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Session3_demoschool_SW.pptx

Published Jun 5, 2013 in Business & Management
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Presentation Slides & Transcript

Presentation Slides & Transcript

Business Development,
An ‘S’ Curve Analysis

Percent Adoption
The S-Curve

The Industry Life Cycle
Growth Boom
Shake-out
Maturity
Boom
Innovation
10%
50%
90%

Percent Adoption
The S-Curve
1900
1907
1914
1921
1928
1935
1942
Cars only for the Rich
Model T Design
Assembly Line
Installment Financing
90% Urban Adoption

Percent Adoption
Mobile Phone S-Curve
2008
2011
2005
2003
1996
2014
2001
1994
1991
1984
1997
1999
1994

Percent Adoption
Smart Phone S-Curve
2009
2007
2001
2010
2011
2008
2006
2016
2012
2008

Percent Adoption
Internet S-Curve
2000
1998
1991
2008
2011
2003
1996
1992
2002
1994
1995
1999
2005

Percent Adoption
Fixed-Line Broadband S-Curve
2006
2008
2011
2004
2002
2005
2000
2001
2007
2009

Percent Adoption
Digital Camera S-Curve
2001
1996
2008
2010
2004
1997
2002
2000
1999
2005
2006
2011
Source: Washington Post http://www.washingtonpost.com/wp-srv/special/business/a-gadgets-life/

Percent Adoption
High Definition TV S-Curve
2008
2009
2011
2006
2005
2007
2001
2010
2014
2008
2005

Percent Adoption
GPS Car Systems S-Curve
2008
2011
2010
2009
2014
2001
1994
2005
2003
2002
Source: Masterlink

Source: NY Times
S-Curves

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

The Business Cycle and Seasons of the Economy

Combining Generational Spending Trends, Inflation, and Interest Rates
40 year generations
Predictable consumer spending
Workforce pressure on inflation
Ebb & flow of interest rates

Spring
Summer
Fall
Winter
Stocks/ Economy
Generation Spending Boom
Simple Four Season Economic Cycle Two Forty-Year Generation Boom/Bust Cycles

Spring
Summer
Fall
Winter
Stocks/ Economy
Generation Spending Boom
Consumer Prices/ Inflation
Simple Four Season Economic Cycle Eighty Years in Modern Times

How Do We Know
Where We Are in the
Business Cycle?

Government data is used to estimate where we are

The information is typically based on how business was done in the 1930s

Over time, administrations tinker with government releases to get a “better picture” of what’s going on
Measuring the Business Cycle

How Average Are You?

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%)
Believe God exists (80%)

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

Legislative, Judicial, & Executive (20%)

Does NOT have a college degree (63%)

Take a bath or shower
(10.4 minute shower, daily)

Own stocks?( 50/50)


How Average Are You?

Selected “Average” Statistics
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
Data Source: Federal Reserve, Survey of Consumer Finances

Percent of Workers by Total
Amount of Retirement Savings 2011
Source: Employee Benefit Research Institute, Dent Research

Percent of Workers and Retirees by Total Amount of Retirement Savings 2011
Source: Employee Benefit Research Institute, Dent Research

Percent of Workers and Retirees by Total Amount of Retirement Savings 2011
Source: Employee Benefit Research Institute, HS Dent Research

Analyzing Data

Statistics And Other Math
Dispersion
Correlation Coefficients
Normal Distribution

Normal Distribution (Bell Curve)

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

Standard Deviations
Number of Observations
-4
-3
-2
0
2
3
4
-1
1
68% fall within +/- 1 standard deviation
Source: Dent Research
The Normal Distribution
aka, the “Bell Curve”

Standard Deviations
Number of Observations
-4
-3
-2
0
2
3
4
-1
1
95% fall within +/- 2 standard deviations
The Normal Distribution
aka, the “Bell Curve”
Source: Dent Research

Standard Deviations
Number of Observations
-4
-3
-2
0
2
3
4
-1
1
99% fall within +/- 3 standard deviations
The Normal Distribution
aka, the “Bell Curve”
Source: Dent Research

Assuming Returns
Are “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.

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
The Flaws of Return Estimates
(Why Returns Are Not Always “Normal”)

Volatility Clustering
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)
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
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
1987 Crash was 20 standard deviations past the mean – a statistical impossibility if returns were truly normal!
Back-to-back “long tail” days during 1929 Crash
1933 “impossible” one-day rally
Monster Bear Market Rally in July 2002

Daily Price Changes
DJIA 1998

Data Source: Bloomberg
Returns vary wildly over time
Roaring 20s and Depressionary 30s: High Volatility
1940s-60s: Low Volatility
1987 Crash: Unprecedented Volatility
Low Volatility
Tech Boom and Bust
Credit Crisis
Daily Price Changes
DJIA 1928-2011

Impossible Market Days
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
“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
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

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.

“We won’t have
recessions
anymore”
“It’s a soft landing”
“Things are so bad they
will never improve”
Source: Dent Research graphic interpretation of data in 2002 Schweser CFA Study Program, Chapter 15, pp 144-45.
The Human Model of Forecasting

Investing Is NEVER Satisfying
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
“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
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
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.

