The Website
Remember that the Investment
Science web site is available. It is located at
http://www.investmentscience.com. You will find copies of
past issues of this Newsletter at the site, as well as additional
information about investment science projects and software tools.
Your comments on the site are welcome.
New
Projects
In addition to continuing work, we have carried
out some new interesting projects this year, including several
student projects done for local companies done in conjunction with
the Investment Practice course taught at Stanford by Robert
Luenberger. In addition, two students worked on a comprehensive
project for HP Labs, and we have carried out a very interesting new
area of investment science at Cadence Design Systems, Inc. The span
of application areas is increasing beyond project valuation and
management to design of pricing systems, study of business models,
development of R&D strategies, and enhancement of intellectual
property values. Sound financial theory coupled with strong
modeling and computation is a formula that has enormous
potential.
Short Course News
Short Course News Investment Science for
Industry was given at Stanford in April. We had a very good group
in attendance and very much enjoyed the interaction. The course
content continues to evolve, partly because of advances we make at
the university and partly because of the feedback and ideas that we
get from participants of the course. The course will next be given
this September 1415. Several past participants have referred
colleagues to the course, and this has become a good mechanism for
spreading the ideas of investment science. I hope you will consider
presenting the idea of the course to your colleagues. You may refer
them to the course though the web site
http://www.stanford.edu/~luen or to the
investmentscience.com web site mentioned above.
New
Dissertation
Mark Erickson obtained his
Ph. D. in Management Science and Engineering, at Stanford
University this past June. His Dissertation is titled, "Real and
Financial Options: Theory and Lattice Techniques." This work
makes a substantial contribution to both the theory and practical
application of investment science. He presented a comprehensive
theory for valuing options that are defined in terms of observable
quantities that do not have prices. For example, one might have an
option based on the weather or on the size of the market for a
certain product. He also presented an outstanding analysis of
various lattice methods for representing uncertain processes and
showed how to construct lattices that are both computationally
efficient and technically accurate. His work is an important piece
within the investment science framework.
Price
Cycles in Competitive CapitalIntensive
Industries By H.
Marius Holtan, Ph.D.
Overview
Severe earnings fluctuations, or cycles, coming about as a result
of large variations in the product's price, is a defining
characteristic of many industries. Prominent examples of such
industries are petrochemical, semiconductor, paper, and
construction. In fact, most if not all commodity industries
experience serious earnings fluctuations.
Capacity planning can be
difficult in a cyclical environment. In particular, while cycles
may at first glance appear to be periodic they also exhibit
strictly random behavior. This randomness, coupled with large
fluctuations, long lead times from initiating capacity construction
to final use, and large capital expenditures makes capacity
planning especially hard.
Capacity planning becomes
easier, however, if we understand the fundamental causes of
cyclicality. With this knowledge we can devise a capacity planning
strategy that takes advantage of good opportunities and minimizes
mistakes during especially difficult times. While uncertainty will
always influence the end result, a structured approach based on an
understanding of the fundamentals driving the industry should
significantly improve the odds.
Some often cited reasons for
cyclicality range from unavoidable cyclical fundamentals in the
industry to the presence of crazy eddies, i.e. irrational behavior
on either the supply or the demand side, to herd behavior, to
industry competition.
While we do not preclude the
presence of any of these reasons as a cause for industry cycles,
recent investment science research has shown that competition
stands as a significant contributor to severe industry
fluctuations. Moreover, companies that are aware of the effects of
competition and equipped with a capacity planning strategy that
incorporates competitive effects may expect to dominate their
industry in the long term.
The next section will
discuss the basis of the research. The discussion is kept at a
qualitative level. We then turn our attention to the competitive
advantage gained by using the competitive knowledgebased strategy
versus a deterministic net present value strategy.
Competition and Industry
Cycles The main motivation of
companies is to improve profitability. In a typical commodity
industry this means cutting costs/improving production efficiencies
or increasing production. Influencing the price is generally not an
option for companies in a commoditytype industry because in such
industries the price is set by the overall market demand and
supply.
