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Business Development, An ‘S’ Curve Analysis. The S-Curve. Innovation . Growth . Maturity . 100 . 90% . 99% . 99.9% . 90 . 80 . 70. 60 . 50. 50% . 40 . 30. 20. 10. 1% . .1% . 10% . 0 . Percent Adoption. The Industry Life Cycle. The S-Curve in Cars.

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Business development an s curve analysis
Business Development, An ‘S’ Curve Analysis


The s curve
The S-Curve

Innovation

Growth

Maturity

100

90%

99%

99.9%

90

80

70

60

50

50%

40

30

20

10

1%

.1%

10%

0

Percent Adoption



The s curve in cars
The S-Curve in Cars

Percent of Urban Households

1900

1907

1942

1921

1935

1914

1928

Assembly Line

Installment Financing

90% Urban Adoption

Cars only for the Rich

Model T Design


Mobile phone s curve
Mobile Phone S-Curve

Innovation

Growth

Maturity

100

90

80

70

60

50

40

30

20

10

0

90%

86%

2007

82%

2006

73%

77%

2005

63%

2004

58%

50%

2003

Percent of Households

2002

47%

2000

13%

10%

1995

2%

1%

1990

2001

1994

2008

Time

Source: Forrester, Census Bureau


Internet s curve
Internet S-Curve

Innovation

Growth

Maturity

100

90

80

70

60

50

40

30

20

10

0

83%

74%

79%

2009

73%

2007

71%

2006

2005

67%

2004

66%

2003

61%

2002

50%

2001

Percent of Households

31%

1999

22%

1998

17%

10%

1997

2000

2007

1993

Time

Source: Pew Internet


Broadband s curve
Broadband S-Curve

Innovation

Growth

Maturity

100

90

80

70

60

50

40

30

20

10

0

90%

91%

2009

80%

2007

63%

2006

50%

Percent of Households

37%

2004

22%

2002

10%

2000.5

2008.5

2004.5

Time

Source: Pew Internet


Digital camera s curve
Digital Camera S-Curve

Innovation

Growth

Maturity

100

90

80

70

60

50

40

30

20

10

0

90%

77%

2009

62%

2007

60%

2005

Percent of Households

43%

2003

10%

1997

2004

2011

Time

Source: Infotrends, Consumer Electronics Association


High definition tv s curve
High-Definition TV S-Curve

Innovation

Growth

Maturity

100

90

80

70

60

50

40

30

20

10

0

90%

53%

2009

50%

Percent of Households

35%

2008

23%

2007

10%

3%

2009

2005

2013

2001

Time

Source: CTAM


Car gps systems s curve
Car GPS Systems S-Curve

Innovation

Growth

Maturity

100

90

80

70

60

50

40

30

20

10

0

90%

99%

50%

Percent of Households

17%

3%

2009

10%

0.9%

1%

2003

2018

2008

2013

2002

2005

Time

Source: Masterlink


S curves
S-Curves

Source: NY Times


S curves1
S-Curves

Innovations follow a curved pattern of acceptance, or “lifecycle”

Industry supplies on a different cycle

Many innovations are moving through the second half of their growth phase, and will peak near the end of the decade

Innovations tend to be developed by the young



Combining generational spending trends inflation and interest rates
Combining Generational Spending Trends, Inflation, and Interest Rates

40 year generations

Predictable consumer spending

Workforce pressure on inflation

Ebb & flow of interest rates


Simple four season economic cycle two forty year generation boom bust cycles
Simple Four Season Economic Cycle Interest RatesTwo Forty-Year Generation Boom/Bust Cycles

Generation Spending Boom

Stocks/ Economy

Summer

Spring

Fall

Winter


Simple four season economic cycle eighty years in modern times
Simple Four Season Economic Cycle Interest RatesEighty Years in Modern Times

Consumer Prices/ Inflation

Generation Spending Boom

Stocks/ Economy

Summer

Spring

Fall

Winter


Video chris martenson crash course fuzzy numbers
VIDEO Interest RatesChris Martenson, Crash CourseFuzzy Numbers


How average are you
How Average Are You? Interest Rates


How average are you1
How Average Are You? Interest Rates

Believe God exists (80%)

Larry, Mo, Curly (& Schemp) (89%)

Legislative, Judicial, & Executive (20%)

Does NOT have a college degree (65%)

