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 e-mail address to receive a link to the LCRT web site for this
presentation and other material or e-mail [email protected]
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
- 1 © 2006 LifeCycle Returns, Inc. All Rights Reserved
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
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
- 2 © 2006 LifeCycle Returns, Inc. All Rights Reserved
HISTORY OF ‘VALUE CHARTS’
Takeaway … The ‘Value Chart’ represents a powerful research tool for
illustrating the historical tracking of valuation models against actual price data.







Value Line began employing “Value
Charts” in the 1930’s 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
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
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.
- 3 © 2006 LifeCycle Returns, Inc. All Rights Reserved
INCLUDING ANALYSTS’ EPS ESTIMATES EXTENDS
THE ‘VALUE LINE’ INTO THE FUTURE
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.
 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
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
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.
- 4 © 2006 LifeCycle Returns, Inc. All Rights Reserved
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 multiyear forecast, but option pricing suggests an
alternative approach, illustrated later.
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
- 5 © 2006 LifeCycle Returns, Inc. All Rights Reserved
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 firm’s common stock.
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
- 6 © 2006 LifeCycle Returns, Inc. All Rights Reserved
TRADITIONAL DCF RESIDES AT THE
VERY ‘HEART OF VALUATION’
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.







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 multiperiod 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
company’s value with minimal analyst
intervention
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
Net Income
204,104
+ Depreciation
+22,772
+ Working Capital Decreases
+51,587
- Capital Expenditures
-34,809
= Net Free Cash Flow
243,654
- 7 © 2006 LifeCycle Returns, Inc. All Rights Reserved
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 company’s operations from the investor’s point of view,
adjusted for units of common purchasing power
CFROI® is a registered
Equals the real internal rate of return of all the projects in place
Trademark of CSFB
HOLT
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
- 8 © 2006 LifeCycle Returns, Inc. All Rights Reserved
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
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
- 9 © 2006 LifeCycle Returns, Inc. All Rights Reserved
Cash Economic Return
ADVANCED LCRT RESEARCH:
REPRESENTATIVE CASH ECONOMIC RETURN
FADE PATTERNS
80
60
40
20
0
(20)
(40)
(60)
(80)
Small High
Large High
Small Low
Large Low
0
1
2
3
4
5
Year
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
6
7
8
9
10
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.
- 10 © 2006 LifeCycle Returns, Inc. All Rights Reserved
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) + 3.00
 Applying the 3.54% to 21,770
investment produces a
$22,549 2005 investment
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
Year
2004
2005
Future
Growth
Rate
5.67
3.54
Constant
Dollar
Gross
Investment
21,779
22,549
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.
- 11 © 2006 LifeCycle Returns, Inc. All Rights Reserved
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
2005
 16.56 = 0.5 * (20.17 – 12.57)
+ 12.57
 Applying the 16.56% to the
22,549 2000 investment
produces 5,977 in gross
cash flow (net income +
depreciation)
 Constant Dollar Gross
Cash Investment Increases
770
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
Constant
Dollar
Gross
Cash
Investment
21,779
22,549
Year
2004
2005
Increase
Cash
Economic
Return
(CER)
20.17
16.56
Constant
Dollar
Gross
Cash
Flow
6,462
5,977
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.
- 12 © 2006 LifeCycle Returns, Inc. All Rights Reserved
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 $3,134 in net
constant dollar cash flows
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
Gross Cash Flows
+5,977
Replacement Investments
-1,973
Growth Investments
- 770
Constant Dollar Net
Free Cash Flow
+3,134
Takeaway … CMA Offshoots ultimately
produce Net Free Cash Flow, but unlike
traditional DCF models it is constant dollar
and derived from CFROI or CER and gross
asset growth rates as value drivers instead of
the traditional sales growth rates, margins,
and capital turns.