Production volume can be
changed in either of two ways: buying other companies' capacity or
constructing more capacity. To understand the effect of competition
on cyclicality we focus on capacity construction, as buying other's
capacity does not alter industry supply.
We now describe a typical
scenario of supply decisions over time according to our industry
model.
Capacity construction occurs
at some point after demand has been rising and current and future
expected industry earnings are high. At this point the return on
investment of building a new production facility is sufficiently
high to cover the capital market's requirement of expected return
relative to the level of accompanying risk. As long as demand rises
more capacity is built. At some point though, because demand is
fundamentally uncertain, demand will suddenly change direction and
start decreasing. This will cause companies to stop initiating new
construction.
The result of the above
description is that the industry will experience first a boom and
then a bust. While demand is rising but before capacity under
construction becomes available, companies experience a seller's
market. Limited supply and ample demand generates high prices and
correspondingly high earnings. When new capacity becomes available
the price level flattens or perhaps decreases slightly, but as long
as demand grows profits remain high.
The boom will come to an
abrupt end, nonetheless, when demand shifts direction. Not only
does demand decline, supply will continue to increase for some time
as construction started prior to the downturn provides new
capacity. Both effects combine to put severe downward pressure on
the price. The result is that the industry will experience a
significant decrease in earnings and losses will occur for the less
efficient suppliers.
It is important to point out
that we do not assume any irrational behavior on the part of the
suppliers. Every supplier has the same objective, which is to
maximize the market value of their company. The outlook at the
point in time when a company is making its investment decision is
positive with an expected return that satisfies the capital
market's requirement of expected return relative to the level of
accompanying risk. It is just that investing in capacity is risky
and at some point the risk will negatively affect the
industry.
With regard to phenomena
such as irrational behavior and herd mentality occurring during a
boom interval, in a well functioning capital market it is not
likely that they will persist on a large scale because such markets
are quick to punish mistakes through means such as bankruptcy,
restructuring, or takeover.
In the above scenario we
defined the time when a company would invest in new capacity as the
time when a new investment would satisfy the capital market's
requirement of return versus accompanying risk. For this statement
to be useful, except for the odd CEO with an intuitive grasp of
market value, it is necessary to have a valuation model that
incorporates the competitive element. In fact, not even the CEO
with an intuitive grasp of market value can usually provide an
exact value. In contrast, a valuation model will quantify an
opportunity and therefore numerically verify the value of a good
opportunity and the savings for avoiding a bad
opportunity.
The basic idea of the
valuation model that emerges from our competitive industry research
is as follows: Suppose there were a futures market for the
industry's product. Suppose further we sold all future output of a
new plant in the futures market. Assuming that no other
uncertainties impacts the plant's cash flows, the value of
uncertainties impacts the plant's cash flows, the value of the
plant's new cash flow would be the net present value of the
discounted futures cash flow stream net of any production costs,
using the riskfree rate as the discount rate. Thus, the futures
price for delivery at some future point in time represents the
value as seen from the point of view of the capital markets of a
delivery of a unit of production at that point in time.
The above works nicely when
a futures market is available, in which case all necessary
information for valuation is publicly available. Our research
describes a method for calculating synthetic futures prices when no
futures market exists for a particular product.
There are two other
characteristics that are worth mentioning. First is the fear of
losing out by being preempted. That is the reason there always is
some company that invests in new capacity at the very moment the
value of the investment is sufficient to meet the capital market's
requirement. Companies know that their choice is to either seize
the moment or face the distinct possibility of being preempted by
another company's investment the next day.
Second, competitive
industries often operate in a price region where customers'
sensitivity to price fluctuations is small. A small change in
demand will result in a large change in price, causing both large
upward and downward fluctuations in earnings for small demand
variations.
