2.00 FOR GAUSSIAN NORMAL Sources: 5,500 Industrial Firms , Total Shareholder Return (TSR) from FY+3 to +15 Months Relative to S&P 500, Hemscott Data, LCRT Platform Calculations, J. Huston McCulloch, Simple Consistent Estimators of Stable Distribution Parameters, Commun. Statist. – Simula., 15(4), 1986, pp ResultsValueStd. Errort-Statistic alpha ("peakedness") Difference from 2.00 beta ("skewness") Difference from 0.00 c ("dispersion") Difference from 0.00 delta ("location" or "average") Difference from 0.00 Takeaway … again suggesting the limitations in the use of CAPM Beta as a risk measure

37 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform THE % UNDER/OVER VALUATION OF OUR DCF RESEARCH MODEL ALSO DISPLAYS STABLE PARETIAN DISTRIBUTION CHARACTERISTICS The 1.33 alpha peakedness parameter is 36.9 standard errors away from the 2.00 value for a Gaussian Normal distribution The distribution displayed covers industrial firms with % debt to debt capacity (PV cash flows from existing assets) < 75% Sources: 5,500 Industrial Firms , Total Shareholder Return (TSR) from FY+3 to +15 Months Relative to S&P 500, Hemscott Data, LCRT Platform Calculations, J. Huston McCulloch, Simple Consistent Estimators of Stable Distribution Parameters, Commun. Statist. – Simula., 15(4), 1986, pp ResultsValue Std. Errort-Statistic alpha ("peakedness") Difference from 2.00 beta ("skewness") Difference from 0.00 c ("dispersion") ,264.60Difference from 0.00 delta ("location" or "average") Difference from 0.00 Takeaway … you should consider employing different risk measures if you are using over/under intrinsic value as an investment decision tool

38 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform STABLE PARETIAN DISTRIBUTION PROPERTIES (2) For alpha peakedness parameters < 2.00, the variance is infinite As the alpha peakedness parameter approaches 1.00 (A Cauchy Distribution, pronounced Kōō – Shēē), the mean becomes infinite Consequently, we have no confidence in calculating the mean as the alpha peakedness parameter approaches 1.00 We hypothesize that distributions with tails so fat that the mean becomes indeterminate are very risky, where effective diversification becomes impossible The Stable Paretian alpha peakedness parameter may become a replacement measure for portfolio risk and effective diversification to replace traditional measures –A new measure of portfolio risk is also necessary to replace traditional CAPM cost of capital estimates as our research model places all the risk in the certainty equivalent cash flows and therefore employs a single real discount rate for the entire super sector each year

39 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform FOR HIGH DEBT FIRMS, THE DISTRIBUTION BECOMES CLOSE TO CAUCHY, WHERE THE MEAN BECOMES INDETERMINATE AND DIVERSIFICATION BECOMES PROBLEMATIC – INVEST IN THE DEBT OR THE EQUITY(?) The distribution displayed covers industrial firms with % debt to debt capacity (PV cash flows from existing assets) > 75% The 1.07 alpha peakedness parameter is only 1.91 standard errors away from the 1.00 value for a Cauchy distribution with infinite mean Sources: From 5,500 Industrial Firms , Total Shareholder Return (TSR) from FY+3 to +15 Months Relative to S&P 500, Hemscott Data, LCRT Platform Calculations, J. Huston McCulloch, Simple Consistent Estimators of Stable Distribution Parameters, Commun. Statist. – Simula., 15(4), 1986, pp ResultsValue Std. Errort-Statistic alpha ("peakedness") Difference from 1.00 beta ("skewness") Difference from 0.00 c ("dispersion") ,827.43Difference from 0.00 delta ("location" or "average")538.21#N/A Difference from 0.00 To assure calculation in all regions of the universe, the % under (over) valuation statistic is normalized by the stock price, which, unlike the intrinsic value, is always greater than zero. % under (over) valuation = 100% * (intrinsic value – price) / price. Regions < -100% probably represent firms where debt trades at a discount from par.