- 13 © 2006 LifeCycle Returns, Inc. All Rights Reserved
INTRINSIC VALUES PER SHARE
RESULT FROM TRADITIONAL
CALCULATIONS
 Present Value of
constant dollar net
cash flows forms the
80,516 enterprise
value
 Adding non-operating
cash, subtracting debt
and dividing by 2,911
shares outstanding
produces the 28.93
spot intrinsic value
per share
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
Present Value of Cash Flows
Cash Less Debt
Equity Intrinsic Value
+80,516
+ 3,687
+84,203
Number of Shares Outstanding
2,911
Equity Intrinsic Value Per Share
28.93
- 14 © 2006 LifeCycle Returns, Inc. All Rights Reserved
CMA OFFSHOOTS EMPLOY CFROI® OR CER
AND GROSS ASSET GROWTH RATES AS
PRIMARY VALUE DRIVERS
 The top panel
compares CER
to the discount
rate for HPQ
 The second
panel
compares
gross asset
growth rates to
sustainable
growth rates
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
- 15 © 2006 LifeCycle Returns, Inc. All Rights Reserved
CASH ECONOMIC RETURN EXAMPLE:
ACCOUNTING TO CASH
SUPERVALU– 2001 ($Millions)
Income
A: Eliminate Non-Operating
Special Extraordinary Items After Tax
Items
(-) Non-operating Expense After-Tax
(16)
B: Translate to Cash
Non-Cash Charges
333
C: Restate for Inflation
Inflation Gain on Non-Fixed Assets
D: Eliminate Leverage
After-Tax Interest (Debt and Operating Leases)
$206
Income
E: Capitalize Expenses
Assets
$5,825
$206
Items
$781
Rentals – Principal Payments
77
Current Dollar
(-) Advertising and R & D After Tax
(0)
Gross Cash Flow
(-) Non-Operating Assets
(-) Purchase Goodwill
Receivables Reserve
B: Translate to Cash Invest.
LIFO Reserve
Accumulated Depreciation
C: Restate for Inflation
14
134
Total Assets
A: Eliminate Non-Operating
33
$5,825
Current Dollar
(137)
Investor Gross
(1,531)
23
Investment
141
$5,704
1,580
Inflation Adjustments to Land, Gross Plant and
Deferred Taxes
249
D: Eliminate Leverage
Gross Leased Property from Operating Leases
1,202
E: Capitalize Expenses
Capitalized Advertising, R & D
F: Capital Owner Cash Invest.
(-) Operating Non-Interest Bearing Liabilities
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
Cash
0
(1,648)
- 16 © 2006 LifeCycle Returns, Inc. All Rights Reserved
CASH ECONOMIC RETURN EXAMPLE:
CASH TO ECONOMICS
SUPERVALU– 2001 ($ MILLIONS)
Current Dollar Gross Cash Flow
$781
Non-Depreciating
Asset Release
$727
($5,704)
Current Dollar
Investor Gross
Cash
Investment
Economic Life: 11.55 Years
Cash Economic Return - IRR: 9.09%
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
Years
IRR
11
8.62
12
9.48
11.55
9.09
- 17 © 2006 LifeCycle Returns, Inc. All Rights Reserved
Net Operating Assets +
Accumulated Depreciation +
Inflation Adjustment
CASH ECONOMIC RETURN REFLECTS THE
AVERAGE INTERNAL RATE OF RETURN OF ALL
THE PROJECTS IN PLACE
Operating Net Income + Depreciation Inflation Adjustments
Working Capital +
Land
Cash Economic
Return
Existing
Projects
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
- 18 © 2006 LifeCycle Returns, Inc. All Rights Reserved
Cash Economic Return
Fade-To
ADVANCED LCRT RESEARCH:
CASH ECONOMIC RETURN FADE TO’S RELY ON
SMALL FIRM PUT
AND MEDIUM SIZE STRADDLE FUNCTIONS
Smallest
Start-Up
Firms
40
35
30
25
20
15
10
5
0
Largest
Medium
Smallest
Largest and Smallest Firms
-100
-50
0
50
100
150
200
Beginning Cash Economic Return (CER)
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
- 19 © 2006 LifeCycle Returns, Inc. All Rights Reserved
Cash Economic Return
Fade Rates
ADVANCED LCRT RESEARCH:
CASH ECONOMIC RETURN FADE RATES
RELY ON PUT FUNCTIONS
100
80
Smallest
Medium
Largest
60
40
20
0
-100
-50
0
50
100
150
200
Beginning Cash Economic Return (CER)
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
- 20 © 2006 LifeCycle Returns, Inc. All Rights Reserved
% Loss of Intrinsic Value
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
80
70
60
50
40
30
20
10
0
[0,1] Function
of Equity Put
for ANY Debt
Smallest
Medium
Largest
Call Functions
0
25
50
75
100
125
150
% Debt to Debt Capacity (PV of Cash
Flows from Existing Assets)
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
- 21 © 2006 LifeCycle Returns, Inc. All Rights Reserved
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 – nonoperating items after
tax – normalized capital
expenditures – working
capital additions
Terminal year’s cash
flow capitalized by
median industry CAPM
nominal discount rate
less nominal growth
rate
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
(Absolute Tracking Error)
LCRT Model
(18.0%)
8 X EBITDA
(30.7%)
Net Free Cash Flow
(37.4%)
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.