Take a bath or shower

(10.4 minute shower, daily)

Own stocks?( 50/50)

Live in same state (60%)

Have 2 children

Eat 3 lb’s of PB per year

Do NOT floss regularly (90%)

Exercise once a week

Recycle (50%)

Shop At Walmart at least Annually (80%)


Selected average statistics
Selected “Average” Statistics Interest Rates

Drinks 55 gallons of soda a year

Does not wash his hands properly after using public restrooms

Throws away more than 100 lbs of food per year

25% of Americans over 18 abstain from alcohol for life

69% of Americans go to the movie theater at least annually


The average american federal reserve survey of consumer finance
The Average American Interest RatesFederal Reserve Survey of Consumer Finance

2001 2004 20072009

Median Family Inc $42.2k $44.3k $50.2k$49.7k

College Degree34.0% 36.6% 35.3%

Cred. Card Bal.44.4% 46.2% 47.8%43.2%

Amount of Bal.$2.0k $2.4k $3.0k$3.3k

Of those 45-54

Own Ret. Acct.63.4% 57.7% 68%68.2%

Amount in it$51.1k $61.0k $81.4k$73k


Percent of workers by total amount of retirement savings 2011
Percent of Workers by Total Interest RatesAmount of Retirement Savings 2011

Source: Employee Benefit Research Institute, HS Dent Research


Percent of workers and retirees by total amount of retirement savings 2011
Percent of Workers and Retirees by Total Amount of Retirement Savings 2011

Source: Employee Benefit Research Institute, HS Dent Research


Percent of workers and retirees by total amount of retirement savings 20111
Percent of Workers and Retirees by Total Amount of Retirement Savings 2011

Source: Employee Benefit Research Institute, HS Dent Research


Analyzing data
Analyzing Data Retirement Savings 2011


Statistics and other math
Statistics And Other Math Retirement Savings 2011

Dispersion

Correlation Coefficients

Normal Distribution


Normal distribution bell curve
Normal Distribution (Bell Curve) Retirement Savings 2011

Gaussian distribution needs only two parameters to describe – mean, and variance

68.26% of observations fall w/in 1 standard deviation of the mean

95.44% w/in 2 standard deviations of the mean

99.74% w/in 3 standard deviations of the mean


The normal distribution aka the bell curve
The Normal Distribution Retirement Savings 2011aka, the “Bell Curve”

Number of Observations

68% fall within +/- 1 standard deviation

-4

-3

-2

0

1

2

3

4

-1

Standard Deviations

Source: H.S. Dent Foundation


Business development an s curve analysis

The Normal Distribution Retirement Savings 2011aka, the “Bell Curve”

Number of Observations

95% fall within +/- 2 standard deviations

-4

-3

-2

0

1

2

3

4

-1

Standard Deviations

Source: H.S. Dent Foundation


The normal distribution aka the bell curve1
The Normal Distribution Retirement Savings 2011aka, the “Bell Curve”

Number of Observations

99% fall within +/- 3 standard deviations

-4

-3

-2

0

1

2

3

4

-1

Standard Deviations

Source: H.S. Dent Foundation


Assuming returns are normal
Assuming Returns Retirement Savings 2011Are “Normal”

Financial software assumes that investment returns are normally distributed around a mean, or average, return (9% for Large Cap Stocks, per SBBI through 2007)

This assumption is made because it is true – usually.


The flaws of return estimates why returns are not always normal
The Flaws of Return Estimates Retirement Savings 2011(Why Returns Are Not Always “Normal”)

Returns are not independent of each other

Returns can be “clustered,” as individual returns are influenced by the same outside variable

Dispersion renders return estimates unusable


Volatility clustering
Volatility Clustering Retirement Savings 2011

Returns are not independent, they rely on underlying economic events and trends

These trends can occur over long periods

Tech Bubble

Tech Bust

9/11

Recent Credit Crisis

Central Bank Actions


Returns gain momentum not independent
Returns Gain Momentum Retirement Savings 2011(not independent)

Most days on equity markets are marked by small, incremental changes. Large percentage changes, however, tend to be followed by large changes.

This is called “volatility clustering”, indicating that exceptional volatility happens in sequence.


True distribution of returns
True Distribution of Returns Retirement Savings 2011

Instead of being Gaussian, or Normal Curve, investment returns fall along a Cauchy Distribution, which exhibits a higher mean, less observations along the curve, and “fat tails”.


