mpany is
providing and toward another that it is not (e.g., consumers preferring
electronic tablets made by Apple over PCs made by Dell), or changes in
the supply environment that causes a company’s capital base to be too large
(e.g., American car companies’ factories having too much capacity after the
U.S. car market saturated in the early 1980s), profit margins are not likely to
settle into an historical range but may materially increase (e.g., Apple, after
the release of iPads, iPhones, and so on) or decrease (e.g., Dell, after Apple’s
release of iPads, iPhones, and so on). Being able to correctly forecast this
type of secular shift is difficult, but can be extremely profitable.
In addition to these factors, there can be rapid drops and rises in
profitability caused by changes in the economic environment. These might
be company-specific events, such as a natural disaster destroying a supply
of inventory, or economy-wide conditions, such as loose monetary policy
encouraging consumers to use debt to make more purchases. While these
kind of factors can have a large short-term effect on profitability, averaged
over a longer time frame of a few years, most businesses’ profit margins end
up returning to a fairly dependable range.
102  •   The Intelligent Option Investor
Another case in which the normal profit range of a company may
change is through improvements in productivity. And although improve-
ments to productivity can take a long time to play out, they can be ex-
tremely important. The reason for this is that even if a company is in a
stage in which revenues do not grow very quickly, if profit margins are in-
creasing, profit that can flow to the owner(s) will grow at a faster rate than
revenues. Y ou can see this very clearly in the following table:
Year 0 1 2 3 4 5 6 7 8 9 10
Revenues
($)
1,234 1,271 1,309 1,348 1,389 1,431 1,473 1,518 1,563 1,610 1,658
Revenue
growth (%)
— 3 3 3 3 3 3 3 3 3 3
OCP ($)
4 432 445 497 485 514 544 560 637 625 708 746
OCP
margin (%)
35 35 38 36 37 38 38 42 40 44 45
OCP
growth
rate (%)
— 3 12 –2 6 6 3 14 –2 13 5
Even though revenues grew by a constant 3 percent per year over this
time, OCP margin (owner’s cash profit/revenues) increased from 35 to
45 percent, and the compound annual growth in OCP was nearly twice
that of revenue growth—at 6 percent.
Thinking back to the earlier discussion of the life cycle of a company,
recall that the rate at which a company’s cash flows grew was a very important
determinant of the value of the firm. The dynamic of a company with a rela-
tively slow-growing revenue line and an increasing profit margin is common.
A typical scenario is that a company whose revenues have been increasing
quickly may be more focused on meeting demand by any means possible rath-
er than in the most efficient way. As revenue growth slows, attention starts to
turn to increasing the efficiency of the production processes. As that efficiency
increases, so does the profit margin. As the profit margin increases, as long as
the revenue line has some positive growth, profit growth will be even faster.
This dynamic is worth keeping in mind when analyzing companies
and in the next section, where I discuss the next driver of company value—
investment level and efficacy.
The Four Drivers of Value  •  103
Investing Level and Efficacy
After our taxi company owner generated profits, she had to figure out if she
was going to invest those profits or spend them, and if she invested them,
she had to figure out what investment project was best. Listed companies
also face the same process and choices. Managers are responsible for in-
vesting owners’ cash profits with the aim of generating greater profits in
the future or for returning owners’ cash profits to the owners via dividends.
Because modern companies are so large and have so many shareholders,
most owners not only do not take an active role in shaping the investments of
their company, but they also don’t even realize that the investment process is
taking place.
5 In this environment, there are unfortunately many instances in
which the owners’ cash profits are invested badly or otherwise squandered on
wasteful projects. Ford paying top dollar to buy a decrepit Jaguar springs to
mind, as does Time Warner’s miserable purchase of AOL at the very peak of the
tech bubble. But these egregious examples are certainly just the tip of the ice-
berg. Companies routinely make implicit capital spending decisions by refus-
ing to close down an underperforming or obsolete business, thereby robbing
owners of cash flows that should have been theirs and instead filling the wallets
of consultants and employees.
6 Or the managers, realizing that their mature
core business throws off an enormous amount of cash, decide to spend some
of that cash on acquisitions of dubious economic benefit to the owners.