- 22 © 2006 LifeCycle Returns, Inc. All Rights Reserved
A CUMULATIVE TRACKING
ERROR CHART SUMMARIZES
5,500 FIRMS FOR ABOUT
30,000 COMPANY-YEARS
Cumulative % of Universe
Median Absolute Tracking Errors
Net Free Cash Flow
166%
8 X EBITDA
86%
LCRT Model
51%
LCRT Model
8 X EBITDA
Net Free Cash Flow


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
Takeaways … Comparisons represent an objective
empirical research process for testing models and
improving DCF valuations for large samples of
firms.
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
More accurate models
are up and to the left.
Less accurate models
are down and to the
right.
LOG2 of % Absolute
Model Tracking Error
versus Actual Price –
Fiscal Year +3 Months to
reflect Disclosure Lag
1994-2004
5,500 Industrials
- 23 © 2006 LifeCycle Returns, Inc. All Rights Reserved
LCRT BACKTESTS
Annual
Quantile
Quarterly
- 24 © 2006 LifeCycle Returns, Inc. All Rights Reserved
THE LCRT RESEARCH DCF MODEL
SEPARATES “WINNERS” AND “LOSERS”
CONSISTENTLY THROUGH MOST YEARS
Performance of Top and Bottom 20%
Under (Over) Valued Firms
Past performance of a
back test is no guarantee
of future performance.
Wealth Index
10000
1000
Top 20%
Universe
Bottom 20%
100
Annual Rebalancing
10
1995
1997
1999
2001
2003
2005
Total Shareholder Return Ending Year
Source: Industrial Firms 19942003, % Debt to Debt Capacity <
62%; Hemscott Data, LCRT
Sources: Financial
Statements Calculations
and Price Data – CapitalIQ &
Platform
CoreData - Calculations – LCRT Platform
Purchase at Fiscal Year + 3
Months
Sale at Fiscal Year + 15
Months
No Transaction or Price
Pressure Costs Included
Equal Weighted
- 25 © 2006 LifeCycle Returns, Inc. All Rights Reserved
LCRT’S RESEARCH DCF MODEL
SEPARATES THE UNIVERSE INTO
“WINNERS” & “LOSERS”
Stock Performance Relative to Under (Over)
Valuation at FY + 3 Mos.
60
Total
40
Shareholder
Return Relative 20
to S&P 500 FY
0
+3 to +15 Mos.
-20
Universe
Large
Company Size
Source: Industrial Firms 19942003, % Debt to Debt Capacity <
62%; Hemscott Data, LCRT
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations
– LCRT Platform
Platform
Calculations
Small
Top 5%
Top 10%
Top 20%
2nd 20%
3rd 20%
4th 20%
Bottom 20%
Bottom 10%
Bottom 5%
No Transaction or Price Pressure Costs Included
Equal Weighted
- 26 -
© 2006 LifeCycle Returns, Inc. All Rights Reserved
THE LCRT DCF RESEARCH MODEL SEPARATES “WINNERS”
AND “LOSERS” CONSISTENTLY THROUGH QUARTERS
FROM ANNUAL DATA
Wealth Index
Performance of Top and Bottom 20%
Under (Over) Valued Firms @ FY + 3 Months
150
140
130
120
110
100
90
Top 20%, N = 3,426
Universe, N = 17,095
Bottom 20%, N = 3,407
1
2
3
4
5
Total Shareholder Return Ending Quarter
Source: Industrial Firms 19942003, % Debt to Debt Capacity <
62%; Hemscott Data, LCRT
Sources: Financial
Statements
and Price Data – CapitalIQ &
Platform
Calculations
CoreData - Calculations – LCRT Platform
No Transaction or Price Pressure Costs Included
Equal Weighted
- 27 © 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
- 28 © 2006 LifeCycle Returns, Inc. All Rights Reserved
INTRODUCTION
 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
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
- 29 © 2006 LifeCycle Returns, Inc. All Rights Reserved
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
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
 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?