Stock returns normal distribution assumed
Stock Returns Retirement Savings 2011Normal Distribution Assumed

1987 Crash was 20 standard deviations past the mean – a statistical impossibility if returns were truly normal!

1933 “impossible” one-day rally

Monster Bear Market Rally in July 2002

Back-to-back “long tail” days during 1929 Crash


Daily price changes djia 1998
Daily Price Changes Retirement Savings 2011DJIA 1998


Daily price changes djia 1928 2011
Daily Price Changes Retirement Savings 2011DJIA 1928-2011

Credit Crisis

Returns vary wildly over time

Tech Boom and Bust

1940s-60s: Low Volatility

Low Volatility

Roaring 20s and Depressionary 30s: High Volatility

1987 Crash: Unprecedented Volatility

Data Source: Bloomberg, 2011


Impossible market days
Impossible Market Days Retirement Savings 2011

Chance of August 31st, 1998 – 1 in 20mm

Chance of the 3 declines in August 1998 – 1 in 500mm

Chance of October 19th, 1987 – less than one in 10 to the negative 50th power, a number that does not occur in nature


What we know about market risk
What We Know About Retirement Savings 2011Market Risk

“Average Return” is poor guide of what will happen – variance and standard deviation too great

Returns are not “Normally” distributed, instead the distribution has “Fat Tails”

Returns are not Independent, there is clear evidence of clustering of returns


Markowitz sticks by his theory
Markowitz Sticks by His Theory Retirement Savings 2011

Those who say a normal distribution shouldn't be used "don't know what they're talking about," said Harry Markowitz, the developer of MPT, who now runs an eponymous San Diego consulting firm.

"If the probability of distributions [on a portfolio] is not too spread out, from a 30% [loss] to a 40% gain," it's OK to use a normal curve, he said.

  • Modern portfolio theory may face more skepticism

  • By Dan Jamieson March 10, 2008, Investment News


Investing is riskier than commonly described
Investing is Riskier Than Commonly Described Retirement Savings 2011

Because investment returns exhibit “fat tails”, the extreme observations or returns are more likely than we would assume.

We value loss more than we value gains (2x).

These two facts together mean that investing in equities is much riskier than we normally describe.


The human model of forecasting
The Human Model of Forecasting Retirement Savings 2011

“We won’t have

recessions

anymore”

“It’s a soft landing”

“Things are so bad they

will never improve”

Source: H.S. Dent graphic interpretation of data in 2002 Schweser CFA Study Program, Chapter 15, pp 144-45.


Investing is never satisfying
Investing Is NEVER Satisfying Retirement Savings 2011

We tend to estimate what will happen based on most recent experience

When our accounts are up, we compare to others (relative income hypothesis)

When our accounts are down, we feel greater loss because we value loss at 2x gains


It s hard for us to stay true to a model even mr markowitz
It’s Hard For Us To Stay True to a Model, Even Mr. Markowitz

“Mr. Markowitz was then working at the Rand Corporation and trying to figure out how to allocate his retirement account. He knew what he should do: ‘I should have computed the historical co-variances of the asset classes and drawn an efficient frontier’...

But, he said, ‘I visualized my grief if the stock market when way up and I wasn’t in it – or if it went way down and I was completely in it.

So I split my contributions 50/50 between stocks and bonds.’”

Can We Turn Off Our Emotions When Investing?

Joe Nocera, 9/27/07, NYT, quoting Jason Zweig’s book, “Your Money & Your Brain” (Simon & Schuster)


Investors already knew this
Investors Already Knew This Markowitz

Even though the return data of the last 82 years shows that large cap stocks return, on average, 9%, investors and advisors are never surprised when their personal experience is something other than 9%. Why? Because everyone knows intuitively that the average is not instructive on what will happen next. It is so unreliable as to have no predictive value.

And yet it is the basis of all financial software.


Sequence more important than average
Sequence More Important Than Average Markowitz

The order in which your returns are earned is more important than the overall average.

Consider a worker who saves over 30 years. If the worst 5 years of the whole period are at the end, it is significantly different than if the best five years are at the end.

It is all in expectations.