7 Luck-
ily, managers can always find an investment banker or two who are ready to
talk about the numerous “synergies” that will no doubt someday come to pass,
and too often boards and shareholders blithely accept the decisions and, once
made, do not demand an accounting of owner benefits as a result of the union.
Using an intelligent option investing framework, however, these here-
tofore hidden investment programs and their success or failure can be seen
much more clearly. First, we must see how much of the owners’ cash profits
for the company were spent on investing projects and forecast the amount that
will likely be invested in the future. The online valuation example provides an
actual look at precisely what financial line items go into this calculation. Right
now, it is enough to frame the term investments as any cash outflows on capital
projects that the company is making over and above the cash outflows neces-
sary to maintain the business as a going concern. Recall that in Chapter 4, I
called this spending expansionary cash flows because they are designed


vides an
actual look at precisely what financial line items go into this calculation. Right
now, it is enough to frame the term investments as any cash outflows on capital
projects that the company is making over and above the cash outflows neces-
sary to maintain the business as a going concern. Recall that in Chapter 4, I
called this spending expansionary cash flows because they are designed to
generate faster profit growth in the future.
104  •   The Intelligent Option Investor
The phrase faster profit growth should prompt the question, “Faster
than what?” It is at this point that we think back to the discussion of the
life cycle of a company. After a company has cleared its supply-side con-
straints, and after it has done all it can to increase profits in an environment
of demand-side constraints, it bumps up against structural constraints .
Structural constraints represent the long-run “speed limit” for the growth
of a firm. Because there is a speed limit for a firm in the long run, it is
logical that during the investment stage of a company’s life we compare the
investment-boosted growth with that structural speed limit.
The ultimate structural speed limit, as discussed earlier, is the nomi-
nal growth in U.S. gross domestic product (GDP). In this case, nominal
means the GDP growth that includes the effect of inflation as well as the
increase in economic activity. A graph of this nominal increase in GDP
from the postwar period follows:
3/1/1947
100
1,000
10,000
Nominal U.S. GDP (Billions of USD)
March 1997–September 2013
U.S. GDP (Logarithmic Scale)
3/1/1957 3/1/1967 3/1/1977 3/1/1987 3/1/1997 3/1/2007
Note that I have displayed this on a logarithmic axis to show how
consistent growth has been. The line representing U.S. nominal GDP
swings above or below the straight trend line but seems to swing back
toward the line eventually.
The Four Drivers of Value  •  105
Over this very long period, the nominal GDP growth in the United
States averaged just over 6 percent per year. If the investment projects
of a company are generally successful, the company will be able to
dependably grow its profits at a rate faster than this 6 percent (or so)
benchmark. The length of time it will be able to grow faster than this
benchmark will depend on various factors related to the competitive-
ness of the industry, the demand environment, and the investing skill
of its managers.
Seeing whether or not investments have been successful over time is
a simple matter of comparing OCP growth with nominal GDP . Let’s look at
a few actual examples. Here is a graph of my calculation of Walmart’s OCP
and OCP margin over the last 13 years:
2000 2005 2010
0.00%
0.50%
1.00%
1.50%
2.00%
2.50%
3.00%
3.50%
4.00%
4.50%
5.00%20,000
18,000
16,000
14,000
12,000
10,000
8,000
6,000
4,000
2,000
-
Estimated Owners’ Cash Proﬁt and OCP Margin for Walmart
Total Estimated OCP (LH) OCP Margin (RH)
As one might expect with such a large, mature firm, OCP margin
(shown on the right-hand axis) is very steady—barely breaking from the
3.5 to 4.5 percent range over the last 10 years. At the same time, its to-
tal OCP (shown on the left-hand axis) grew nicely as a result of increases
in revenues. Over the last seven years, Walmart has spent an average of
around 2 percent of its revenues on expansionary projects, implying that
106  •   The Intelligent Option Investor
cash flow left for shareholders amounted to about $0.02 (≈ $0.045 – $0.02)
on every dollar, on average. How efficacious were these investments?