40 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform THE TOP QUINTILE (20%) OF UNDER VALUED FIRMS SHOW A 34.3% MEAN RELATIVE SHAREHOLDER RETURN AND A DETERMINATE 1.38 ALPHA PEAKEDNESS The distribution displayed covers industrial firms with % debt to debt capacity (PV cash flows from existing assets) < 75% The 1.38 alpha peakedness parameter is 8.73 standard errors away from the 1.00 value for a Cauchy distribution Sources: From 5,500 Industrial Firms , Total Shareholder Return (TSR) from FY+3 to +15 Months Relative to S&P 500, Hemscott Data, LCRT Platform Calculations, J. Huston McCulloch, Simple Consistent Estimators of Stable Distribution Parameters, Commun. Statist. – Simula., 15(4), 1986, pp Mean = 34.3 ResultsValue Std. Errort-Statistic alpha ("peakedness") Difference from 1.00 beta ("skewness") Difference from 0.00 c ("dispersion") ,548.21Difference from 0.00 delta ("location" or "average") Difference from 0.00 Takeaway… This suggests that in this area of the universe, diversification can be used to achieve mean performance

41 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform THE TOP 5% OF UNDER VALUED SMALL FIRMS SHOW A 61.8% MEAN RELATIVE SHAREHOLDER RETURN BUT AN INDETERMINATE 1.20 ALPHA PEAKEDNESS, NOT SIGNIFICANTLY DIFFERENT FROM CAUCHY 1.00 The risk of one or more torpedo stocks is too great compared to large gains of a few stocks Sources: 529 Small Industrial Firms , C$GI < 100, Total Shareholder Return (TSR) from FY+3 to +15 Months Relative to S&P 500, Hemscott Data, LCRT Platform Calculations, J. Huston McCulloch, Simple Consistent Estimators of Stable Distribution Parameters, Commun. Statist. – Simula., 15(4), 1986, pp Mean = 61.8 ResultsValue Std. Errort-Statistic alpha ("peakedness") Difference from 1.00 beta ("skewness") Difference from 0.00 c ("dispersion") Difference from 0.00 delta ("location" or "average")131.59#N/A Difference from 0.00 Takeaway … suggesting that in this area of the universe, diversification cant be used to achieve mean performance Traditional dispersion risk measures of standard deviation and CAPM Beta dont pick up this effect

42 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform THE CASH ECONOMIC RETURN FUNDAMENTAL DRIVER OF THESE DCF INTRINSIC VALUATIONS ALSO FOLLOWS A STABLE PARETIAN DISTRIBUTION WITH TAILS FATTER THAN CAUCHY OF 1.00 ALPHA PEAKEDNESS PARAMETER Sources: 5,500 Industrial Firms , Total Shareholder Return (TSR) from FY+3 to +15 Months Relative to S&P 500, Hemscott Data, LCRT Platform Calculations, J. Huston McCulloch, Simple Consistent Estimators of Stable Distribution Parameters, Commun. Statist. – Simula., 15(4), 1986, pp ResultsValue Std. Errort-Statistic alpha ("peakedness") Difference from 1.00 beta ("skewness") Difference from 0.00 c ("dispersion") Difference from 0.00 delta ("location" or "average")18.58#N/A Difference from 0.00 The LCRT approximation procedure divides the Stable Paretian intervals into 128 pieces (limited by Excels 256 columns), which is not sufficient enough to model the tails accurately for distributions fatter than Cauchy. Takeaway …A lot of risk exists in estimating future changes in the Cash Economic Return of selected stocks.

43 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform CONCLUSIONSCONCLUSIONS Our research into intrinsic valuation reveals the existence of fat tailed distributions in % under/over valuations and therefore suggests that traditional measures of risk may need re-evaluation Based on this empirical evidence, portfolio managers may wish to reconsider the use of CAPM Beta as a primary risk measure The research suggests the alpha peakedness parameter of the Stable Paretian distribution as a valid replacement risk measure –Assures effective portfolio diversification with fat tailed distributions –Our valuation platform includes the data necessary to measure this form of risk and % under/over valuation

44 © 2006 LifeCycle Returns, Inc. All Rights Reserved LCRT BACKTESTS ON FIRMS ABOVE $5 PER SHARE By Rawley Thomas President of LifeCycle Returns (LCRT) January 31, 2006

45 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform INTRODUCTIONINTRODUCTION A sophisticated portfolio manager client asked LCRT to extend our back tests to include only companies with stock prices greater than $5 per share at Fiscal Year + 3 Months –Excludes firms where borrowing stock to short is restricted –Excludes firms where some institutions decline to trade LCRT extends the tests to include effects of –Longer holding periods for quarters 5-13 –Screening on signed model tracking error

46 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform THE LCRT RESEARCH DCF MODEL SEPARATES WINNERS AND LOSERS CONSISTENTLY THROUGH MOST YEARS BY A FACTOR OF 4 (= 200 / 50) OVER 9 YEARS Source: Industrial Firms , % Debt to Debt Capacity $5 Per Share; Hemscott Data, LCRT Platform Calculations; Annual Rebalancing; Purchase at Fiscal Year + 3 Months; Sale at Fiscal Year + 15 Months; No Transaction or Price Pressure Costs Included; Equal Weighted Takeaway … suggests purchasing under valued stocks outperforms the universe. Past performance of a back test is no guarantee of future performance.