Stock returns 1966 1970
Stock Returns Markowitz1966-1970

Source: Ibbotson SBBI, Large Company Stocks: Total Returns


Stock returns 1996 2000
Stock Returns Markowitz1996-2000

Source: Ibbotson SBBI, Large Company Stocks: Total Returns


Understanding risk
Understanding Risk Markowitz

Understanding samples and margins of error

Explaining normal distributions and standard deviations

Explaining flaws of applying normal distributions to investment returns

Reading list – Mandelbrot, Taleb


Investing in each season what you are told vs what really happens
Investing In Each Season MarkowitzWhat you are told vs. what really happens


Efficient frontier 1970 2007
Efficient Frontier, 1970-2007 Markowitz

1970-2007

Source: Advisory World, HS Dent

Source: Advisory World, HS Dent


Efficient frontier 1970 2007 and 1970s
Efficient Frontier, 1970-2007 Markowitzand 1970s

1970-2007

1970s

Source: Advisory World, HS Dent

Source: Advisory World, HS Dent


Efficient frontier 1970 2007 and 1970s 1980s
Efficient Frontier, 1970-2007 Markowitzand 1970s, 1980s

1980s

1970-2007

1970s

Source: Advisory World, HS Dent

Source: Advisory World, HS Dent


Efficient frontier 1970 2007 and 1970s 1980s 1990s
Efficient Frontier, 1970-2007 Markowitzand 1970s, 1980s, 1990s

1990s

1980s

1970-2007

1970s

Source: Advisory World, HS Dent

Source: Advisory World, HS Dent


Efficient frontier 1970 2007 and 1970s 1980s 1990s 2000s
Efficient Frontier, 1970-2007 Markowitzand 1970s, 1980s, 1990s, 2000s

1990s

Return (%)

1980s

1970-2007

1970s

2000s

Article #5 MPT/Markowitz

Source: Advisory World, HS Dent

Source: Advisory World, HS Dent



Immigration not job hunting
Immigration, MarkowitzNOT JOB HUNTING

Historically, immigrants moved here to stay

Now, immigrants come for work, no intention of staying


Immigration to the united states 1820 2010
Immigration to the MarkowitzUnited States 1820-2010

Data Source: U.S. Department of Homeland Security, 2011


Average immigrants per year by age 1945 2000
Average Immigrants per Year Markowitzby Age 1945-2000

Source: US Census Bureau


Migration of mexicans into and out of mexico
Migration of Mexicans Into and Out of Mexico Markowitz

Source: Pew Hispanic Center, 2011


White and hispanic populations by age
White and Hispanic MarkowitzPopulations by Age

Source: US Census Bureau, 2000 Census


Migration flows
Migration Flows Markowitz


Movers to a different state by age 2000 2005
Movers to a Different State by Age Markowitz2000-2005

Source: US Census Bureau


Movers to a different state by age 2006 2010
Movers to a Different State by Age Markowitz2006-2010

Data Source: US Census Bureau, 2011


Movers to different state young vs old 2000 to 2005
Movers to Different State: MarkowitzYoung vs. Old 2000 to 2005

3.5x

In Thousands

Data Source: US Census Bureau


Movers to different state young vs old 2006 to 2010
Movers to Different State: MarkowitzYoung vs. Old 2006 to 2010

6.8x

In Thousands

Data Source: US Census Bureau, 2011


Of americans moving each year 1948 2005
% of Americans Moving Each Year Markowitz1948-2005

Source: US Census Bureau


Effect of recession on household composition
Effect of Recession on Household Composition Markowitz

Source: US Census Bureau, 2010


State population growth
State Population Growth Markowitz

Source: New York Times


United van lines migration patterns 2007
United Van Lines MarkowitzMigration Patterns 2007

Source: United Van Lines, via Unigroup, Inc.


United van lines migration patterns 2009
United Van Lines MarkowitzMigration Patterns 2009

Source: United Van Lines, via Unigroup, Inc.


United van lines migration patterns 2010
United Van Lines MarkowitzMigration Patterns 2010

Source: United Van Lines, via Unigroup, Inc., 2011


Top 10 outbound states 2010
Top 10 Outbound States Markowitz2010

Data Source: United Van Lines, 2011


Top 10 inbound states 2010
Top 10 Inbound States Markowitz2010

Data Source: United Van Lines, 2011


Top 10 inbound states 20101
Top 10 Inbound States Markowitz2010

Data Source: United Van Lines, 2011


Lack of mobility from downturn
Lack of Mobility from Downturn Markowitz

NYT April 23, 2009

Slump Creates Lack of Mobility for Americans

By SAM ROBERTS

“Stranded by the nationwide slump in housing and jobs, fewer Americans are moving, the Census Bureau said Wednesday.

