From: John Conover <john@email.johncon.com>
Subject: Re: The Standard: Warning: Recession Ahead
Date: 6 May 2001 22:18:52 -0000
As a sidebar, VCs usually want an investment to run about 5 years,
then the company IPOs, and the VC makes lots of money-and so do the
employees, (sound like a Ponzi scheme? It is.)
What's the VC industry's success rate?
The industry numbers are about one in nine, (the remaining eight get
redeployed, reorganized, or re-something'ed into Chapter 7 and 11
which is a popular end game with the dot-coms these days.)
What's the probability of a company's strategy and marketplace lasting
at least 5 years = 60 months, without having a negative month where it
couldn't meet the payroll?
The deviation of the distribution of the GR of all companies at the
end of 5 years is about 1 / sqrt (60) = 0.13, or about 15.8% of the
companies will have done at least 87% of what they were supposed to
do. So, we need 1 / 0.87 standard deviations = 1.14, which corresponds
to 0.127, or about one in 8-or about a 10% error from the empirical
metrics published by the industry associations.
Call it about one in ten, which is the number I used, below, in the
statement:
... We would, also, expect about a 1 in 10 survival rate, (e.g.,
any company that has less than 10% market share, e.g., a 90%
chance of failure, won't make it,) and we are seeing those
numbers." ...
So, one probably shouldn't invest in companies with less than a 10%
market share, (or do business with them, either,) unless there is
compelling reason to do so, since only about 1 in 10 will survive at
least 5 years.
The one in ten number is consistent with what has been published about
the other industrial "bubbles" of the 20'th century, (specifically,
both the radio and television set industry, of the 20's and 50's, the
electronic game industry of the mid 70's, the CB radio industry of the
early 80s, and the software industry of the late 80's-and now the
dot-com industry.)
John
BTW, the number is consistent with the other "bubbles" in the distant
historical perspective, too, including tulips in 17'th century
Holland, and the South Sea "bubble" of the 18'th century. As a
generality, about 90% of the investors lost 90% of their investment,
and the remaining 10% increased their wealth by about 10X, on average,
in each of the "bubbles" throughout the last 5 centuries. Such things
are Ponzi, (or pyramid,) schemes, and executive management should know
better than to participate, and run companies like a grand industry
sanctioned Ponzi scheme, (that goes for the investors, too.)
Interestingly, according to MBA dogma, the executives in the dot-coms
did everything right. The MBA methodology of corporate organization is
not new, and it works well for what it was designed to do-efficiently
manage an industrial assembly line process through Taylor's
time-motion analysis. Extrapolating the methodology as a management
paradigm is a large leap of faith, however, and business schools have
to shoulder a large part of the blame for the dot-com "bubble" for not
including modern concepts into the curriculum.
But the young executives that operated the dot-com "bubble" have a lot
of historical company. None other than Sir Isaac Newton lost a
fortune in the South Seas "bubble" of 1720:
... Sir Isaac Newton, scientist, master of the mint, and a
certifiably (sic) rational man ... sold his 7,000 pounds of stock
in April for a profit of 100 percent. But something induced him to
reenter the market at the top, and he lost 20,000 pounds,
... [prompting him to say] ... "I can calculate the motions of the
heavenly bodies, but not the madness of people."
Re: Harvard Magazine,
http://www.harvard-magazine.com/issues/mj99/damnd.html, and search
forward for the word "Newton".
John Conover writes:
> Looking at the valuation of a well managed company, (i.e., one where
> management knows what its doing-knows what has to be done, and knows
> how to execute on it, optimally, e.g., grow 2X per year, for at least
> 10 years, before it starts its demise,) and if it starts with a
> million bucks of GR the first year, (a reasonable value,) then IPOs in
> year 5, (with a GR = 2^5 * 10^6 = 32 million-a reasonable number from
> the historical perspective,) then in year 10 it would have a GR of 2^5
> * 32^6 = one billion bucks.
>
> If the company was floated on an initial million bucks investment,
> (i.e., it was not profitable only in the first year,) it would be a
> thousand X increase in valuation over the decade, (assuming that the
> capitalization vs. GR remained constant, which was 3X in the 20'th
> century, averaged over all companies-I'm using a conservative 1X.)
> Compare that with the 100X the best dot-coms delivered, (and then
> didn't.)
>
> Now assume that management is less efficient. It grows the company at
> only 10 percent less than optimal, at 1.9X per year-everything else
> remaining the same. At the end of the decade, the company's GR would
> be 613 million, or its valuation/capitalization would be almost 40%
> less. About half, for a consistent 10% inefficiency in management.