In the graph below, any point above the “0 ppt” horizontal axis
indicates that Walmart’s year-over-year OCP growth has exceeded the
U.S. GDP by that amount, and vice versa. The year-over-year OCP growth
statistics are fairly noisy, bouncing back and forth above and below growth
in GDP; however, looking at a five-year compound annual growth rate
(CAGR) tells the same story as the linear trend line on the chart: Walmart’s
growth has slowed significantly and now looks to be close to that of the
economy at large on average. The rise in Walmart’s fiscal 2010 result (which
corresponds with calendar year 2009) is more a function of the company’s
revenues remaining resilient despite a U.S. recession than its growth out-
pacing a growing U.S. economy.
40 ppt
30 ppt
20 ppt
10 ppt
0 ppt
-10 ppt
-20 ppt
-30 ppt
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Growth in Walmart’s OCP Over (Below) Nominal GDP
Real Growth in OCP Linear (Real Growth in OCP)
To the credit of Walmart’s management, the company has spent in-
creasingly smaller proportions of revenues on expansionary projects over
the last few years, perhaps in recognition that its expansionary projects
were bringing in less bang for the buck over time.
In contrast, let’s take a look at a firm whose investments seem to
be adding considerable value—Oracle. First, let’s take a look at its OCP
margin:
The Four Drivers of Value  •  107
35%
40%
30%
25%
20%
15%
10%
5%
0%
Estimated OCP Margin for Oracle
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Other than the disastrous year of the tech bust in 2001, the company’s
OCP margin has held fairly steadily in the 30 percent range, but recently
it has started to move toward the 35 percent level. Over the last five years,
the company’s expansionary spending has averaged around 15 percent of
revenues per year, mainly through acquisitions. Because the expansionary
spending is governed by its acquisitions, its investments are not uniform,
and looking at the 2005–2008 period, the company was spending roughly
half its revenues on expansion. Over this time period, how has Oracle’s
OCP growth been vis-à-vis GDP? Let’s take a look:
50 ppt
60 ppt
40 ppt
30 ppt
20 ppt
10 ppt
0 ppt
-10 ppt
-20 ppt
Growth in Oracle’s OCP Above (Below) Nominal GDP
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Real Growth in OCP Linear (Real Growth in OCP)
108  •   The Intelligen

eBooks\Erik Kobayashi-Solomon - The Intelligent Option Investor- Applying Value Investing to the World of Options\Erik Kobayashi-Solomon - The Intelligent Option Investor- Applying Value Investing to the World of Options.pdf


he company was spending roughly
half its revenues on expansion. Over this time period, how has Oracle’s
OCP growth been vis-à-vis GDP? Let’s take a look:
50 ppt
60 ppt
40 ppt
30 ppt
20 ppt
10 ppt
0 ppt
-10 ppt
-20 ppt
Growth in Oracle’s OCP Above (Below) Nominal GDP
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Real Growth in OCP Linear (Real Growth in OCP)
108  •   The Intelligent Option Investor
In contrast with Walmart, through this lens, we see that Oracle’s
investments have generally allowed its OCP to grow at a much faster
rate than the economy at large (2010 was the year Oracle acquired Sun
Microsystems, and the OCP that year is an artifact of that acquisition—
I believe that its OCP that year was actually higher than stated here).
The beauty of this way of looking at companies is that the hidden or
implicit investments a company is making will show up in this as well. I
believe that, like many large companies, Walmart is finding that it must
spend money on expansion because it is investing ineffectually through its
internal business processes. One percent of revenues worth of expansionary
cash flows per year—roughly 25 percent of owners’ cash profits—is be-
ing spent so that the company can basically keep up with growth of the
economy at large.
This discussion deals with investment efficacy. Investments—
especially in the corporate environment, where one company completely
takes control of another and must integrate the acquiree into its own
business systems and culture—take time for results to be visible. As such,
it is easy to see why the table at the start of this section showed investment
efficacy affecting the medium-term results of the company—its growth
rates in particular.
Understanding the interaction among these three drivers—revenue
growth, profitability, and investing efficacy—allows an investor to take the
biggest step toward valuing a stock. Occasionally, though, one must take
what I call balance-sheet effects into consideration.
Balance-Sheet Effects
Let’s think back to our taxi-cab service. Let’s say that our owner decided
that after the first year, the investment prospects for her firm were so good
that she would buy two new cars. She thought that she could save money
by buying two low-mileage, off-lease cars rather than new ones.