The bureau found that the number of people who changed residences declined to 35.2 million from March 2007 to March 2008, the lowest number since 1962, when the nation had 120 million fewer people.”


Real estate
Real Estate Markowitz


Long term house prices vs inflation
Long Term House Prices vs. Inflation Markowitz

Source: Robert J. Shiller, Irrational Exuberance, 2nd Edition, Princeton University Press, 2005.


Real estate spending cycles
Real Estate Spending Cycles Markowitz

Vacation Homes

46-50

Resorts

Trade-Up Homes

54

37-42

Vacation / Retirement Homes

29-33

Spending

63-65

26

Starter Homes

21

18

Apartments / Shopping Centers

Offices

Colleges

20

24

28

32

36

40

44

48

52

56

60

64

68

Age


Long term house prices vs inflation1
Long Term House Prices vs. Inflation Markowitz

Source: Robert J. Shiller, Irrational Exuberance, 2nd Edition, Princeton University Press, 2005.


Business development an s curve analysis

Long Term House Prices vs. Inflation Markowitz

Source: Robert J. Shiller, Irrational Exuberance, 2nd Edition, Princeton University Press, 2005.


Borrowing power of a typical home purchaser
Borrowing Power of a Typical MarkowitzHome Purchaser

Pre-Tax Income

Borrowing Power

2.8 times

Source: Amherst Securities


Average us home prices case shiller 20 city hpi jan 2000 aug 2011
Average US Home Prices MarkowitzCase-Shiller 20 City HPI: Jan 2000 – Aug 2011

Seasonally Adjusted In Thousands

Data Source: Standard & Poor’s Case-Shiller US 20-City Home Price Index, 2011


Average us home prices case shiller 10 city hpi jan 1994 july 2011
Average US Home Prices MarkowitzCase-Shiller 10 City HPI: Jan 1994 – July 2011

-33%

Seasonally Adjusted In Thousands

-55%

-65%

Data Source: Standard & Poor’s Case-Shiller US 10-City Home Price Index, 2011


Case shiller top metro areas percent decline from peak values
Case-Shiller Top Metro Areas MarkowitzPercent Decline from Peak Values

-54%

-52%

-48%


Case shiller top metro areas percent decline from peak values1
Case-Shiller Top Metro Areas MarkowitzPercent Decline from Peak Values

-54%

-52%

-48%

-36%

-34%


Case shiller top metro areas percent decline from peak values2
Case-Shiller Top Metro Areas MarkowitzPercent Decline from Peak Values

-54%

-52%

-48%

-36%

-34%

-11%

- 8%


New home sales january 1963 august 2011
New Home Sales MarkowitzJanuary 1963 – August 2011

Seasonally Adjusted In Thousands

Source: US Census Bureau


Nahb housing market index and single family starts
NAHB Housing Market Index and Single Family Starts Markowitz

Source: Calculated Risk Blog, 2011


Defaults and foreclosures 2005 2011
Defaults and Foreclosures Markowitz2005-2011

Source: Calculated Risk Blog


Foreclosure rate heat map
Foreclosure Rate Heat Map Markowitz

Source: RealtyTrac, 2011


Rising mortgage defaults
Rising Mortgage Defaults Markowitz

First Time Defaults

Inventory Overhang

Liquidations


Foreclosure sales stagnate
Foreclosure Sales Stagnate Markowitz

Source: Information Supplied by LPS Analytics, 2011


Home price to rent ratio 1983 2011 rent 1 in q1 1998
Home Price to Rent Ratio Markowitz1983-2011, rent = 1 in Q1 1998

Source: Calculated Risk Blog, 2011


Adults aged 25 34 living in their parents homes
Adults Aged 25-34 Living in Their Parents Homes Markowitz

Percent

Data Source: U.S. Census Bureau, 2011


Total delinquent percent vs unemployment rate
Total Delinquent Percent vs. Unemployment Rate Markowitz

Source: Information Supplied by LPS Analytics, 2011


Break
BREAK Markowitz



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