>
> Suppose the management of the company putzed around and didn't execute
> on engaging the marketplace for a year, then ran optimally. At the end
> of the decade, the company's GR would be 512 billion bucks, or its
> valuation/capitalization would be about half, for a 10% delay in
> execution.
>
> So, roughly, every delay or inefficiency by management of 10%
> ultimately costs the company 50% of its valuation/capitalization.
>
> John
>
> BTW, its not a linear relationship. Another inefficiency/delay of 10%
> doesn't cost another half billion bucks again. It cuts it in about
> half again.
>
> Note that a delay of one year costs a 50% market share in a
> competitive environment, so the market would shake out, ultimately, at
> a 2:1 market share ratio between a well run company, and a not so well
> run one, who has a 30% market share. In other words, a loss of 20%
> market share would spell the ultimate demise as the cost for the
> management mistake. It wouldn't make it through the decade.
>
> John Conover writes:
> > While we are on the subject of the dot-coms, (which is an interesting
> > case study-it is/was capitalism and free-market'ism at its finest-no
> > barrier to entry, insignificant infrastructure requirements, ample
> > investment or access to capital, and a choice of thousands for
> > consumers-what more could anyone want?) and completely mismanaged by
> > our finest young executives with their recent MBAs in tote. (Although
> > entropic methodologies are taught as a core competency requirement in
> > all university financial curriculum, it is not taught in business
> > school.)
> >
> > As an example, what is the chance that a company with 1% market share
> > will ever replace an industry leader with 50%?
> >
> > The answer is 2%, (you just divide the two numbers-its the gambler's
> > ultimate ruin scenario-and if it is an entropic system, it has to be
> > that way.)
> >
> > What that means is that, as an investor, one has to expect returns of
> > at least 50X to justify investing in companies with a 1% market share,
> > no matter how good the concept or vision is.
> >
> > We would, also, expect about a 1 in 10 survival rate, (e.g., any
> > company that has less than 10% market share, e.g., a 90% chance of
> > failure, won't make it,) and we are seeing those numbers.
> >
> > What's the duration of time one would expect for such a company to
> > be in business?
> >
> > About 50 * (50 - 10) = 2,000 days, or about 7.9 years for 253 business
> > days per year. (And, we have seen that, too; many of the dot-coms that
> > are failing were started as early as 1994, or so.)
> >
> > That's why so many dot-coms are failing.
> >
> > It is called management mistakes.
> >
> > John
> >
> > BTW, compare this to a company with competent executive staff that put
> > the company in biz early in the industry's development, started with,
> > and maintained a 50% market share. Such a company would be expected to
> > last about 100 * (100 - 50) = 5,000 days = 19.7 years, (at 253
> > business days per year.) The empirical metrics on the history of US
> > commerce in the 20'th century says 22 about years, (i.e., about a 10%
> > error in the probability estimate.)
> >
> > Only GE survived the entire century, BTW.
> >
> > But nothing is eternal in entropic systems. What's the chance of a
> > company in a leadership position eventually failing?
> >
> > Its a virtual certainty. That's the way capitalism works.
> >
> > John Conover writes:
> > >
> > > As approximate numbers, (taken from the average of many industries on
> > > http://www.johncon.com/ndustrix/,) to exploit the lack of
> > > predictability in the US GDP and its constituent industrial markets,
> > > the short term predictability of a few months to a 70% accuracy means
> > > that about (2 * 0.7) - 1 = 40% of a company's assets should be at risk
> > > in the short term, in WIP, capital expenditures, R&D, etc.
> > >
> > > Note that it is a conceptual framework, and just says what has to be
> > > done-not how to do it. The implementational paradigm is evaluation of
> > > "what if we do?", and "what if we don't?" scenarios, with more
> > > attention paid to the latter, (since you get more for better
> > > mitigation of risk than picking winners.)
> > >
> > > In the long term, (i.e., more than a few months,) mitigate risk
> > > through product diversification, (no less than 8 product lines, 10
> > > being about right, 12 probably too many,) and shuffle investment
> > > capital around in the product lines to avoid leptokurtotic behavior in
> > > the corporate P&L, (i.e., as a first order approximation, the GR
> > > generated by each product line should be about equal as a management
> > > paradigm; as a better approximation, the GR generated by each product
> > > should be proportional to the average divided by the deviation of the
> > > marginal increments in the GR. Likewise for the investment of
> > > capital.) That, also, defines investment in new product areas. The
> > > long term plan is to put about 2% of the company at risk, per business
> > > day, (or, cumulative, about 40% per month,) which is consistent with
> > > the short term strategy.
> > >
> > > The numbers would support a corporate growth of slightly more than 2X
> > > a year, and would maximize growth, while at the same time, minimizing
> > > risk exposure.