Before putting the cars into service, she cleans each of the cars
thoroughly. While cleaning out the trunk of the first car, she finds a
tightly wrapped brown paper package. Curious, she opens the package
to find a pound of illegal drugs. She calls the police, who come to
The Four Drivers of Value  •  109
investigate. After looking over the situation, the police impound the
car, telling our taxi entrepreneur that they had no estimate for when it
would be returned.
The value of our taxi-cab company suddenly drops. Without the
use of the car, there is no way for it to generate revenues. However, while
revenues are not coming in, the company is still incurring costs (financing
and insurance costs, in particular), so the new car is actually lowering the
cash flow available to the owner. In the parlance of accounting experts, the
company has experienced a nonoperational contingency that has resulted
in a devaluation of one of its assets. This is a value-destroying balance-
sheet effect.
The taxi company owner, upset with the turn of events and her bad luck
in picking automobiles, grumbles as she gets back to work cleaning out the
second car. Cleaning between the back seats, she finds a valid lottery ticket
that was forgotten by the previous owner. Expecting a couple bucks worth
of winnings, she checks the number and is more than overjoyed to find that
she is holding the winning ticket for a $500,000 prize! The disappointment
from the police impounding her other car melts away as she realizes this
little slip of paper represents 125 years’ worth (+$500,000/$4,000) of her
company’s first-year OCP . This is one heck of a positive balance-sheet
effect.
The base assumption we make when we analyze a company is that all
the assets on the balance sheet are operating assets—that they are being
fully exploited to generate cash flows on behalf of owner(s). However, this
is sometimes not a valid assumption to make. Sometimes the true value of
assets can be hidden and remain hidden for some time.
On the hidden-asset side, one of the biggest jobs of the class of
institutional investors known as activist investors is to dig into the operating
details of a company to find assets that the company is not fully using or
is using so badly that the company is not able to create maximum cash
flows. Usually, the activist investor is looking to throw out the current
management team and replace it with people he or she thinks can better
use the assets. This is termed a hostile takeover , but it is important to
remember that the term hostile is only valid from the perspective of the
target’s management team. An insightful activist investor with patience,
110  •   The Intelligent Option Investor
foresight, and enough board seats to push through a change can be an
enormous boon to investors in the company.
In the same way that there are hidden assets, there also can be
hidden liabilities. Enron’s complex transactions with its “special-
purpose vehicles” are a vivid example of how dangerous hidden liabilities
can be. Enron managers found ways to effectively channel financial
transactions and obligations that they did not want on Enron’s own
books (namely, losses and liabilities) onto the books of off-shore entities.
Even though the off-shore entities were established and controlled by
Enron’s management, they were not consolidated into Enron’s own
financial statements, so the transactions and obligations effectively
disappeared from most investors’ view. Several investor groups started
putting two and two together and realized that the answer was less than
four. Eventually, when the spec


shore entities.
Even though the off-shore entities were established and controlled by
Enron’s management, they were not consolidated into Enron’s own
financial statements, so the transactions and obligations effectively
disappeared from most investors’ view. Several investor groups started
putting two and two together and realized that the answer was less than
four. Eventually, when the special-purpose vehicles became known by
the investment community, it was obvious that there was much less
equity for investors to own than they had thought previously, and the
stock price plummeted.
Whereas hidden assets can be thought of as a winning lottery ticket
stuck in between the seats of a used car, an old colleague of mine in the
hedge fund world used to call hidden liabilities “snakes sleeping in a
basket. ” Usually, it takes some time and familiarity with a company or
industry to understand where these lottery tickets or snakes may reside,
but most companies have them to a greater or lesser extent. Mostly, these
hidden items are not material to valuation and thus can be ignored, but
when they are not material, they can be truly powerful influences on
valuation.
It is impossible to explain precisely where to look for these hidden
items, but there are a few places one can typically start looking:
Lottery Tickets
1. Real estate carried at historical cost
2. Intellectual property (e.g., patents, copyrighted material, etc.)
3. Government connections (not as important in developed markets
but could be vitally important in certain emerging markets)
4. Overfunded pensions
The Four Drivers of Value  •  111
Snakes
1. Latent product/accident liability claims (e.g., asbestos, pollution
remediation, etc.)
2. Manager malfeasance (e.g., price fixing, Foreign Corrupt Practices
Act noncompliance, etc.)