- 30 © 2006 LifeCycle Returns, Inc. All Rights Reserved
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.
307-332.
–
Benoit Mandelbrot and Richard L. Hudson, The (Mis)Behavior of Markets: A
Fractal View of Risk, Ruin, and Reward, Basic Books, 2004.
–
J. Huston McCulloch, “Simple Consistent Estimators of Stable Distribution
Parameters,” Commun. Statist. – Simula., 15(4), 1986, pp. 1109-1136.
(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
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
- 31 © 2006 LifeCycle Returns, Inc. All Rights Reserved
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
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
- 32 © 2006 LifeCycle Returns, Inc. All Rights Reserved
A FAT TAILED STABLE PARETIAN DISTRIBUTION DISPLAYS A
BETTER VISUAL FIT TO TOTAL SHAREHOLDER RETURN
DATA THAN DOES GAUSSIAN NORMAL
3000
3000
2500
2500
2000
Actual
1500
Normal
1000
Number of Company - Years
Number of Company - Years
Takeaway … This suggests potential for the use of non-traditional measures
of risk based on fat tailed Stable instead of Gaussian distributions
2000
500
0
0
20
80 14 0 20 0 26 0 32 0 38 0
Total Shareholder Returns
Stable
1000
500
00 -4 0
-1
Actual
1500
00
-1
0
-4
20
80 14 0 20 0 26 0 32 0 38 0
Total Shareholder Returns
Sources: 5.500 Industrial Firms 1994-2003, 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. 1109-1136.
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
- 33 © 2006 LifeCycle Returns, Inc. All Rights Reserved
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)
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
Results
Value
Std. Error
t-Statistic
alpha ("peakedness")
1.39
0.01
41.41
Difference from 2.00
beta ("skewness")
0.83
0.03
32.27
Difference from 0.00
c ("dispersion")
33.02
0.01
4,205.23
Difference from 0.00
delta ("location" or "average")
24.12
0.05
449.93
Difference from 0.00
Sources: 5,500 Industrial Firms 1994-2003, 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. 1109-1136.
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
- 34 © 2006 LifeCycle Returns, Inc. All Rights Reserved
A FAT TAILED STABLE PARETIAN DISTRIBUTION DISPLAYS A
BETTER VISUAL FIT TO LN OF TOTAL SHAREHOLDER
RETURN DATA THAN 2.00 FOR GAUSSIAN NORMAL
3000
2500
2500
LN of Wealth Index from Total
Shareholder Return Relative to S&P 500
1.6
0.9
0.2
-0.5
1.6
0.9
0.2
-0.5
-1.2
0
-1.9
0
-2.6
500
-3.3
500
-1.2
1000
-1.9
1000
Stable
-2.6
Normal
Actual
1500
-3.3
Actual
1500
2000
-4
2000
Number of Company - Years
3000
-4
Number of Company - Years
Takeaway … This suggests the LN transform or assuming a log
normal distribution is inadequate to fix the fit problem.
LN of Wealth Index from Total
Shareholder Return Relative to S&P 500
Sources: 5,500 Industrial Firms 1994-2003, 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. 1109-1136.
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
- 35 © 2006 LifeCycle Returns, Inc. All Rights Reserved
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
Takeaway … again suggesting the limitations in the use of CAPM Beta as a risk measure
Results
alpha ("peakedness")
beta ("skewness")
c ("dispersion")
delta ("location" or "average")
Value
Std. Error
t-Statistic
1.48
0.01
43.41
Difference from 2.00
-0.31
0.02
-17.55
Difference from 0.00
0.39
0.01
50.60
Difference from 0.00
-0.16
0.02
-7.32
Difference from 0.00
Sources: 5,500 Industrial Firms 1994-2003, 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. 1109-1136.