Competitive Advantage of a
Competitive KnowledgeBased Strategy To compare the long term competitive advantage of
following a competitive knowledgebased strategy versus following a
strategy based on a deterministic net present value model we built
a model that simulates industry conditions over a 40 year time
horizon. The model works as follows: The industry is defined by a
supply side, consisting of a number of companies, and a demand
side. The total supply at a point in time is the sum of each
company's capacity. The demand side changes randomly at the
beginning of every month, the expected growth rate of demand being
positive. After demand has changed we derive the price of a unit of
production by equating supply over that month with demand during
the month. The earnings of a company for a particular month is
given by the product of its capacity and the price of a unit of
production reduced by the cost of producing that unit.
Each company can attempt to
increase its earnings by building more capacity. Building capacity,
however, is costly and there is a construction lag. To make
construction decisions each company relies on its own valuation
model. A valuation model can be used to value a new unit of
capacity or an existing unit of capacity. For the purposes of this
study, there are two types of valuation models and we partition the
companies into two groups according to which valuation model they
use. One group uses a deterministic net present value model. The
other group's valuation model is based on our research, which takes
into account competition and demand uncertainty when valuing
capacity.
Companies can also trade
capacity. We assume a trade occurs each time that the estimated
values of the two models differ by more than 10%. When this is the
case the group with the lower estimated value will sell 5% of its
capacity at the middle value point.
Each group's performance is
measured by repeatedly simulating industry conditions. At the end
of each run we collect for each group the overall level of
cumulative earnings and the overall level of capacity. We assume
that the group using the deterministic net present value model
initially owns 90% of the total capacity.
Figures 1 and 2 show the
results under two industry scenarios. In the first scenario the two
groups are not allowed to trade capacity while in the second
scenario the groups are allowed to trade. In both scenarios the
groups can build new capacity.
The columns represent
expected total return on capacity and earnings relative to initial
market share. Thus, under scenario 1 the expected capacity growth
of the group using the deterministic net present value model is
3.06 times relative to their initial capacity and expected
cumulative earnings invested at the risk free rate is 9 times their
initial market share. These results can be contrasted by the
knowledgebased strategy group where the expected capacity growth
is 23.64 times their initial capacity and expected cumulative
earnings invested at the risk free rate is 8.17 times their initial
market share.
Figure
1. Expected total returns on
capacity and earnings when no trading of capacity is allowed
between the two groups. DCF represents the supplier group using
deterministic net present value model and MF represents the
supplier group using the competitive knowledge based
strategy.
Figure 2.
Expected total returns on capacity and
earnings when trading of capacity is allowed between the two
groups. DCF represents the supplier group using deterministic net
present value model and MF represents the supplier group using the
competitive knowledge based strategy.
While the relative advantage
of using the competitive knowledgebased strategy is significant
when the two groups do not trade capacity, the performance is
especially impressive when trading is allowed. In the latter case
we expect the group following the competitive knowledge based
strategy to increase its market share from 10% to about 64% while
at the same time through timely trading capturing 76% of the
earnings generated by the industry.
Conclusion
Cycles is a phenomena that has impacted
certain industries for a long time. Our research shows that the
existence of cycles can be viewed as a natural characteristic of a
competitive economy. In this case, it is our view that behaving
optimally within a cyclical environment, managing risk and
aggressively pursuing new opportunities when they appear, will lead
to superior results compared to trying to control or influence the
cycles. The latter actions can potentially go against the natural
behavior in a competitive industry and can thus reduce the
beneficial aspects of economic competition.
The model emerging from the
cycle research also provides a collection of synthetic futures
prices. These futures prices can be used to calculate the value of
an existing plant and also on a potential investment in new
capacity. This valuation model can be used to generate a superior
strategy for new capacity investments and also for identifying good
opportunities for trading capacity with other industry
participants.
The long term effect of
adhering to the competitive based investment strategy was shown to
be significant, clearly outperforming the strategy based on an
application of the discounted cash flow model.
The overall principles
outlined here should apply generally and should therefore be useful
for providing a basis for constructing an investment strategy for
many cyclical industries. Nonetheless, a careful study of the
industry with the purpose of extending and calibrating the model to
a particular situation should be executed. The simulation results
suggest that the return of such a study would justify the
effort.