Source: Ibbotson SBBI, Large Company Stocks: Total Returns
Stock Returns
1966-1970

Source: Ibbotson SBBI, Large Company Stocks: Total Returns
Stock Returns
1996-2000

Understanding Risk
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

Spring
Summer
Fall
Winter
Stocks/ Economy
Generation Spending Boom
Consumer Prices/ Inflation
Simple Four Season Economic Cycle Eighty Years in Modern Times

Source: Advisory World, HS Dent
1970-2007
Efficient Frontier, 1970-2007

1970s
1970-2007
Efficient Frontier, 1970-2007
and 1970s
Source: Advisory World, HS Dent

1980s
1970s
1970-2007
Efficient Frontier, 1970-2007
and 1970s, 1980s
Source: Advisory World, HS Dent

1990s
1980s
1970s
1970-2007
Efficient Frontier, 1970-2007
and 1970s, 1980s, 1990s
Source: Advisory World, HS Dent

1990s
1980s
1970s
2000s
1970-2007
Article #5 MPT/Markowitz
Return (%)
Efficient Frontier, 1970-2007
and 1970s, 1980s, 1990s, 2000s
Source: Advisory World, HS Dent

Immigration & Migration

Immigration,
NOT JOB HUNTING
Historically, immigrants moved here to stay

Now, immigrants come for work, no intention of staying

Immigration to the
United States, 1820-2011
Data Source: U.S. Department of Homeland Security, 2012

Average Immigrants per Year
by Age 1945-2000
Source: US Census Bureau

Migration of Mexicans Into and Out of Mexico
Source: Pew Hispanic Center, 2011

White and Hispanic
Populations by Age
Source: US Census Bureau, 2000 Census

Migration Flows

Movers to a Different State by Age 2011
Data Source: US Census Bureau, 2012

Movers to Different State:
Young vs. Old, 2011
Data Source: US Census Bureau, 2012
5x
In Thousands

% of Americans Moving Each Year
1948-2011
Data Source: US Census Bureau, 2012

Movers Heat Map by County
2000-2010
Source: US Census Bureau, 2012

State Population Growth
Data Source: Census Bureau, 2012

Source: United Van Lines, via Unigroup, Inc.
United Van Lines
Migration Patterns 2007

Source: United Van Lines, via Unigroup, Inc.
United Van Lines
Migration Patterns 2009

Source: United Van Lines, via Unigroup, Inc., 2011
United Van Lines
Migration Patterns 2010

Source: United Van Lines, via Unigroup, Inc., 2012
United Van Lines
Migration Patterns 2012

Top 10 Outbound States
2012
Source: United Van Lines, via Unigroup, Inc., 2012

Top 5 Inbound States
2012
Source: United Van Lines, via Unigroup, Inc., 2013

Top 5 Inbound States
2012
Source: United Van Lines, via Unigroup, Inc., 2013

Most Common State-to-State Migration Flows

Lack of Mobility from Downturn
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 Spending Cycles
Spending
20
Age
24
28
32
36
40
44
48
52
56
60
64
68
Vacation / Retirement Homes
63-65
Resorts
54
46-50
Vacation Homes
37-42
Trade-Up Homes
29-33
Starter Homes
26
Apartments / Shopping Centers
21
Offices
18
Colleges

Source: Robert J. Shiller, Irrational Exuberance, 2nd Edition, Princeton University Press, 2005.
Long Term House Prices vs. Inflation

Source: Robert J. Shiller, Irrational Exuberance, 2nd Edition, Princeton University Press, 2005.
Long Term House Prices vs. Inflation

Long Term House Prices vs. Inflation
Source: Robert J. Shiller, Irrational Exuberance, 2nd Edition, Princeton University Press, 2005.

Source: Amherst Securities
Pre-Tax Income
Borrowing Power
2.8 times
Borrowing Power of a Typical
Home Purchaser

Average US Home Prices
Case-Shiller 20 City HPI: 2000-2013
Data Source: Standard & Poor’s Case-Shiller US 20-City Home Price Index, 2013

-55%
-65%
-33%
Average US Home Prices
Case- Shiller 10 City HPI: 1994-2012
Data Source: Standard & Poor’s Case-Shiller US 10-City Home Price Index, 2013

Defaults and Foreclosures
2005-2012
Source: Calculated Risk Blog, 2013

Foreclosure Rate Heat Map
Source: RealtyTrac, 2012

Delinquent and Foreclosure Rates, 1995-2013
Source: Information provided by LPS Applied Analytics, 2013
7.57%
Feb-12
4.13%
Feb-12
Foreclosure Starts Outnumber sales by a factor of 3:1

Foreclosure Starts vs. Sales
2005-2012
Source: Information provided by LPS Applied Analytics, 2013

Shadow Inventory, Housing
2000-2012
Data Source: Bloomberg, 2013

New Home Sales 1963- 2013
Data Source: Calculated Risk, US Census Bureau, 2013
Seasonally Adjusted In Thousands

New Home Completions
1968-2012
Data Source: St. Louis Federal Reserve, 2013

Percent of Labor Force in Construction, 1948-2012
Data Source: St. Louis Federal Reserve, 2012

Housing as a Percentage of GDP 1947-2012
Data Source: St. Louis Federal Reserve, 2012

Home Price to Rent Ratio
1983-2012
Source: Calculated Risk Blog, 2013

Adults Aged 25-34 Living in Multi-Generational Homes
Data Source: Pew Research, 2012

Effect of Recession on Household Composition, 1989-2012
Source: US Census Bureau, 2012

Change in Household Composition, 2007-2012 est.
Millions
29.8% Increase
Source: US Census Bureau, 2012