47 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform THE SPREAD BETWEEN THE TOP AND BOTTOM DECILES OF LCRTS UNDER (OVER) VALUATION IS ABOUT 15% (9%+6%) Source: Industrial Firms , % Debt to Debt Capacity $5 Per Share; Hemscott Data, LCRT Platform Calculations; Annual Rebalancing; Purchase at Fiscal Year + 3 Months; Sale at Fiscal Year + 15 Months; No Transaction or Price Pressure Costs Included; Equal Weighted Takeaway … suggests the LCRT Research DCF Model under (over) valuation effectively separates performance as price migrates toward intrinsic value.

48 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform THE LCRT DCF RESEARCH MODEL SEPARATES WINNERS AND LOSERS CONSISTENTLY THROUGH QUARTERS FROM ANNUAL DATA WITH A PERSISTENCY BEYOND ONE YEAR Source: Industrial Firms , % Debt to Debt Capacity $5 Per Share; Hemscott Data, LCRT Platform Calculations; Annual Rebalancing; Purchase at Fiscal Year + 3 Months; Sale through Quarter indicated ; No Transaction or Price Pressure Costs Included; Equal Weighted Note the run down and run up of prices just prior to financial statement release, indicating Inflection Points. Takeaway … suggests the migration of price toward intrinsic value may take several quarters to 2-3 years.

49 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform FOR THE TOP DECILE OF UNDER-VALUED FIRMS, SCREENING ON TRACKING ERROR INCREASES RETURN FROM 20 TO 34 AND REDUCES ALPHA PEAKEDNESS RISK Region of Max Return and Min Peakedness Risk N=1,050 N=130 Takeaway … suggests that a more accurate model enhances return and reduces risk, but due care must also be given to the smaller number of stocks in the portfolio and the related potential torpedo risk of a few large losers. Source: Industrial Firms , % Debt to Debt Capacity $5 Per Share; Hemscott Data, LCRT Platform Calculations; Annual Rebalancing; Purchase at Fiscal Year + 3 Months; Sale at Fiscal Year + 15 Months; No Transaction or Price Pressure Costs Included; Equal Weighted Alpha Peakedness rises from 1.5 to 1.8 approaching Gaussian Normal (less risk)

50 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform FOR THE BOTTOM DECILE OF OVER-VALUED FIRMS, SCREENING ON TRACKING ERROR REDUCES RETURN FROM -2 TO -4 AND REDUCES ALPHA PEAKEDNESS RISK Region of Min Return and Min Peakedness Risk N=1,044 N=190 Takeaway … suggests that a more accurate model enhances return and reduces risk for shorts, but due care must also be given to the smaller number of stocks in the portfolio and the related potential torpedo risk of a few large losers. Source: Industrial Firms , % Debt to Debt Capacity $5 Per Share; Hemscott Data, LCRT Platform Calculations; Annual Rebalancing; Purchase at Fiscal Year + 3 Months; Sale at Fiscal Year + 15 Months; No Transaction or Price Pressure Costs Included; Equal Weighted Alpha Peakedness rises from 1.5 to 1.9 approaching Gaussian Normal (less risk)

51 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform CONCLUSIONSCONCLUSIONS These results extend our back test research to those firms with prices greater than $5 per share at Fiscal Year + 3 Months Over nine years, the top decile of under valued firms double in relative wealth, while the bottom decile of over valued firms loses half its value The spread between top and bottom deciles approximate 15% per year as price migrates toward intrinsic value The migration toward intrinsic value takes several quarters to 2-3 years –The run down and run up of prices during the quarter prior to the release of financial statements at Fiscal Year + 3 months suggest inflection points for under and (over) valued firms arising from the change in intrinsic valuations derived from Cash Economic Returns A more accurate model measured by tracking error significantly enhances return and reduces risk

52 © 2006 LifeCycle Returns, Inc. All Rights Reserved Sources: Financial Statements and Price Data – CapitalIQ & CoreData - Calculations – LCRT Platform PRESENTATION CONCLUSIONS Suggests two empirical research measurement methodologies to improve DCF models –Value Charts with tracking errors for individual companies (based on capitalization methods using only historical information with minimal analyst intervention) –Cumulative Tracking errors for large sample of companies Fading Cash Economic Returns provides a conceptual and empirical basis for dealing effectively with competitive reaction and its likely impact on the future cash flows of the firm Back tests suggest significant excess investment returns result from prices migrating toward intrinsic values over several quarters The Stable Paretian Alpha Peakedness parameter provides one replacement risk measure for traditional mean variance CAPM beta, as it identifies regions of the universe where the tails of the distribution become so fat that the mean becomes indeterminate


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