> > >
> > > Whether such growth could be managed and executed in the long run is
> > > an entirely different issue. (Its tough.)
> > >
> > > John
> > >
> > > BTW, there are other solutions for the same numbers-but they aren't
> > > optimally maximal. It is possible to grow a company faster than 2X a
> > > year-for awhile. Its a "coffin corner" solution, that will exhibit
> > > high growth, followed by a crash, (no matter what growth metric is
> > > used.) About 2X is the maximum SUSTAINABLE growth with those numbers.
> > > (Does the dot-com industry ring a bell as a case in point? Many of
> > > those companies exhibited growth of 2X in a single month-for
> > > awhile. The amount of the company placed at risk was far larger than
> > > the optimal 40% per month-most of it in capital investment. Glory has
> > > its price.)
> > >
> > > John Conover writes:
> > > > Interestingly, the lack of predictability in the US GDP is
> > > > exploitable.
> > > >
> > > > As a mathematical expediency most corporate strategies are divided
> > > > into short term and long term, (short term being a few months, long
> > > > term being more.)
> > > >
> > > > For long term strategies, the size of the window of predictability is
> > > > regarded as zero-meaning that the US GDP is treated as an entropic
> > > > system that behaves randomly. Strategies are developed that fit into a
> > > > framework determined by the average, (the potential gain,) and the
> > > > standard deviation, (the risk,) of the marginal increments of the US
> > > > GDP or industrial market-usually using monthly or quarterly data.
> > > >
> > > > Short term predictability is an inefficiency, (at least in the sense
> > > > of the efficient market hypothesis,) and can be exploited by adjusting
> > > > operations to near term GDP/market anticipations faster than anyone
> > > > else, by using the like of JIT techniques to minimize WIP risk, etc.
> > > >
> > > > The potential advantage is quite significant.
> > > >
> > > > John
> > > >
> > > > BTW, what would happen if every company that contributes to the US GDP
> > > > did that? Not much, except that it would grow faster. The US GDP would
> > > > become entirely entropic, (i.e., unpredictable,) and would be
> > > > efficient, and fair. It would be stable, (in the sense that it could
> > > > exist like that, forever.) Further, if the average of the marginal
> > > > increments equaled the variance, it would be maximally optimal.
> > > >
> > > > Unfortunately, it is a politically inexpedient solution. Such concepts
> > > > as monetary/fiscal policy to affect consistency in full employment,
> > > > etc., (which has been the paradigm of the last seven decades in all
> > > > industrialized countries,) would have to be discarded. Perceived lack
> > > > of influence over economic issues is not an expedient political
> > > > platform.
> > > >
> > > > (BTW, full employment is not necessarily optimal. It has, in general,
> > > > an unsustainable cost. Maximal sustainable employment is when the
> > > > GDP's average of its marginal increments equals the variance. However,
> > > > maximal employment does not, necessarily, mean full employment.)
> > > >
> > > > John Conover writes:
> > > > >
> > > > > In case you are curious, the big US economic recessions since
> > > > > Independence happened in 1819, 1833, 1837, 1857, 1873, 1893,
> > > > > 1929, (using the GNP/GDP numbers, which are not necessarily
> > > > > coincident with the stock market numbers, as far as downturns
> > > > > go,) and, (possibly-we don't know yet,) 2000.
> > > > >
> > > > > Note that this is the first generation in US history that has not
> > > > > had to endure a famine/depression, (at least yet,) and our
> > > > > perspective does not include how ugly they really are.
> > > > >
> > > > > John
> > > > >
> > > > > BTW, the numbers are interesting. To make predictions-like in the
> > > > > attached-the US GDP must be a deterministic system. Finding a
> > > > > mechanism that gives zero-free paths representing those numbers is
> > > > > a formidable proposition. If that can't be done, then, predictions
> > > > > can not be made.
> > > > >
> > > > > In NLDS systems, (of which the US GDP is certainly one,) the
> > > > > influence of the past on the future deteriorates rapidly-meaning that
> > > > > a small window into the past can be used to predict a small window
> > > > > into the future, and that is the best that can be done. The size of
> > > > > the window for the US GDP, is, at best, a few months, to a 70%, or so,
> > > > > accuracy.
> > > > >
> > > > > Unfortunately, the prevailing wisdom is that fiscal/monetary policy
> > > > > can not be used to influence the fluctuations in the US GDP-which
> > > > > was the paradigm of the past seven decades, and has since been
> > > > > abandoned.
> > > > >
> > > > > http://www.thestandard.com/article/0,1902,24243,00.html
--
John Conover, john@email.johncon.com, http://www.johncon.com/