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
- 36 © 2006 LifeCycle Returns, Inc. All Rights Reserved
THE % UNDER/OVER VALUATION OF OUR DCF “RESEARCH
MODEL” ALSO DISPLAYS STABLE PARETIAN DISTRIBUTION
CHARACTERISTICS
Takeaway … you should consider employing different risk measures if
you are using over/under intrinsic value as an investment decision tool
1600

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%
Number of Company - Years
1400
1200
1000
Actual
800
Stable
600
400
Value
Std.
Error
t-Statistic
alpha ("peakedness")
1.33
0.02
36.90
Difference from 2.00
beta ("skewness")
1.00
0.03
31.37
Difference from 0.00
c ("dispersion")
44.03
0.01
4,264.60
Difference from 0.00
delta ("location" or
"average")
65.68
0.10
668.51
Difference from 0.00
Results
200
460
390
320
250
180
110
40
-30
-100
0
LCRT Research Model % Under
(Over) Valuation
Sources: 5,500 Industrial Firms 1994-2003, 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. 1109-1136.
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
- 37 © 2006 LifeCycle Returns, Inc. All Rights Reserved
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
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
- 38 © 2006 LifeCycle Returns, Inc. All Rights Reserved
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(?)
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.

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
% under (over) valuation = 100% * (intrinsic value – price) / price.
Number of Company - Years
250
Regions < -100% probably represent firms where debt trades at a
discount from par.
200
150
Actual
Stable
100
50
Value
Std.
Error
t-Statistic
alpha ("peakedness")
1.07
0.03
-1.91
Difference from 1.00
beta ("skewness")
0.82
0.04
20.59
Difference from 0.00
71.91
0.04
1,827.43
Difference from 0.00
#N/A
Difference from 0.00
Results
0
00 2 00 1 00
-3
-
0
0
0
0
0
10 20 30 40
0
50
LCRT Research Model % Under
(Over) Valuation
c ("dispersion")
delta ("location" or
"average")
538.21
#N/A
Sources: From 5,500 Industrial Firms 1994-2003, 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. 1109-1136.
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
- 39 © 2006 LifeCycle Returns, Inc. All Rights Reserved
THE TOP QUINTILE (20%) OF UNDER VALUED FIRMS SHOW A
34.3% MEAN RELATIVE SHAREHOLDER RETURN AND
A DETERMINATE 1.38 ALPHA PEAKEDNESS
Takeaway… This suggests that in this area of the universe, diversification can be used to achieve mean performance
350

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
Number of Company - Years
300
Mean = 34.3
250
200
Actual
Stable
150
100
50
0
-100
Value
Std.
Error
t-Statistic
alpha ("peakedness")
1.38
0.04
-8.73
Difference from 1.00
beta ("skewness")
0.99
0.08
12.07
Difference from 0.00
c ("dispersion")
36.29
0.02
1,548.21
Difference from 0.00
delta ("location" or
"average")
48.37
0.18
269.51
Difference from 0.00
Results
0
100
200
300
400
500
Total Shareholder Returns
Sources: From 5,500 Industrial Firms 1994-2003, 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. 1109-1136.
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
- 40 © 2006 LifeCycle Returns, Inc. All Rights Reserved
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
Takeaway … suggesting that in this area of the universe, diversification can’t be used to achieve mean performance
100
90
Mean = 61.8
Number of Company - Years
80
70
Traditional
dispersion
risk
measures
of standard
deviation
and CAPM
Beta don’t
pick up this
effect
60
50
40
30
20
Actual
 The “risk” of one or
more torpedo stocks
is too great compared
to large gains of a few
stocks
Stable
Value
Std.
Error
t-Statistic
alpha ("peakedness")
1.20
0.11
-1.86
Difference from 1.00
beta ("skewness")
1.00
0.16
6.27
Difference from 0.00
48.41
0.07
669.22
Difference from 0.00
Results
10
c ("dispersion")
0
-100
150
400
650
900
delta ("location" or
"average")
131.59
#N/A
#N/A
Difference from 0.00
Total Shareholder Returns
Sources: 529 Small Industrial Firms 1994-2003, 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. 1109-1136.
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
- 41 © 2006 LifeCycle Returns, Inc. All Rights Reserved
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
Value
Std.
