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## Business Development, An ‘S’ Curve Analysis

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**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**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**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**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**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**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**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**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**Source: NY Times**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 40 year generations Predictable consumer spending Workforce pressure on inflation Ebb & flow of interest rates**Simple Four Season Economic CycleTwo Forty-Year Generation**Boom/Bust Cycles Generation Spending Boom Stocks/ Economy Summer Spring Fall Winter**Simple Four Season Economic CycleEighty Years in Modern**Times Consumer Prices/ Inflation Generation Spending Boom Stocks/ Economy Summer Spring Fall Winter**How Average Are You?**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**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 AmericanFederal Reserve Survey of Consumer**Finance 2001 2004 2007 2009 Median Family Inc $42.2k $44.3k $50.2k $49.7k College Degree 34.0% 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 Source: Employee Benefit Research Institute, HS Dent Research**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 2011 Source: Employee Benefit Research Institute, HS Dent Research**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**The Normal Distributionaka, 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**The Normal Distributionaka, 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 Distributionaka, 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”**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”) 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**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 ReturnsNormal 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 ChangesDJIA 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**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 AboutMarket 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.**The Human Model of Forecasting**“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**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.**Stock Returns1966-1970**Source: Ibbotson SBBI, Large Company Stocks: Total Returns**Stock Returns1996-2000**Source: Ibbotson SBBI, Large Company Stocks: Total Returns**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