Error
t-Statistic
0.92
0.01
8.08
Difference from 1.00
beta ("skewness")
-0.37
0.01
-25.81
Difference from 0.00
9000
c ("dispersion")
9000
4.02
0.02
258.78
Difference from 0.00
8000
delta ("location" or
"average") 8000
18.58
7000
7000
Results
alpha ("peakedness")
6000
5000
Actual
Normal
4000
3000
2000
Number of Company - Years
Number of Company - Years
Takeaway …A lot of “risk” exists in
estimating future changes in the Cash
Economic Return of selected stocks.
5000
0
25
50
75 10 0
Cash Economic Return
The LCRT approximation procedure
divides the Stable Paretian intervals
into 128 pieces (limited by Excel’s 256
columns), which is not sufficient
enough to model the tails accurately
for distributions fatter than Cauchy.
2000
0
5
-2
Stable
3000
0
0
-5
Difference from 0.00
Actual
4000
1000
5
-7
#N/A
6000
1000
00
-1
#N/A
00
-1
5
-7
0
-5
5
-2
0
25
50
75 10 0
Cash Economic Returns
Sources: 5,500 Industrial Firms 1994-2003, 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. 1109-1136.
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
- 42 © 2006 LifeCycle Returns, Inc. All Rights Reserved
CONCLUSIONS
 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
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
- 43 © 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
- 44 © 2006 LifeCycle Returns, Inc. All Rights Reserved
INTRODUCTION
 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
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
- 45 © 2006 LifeCycle Returns, Inc. All Rights Reserved
THE LCRT RESEARCH DCF MODEL SEPARATES “WINNERS” AND
“LOSERS” CONSISTENTLY THROUGH MOST YEARS BY A FACTOR
OF 4 (= 200 / 50) OVER 9 YEARS
Takeaway … suggests purchasing under valued stocks outperforms the universe.
Performance of Top and Bottom 10%
Under (Over) Valued Firms
Wealth Index
250
200
Top 10%
Universe
Bottom 10%
150
100
50
0
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Total Shareholder Return Ending Year
Source: Industrial Firms 1994-2003, % Debt to Debt
Capacity < 83%; Prices > $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
Sources: Financial Statements and Price Data – CapitalIQ &
Price Pressure
Included;
Equal Weighted
CoreData - Costs
Calculations
– LCRT Platform
Past performance of a
back test is no guarantee
of future performance.
- 46 © 2006 LifeCycle Returns, Inc. All Rights Reserved
THE SPREAD BETWEEN THE TOP AND BOTTOM DECILES OF
LCRT’S UNDER (OVER) VALUATION IS ABOUT 15% (9%+6%)
Takeaway … suggests the LCRT Research DCF Model under (over) valuation
effectively separates performance as price migrates toward intrinsic value.
Stock Performance Relative to Under (Over)
Valuation at FY + 3 Mos.
10
Total
Shareholder
Return Relative
to S&P 500 FY
+3 to +15 Mos.
5
0
-5
-10
Firms with Stock Prices Over $5
Per share
Source: Industrial Firms 1994-2003, % Debt to Debt
Capacity < 83%; N=16,026 Company-Years; Prices > $5 Per
Share; Hemscott Data, LCRT Platform Calculations;
Annual Rebalancing; Purchase at Fiscal Year + 3 Months;
Sale
at Fiscal
Year
+ 15 Months;
Sources:
Financial
Statements
and Price No
DataTransaction
– CapitalIQ & or Price
PressureCoreData
Costs Included;
Equal
Weighted
- Calculations – LCRT Platform
Top 5%
Top 10%
Top 20%
2nd 20%
3rd 20%
4th 20%
Bottom 20%
Bottom 10%
Bottom 5%
- 47 © 2006 LifeCycle Returns, Inc. All Rights Reserved
THE LCRT DCF RESEARCH MODEL SEPARATES “WINNERS” AND “LOSERS”
CONSISTENTLY THROUGH QUARTERS
FROM ANNUAL DATA WITH A PERSISTENCY BEYOND ONE YEAR
Takeaway … suggests the migration of price toward intrinsic value may take several quarters to 2-3 years.
Total Shareholder Return
Ending Quarter Relative to S&P 500
Source: Industrial Firms 1994-2003, % Debt to Debt
Capacity < 83%; Prices > $5 Per Share; Hemscott
Data, LCRT Platform Calculations; Annual
Rebalancing; Purchase at Fiscal Year + 3 Months;
Sale through Quarter indicated ; No Transaction or
Sources:
Financial Statements
and Price Data
– CapitalIQ
&
Price
Pressure
Costs Included;
Equal
Weighted
CoreData - Calculations – LCRT Platform
9
10
11
12
13
1
2
3
4
Note the run down and run up of prices
just prior to financial statement
release, indicating Inflection Points.
5
6
7
8
Top 10%, N = 1,508
Universe, N = 15,166
Bottom 10%, N = 1,478
150
140
130
120
110
100
90
80
FY
Wealth Index
Performance of Top and Bottom 10%
Under (Over) Valued Firms @ FY + 3 Months
- 48 © 2006 LifeCycle Returns, Inc. All Rights Reserved
FOR THE TOP DECILE OF UNDER-VALUED FIRMS, SCREENING
ON TRACKING ERROR INCREASES RETURN FROM 20 TO 34
AND REDUCES ALPHA PEAKEDNESS RISK
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.
1.3
Region of Max
Return and Min
Peakedness Risk
40
35
1.4
N=130
1.5
30
25
1.6
20
1.7
N=1,050
15
1.8
10
Alpha Peakedness rises
from 1.5 to 1.8 approaching
Gaussian Normal (less risk)
5
1.9
Mean TSR
Peakedness
Year
1998
1999
2000
2001
2002
2003
N
8
14
19
35
31
23
130
12
8
im
Un
l
(9
5t
h)
80
(9
0t
h)
53
(8
5t
h)
39
(8
0t
h)
27
(7
5t
h)
18
(7
0t
h)
11
(6
5t
h)
4
(6
0t
h)
-2
(5
5t
h)
-8
(5
0t
h)
-1
4
(4
5t
h)
-1
9
(4
0t
h)
-2
5
(3
5t
h)
2
it e
d
0
Alpha Peakedness Risk Parameter of
Stable Paretian Distribution
Total Shareholder Return Relative to S&P
500 FY +3 to +15 Mos.
45
Signed Model Tracking Error (Percentile)
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
Source: Industrial Firms 1998-2003, % Debt to Debt
Capacity < 83%; Prices > $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 - 49 Price Pressure Costs© Included;
Equal
Weighted
2006 LifeCycle
Returns,
Inc. All Rights Reserved
FOR THE BOTTOM DECILE OF OVER-VALUED FIRMS,
SCREENING ON TRACKING ERROR REDUCES RETURN FROM
-2 TO -4 AND REDUCES ALPHA PEAKEDNESS RISK
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.
(5
th
)
-5
6
(1
0t
h)
-4
9
(1
5t
h)
-4
2
(2
0t
h)
-3
6
(2
5t
h)
-3
0
(3
0t
h)
-2
5
(3
5t
h)
-1
9
(4
0t
h)
-1
4
(4
5t
h)
-8
(5
0t
h)
-2
(5
5t
h)
4
(6
0t
h
11 )
(6
5t
18 h)
(7
0t
h)
-6
9
Un
l
im
it e
d
Signed Model Tracking Error (Percentile)
1.3
0
N=1,044
Alpha Peakedness rises
from 1.5 to 1.9 approaching
Gaussian Normal (less
risk)
1.4
1.5
-2
1.6
-4
N=190
1.7
-6
1.8
-8
Region of Min
Return and Min
Peakedness Risk
-10
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
1.9
Alpha Peakedness Risk Parameter of
Stable Paretian Distribution
Total Shareholder Return Relative to S&P
500 FY +3 to +15 Mos.
2
Mean TSR
Peakedness
Year
1998
1999
2000
2001
2002
2003
N
17
20
20
35
35
63
190
2 Firms 1998-2003, % Debt to Debt
Source: Industrial
Capacity < 83%; Prices > $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 - 50 Price Pressure Costs© Included;
Equal
Weighted
2006 LifeCycle
Returns,
Inc. All Rights Reserved
CONCLUSIONS
 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
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
- 51 © 2006 LifeCycle Returns, Inc. All Rights Reserved
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
Sources: Financial Statements and Price Data – CapitalIQ &
CoreData - Calculations – LCRT Platform
- 52 © 2006 LifeCycle Returns, Inc. All Rights Reserved