3. Underfunded pensions
4. Off-balance-sheet corruption
5. Fraud
It’s usually hard to find these, but if you do, you should try to make an
assumption about the best- and worst-case financial impacts of these items
and simply tack that onto whatever cash-flow projections you have made.
Tying It All Together
Throughout our analysis of a company’s valuation drivers, our focus as
investors should always be to estimate the free cash flow to owners that a
firm will likely generate.
In the short-term, FCFO is driven by how fast revenues are growing,
how efficiently the company is converting those revenues to profits, and
how much of the profits the firm is spending on expansionary projects.
In the medium-term, FCFO is driven by how effective the investments
the firm made in the preceding period are likely to be.
In the long-term FCFO is driven by structural constraints because a
firm cannot grow faster than the economy at large.
Each driver has both best- and worst-case projections, so pooling all
the best-case projections into a best-case FCFO scenario and all the worst-
case projections into a worst-case FCFO scenario gives us an idea of the
most and least cash flow that the firm will generate for us in the future
(you can see an example of this on the Intelligent Option Investor website).
Discounting those FCFO scenarios generates a present value range for the
company. If we can find any balance-sheet effects, we add or deduct those
effects from the value found from discounting the FCFO scenarios. This is
the final valuation range of the company that we can compare to the market
price of the stock. When the valuation range of a company and the price of
a stock differ by a great amount, we have an opportunity to invest profitably.
112  •   The Intelligent Option Investor
Advanced Building Corp. (ABC)
5/18/2012 5/20/2013 249 499 749 999
Worst Case, 45
Best Case, 70
80
60
40
20
-
Date/Day Count
Stock Price
These are the general principles of intelligent investing, but again, the
reader is invited to work through the detailed valuation example on the IOI
website to help bring these general principles to life.
The preceding chapter on understanding the golden rule of valuation
and this chapter on recognizing the valuation drivers are a great step to-
ward building what Warren Buffett called a “sound framework for making
[investment] decisions. ”
The one thing that I hope you have realized while reading this and the
preceding chapter is what a simple and commonsense process valuation is.
It is worth asking why—if rational valuation is such a simple process—do
people generally have such a very difficult time investing and run into so
many pitfalls.
To understand this, I now turn to an explanation of the behavioral
biases and structural impediments that trip investors up and make sugges-
tions on how to avoid them.
113
Chapter 6
understanding
and overcoming
investing pitfalls
You have seen that valuation is not a difficult thing. It requires
understanding of a few key relationships, but it is basically a straightforward
process most of the time.
Why then, do so many investors have such a hard time doing it well?
The main reason, I am sorry to say, is our nature as human beings and
the weaknesses of our nature. This chapter discusses two facets of that—be-
havioral biases and structural impediments. The first facet—behavioral bi-
ases—involves how we as human beings try to figure out complex things and
get caught in the process of doing so. The second facet—structural impedi-
ments—speaks about how we investors tend to buy—lock, stock, and bar-
rel—into a game designed only for us to lose, whereas the winners’ kids go to
$50,000-a-year prep schools followed by a four-year tour of the Ivy Leagues.
There is hope. Don’t despair. The first step to not falling for these
pitfalls is simply to understand that they exist.
Obviously, being an intelligent option investor means investing
intelligently, minimizing—as much as possible—the effects of irrational and
emotional decision making. This chapter is designed to help you do just that.
Jargon introduced in this chapter


our of the Ivy Leagues.
There is hope. Don’t despair. The first step to not falling for these
pitfalls is simply to understand that they exist.
Obviously, being an intelligent option investor means investing
intelligently, minimizing—as much as possible—the effects of irrational and
emotional decision making. This chapter is designed to help you do just that.
Jargon introduced in this chapter is as follows:
X-system Risk neutral
Risk seeking Risk averse
C-seeking Prospect theory
114  •   The Intelligent Option Investor
Behavioral Biases
Human intelligence evolved in an environment that is very different from
the one in which we live today. Gone is the necessity to hunt and gather,
protect ourselves from predators, and fashion our own shelter. In con-
trast, in our modern lives, we are safe from most environmental factors
but are instead confronted with massive amounts of data. Groundbreak-
ing photographer Rick Smolan, in his book, The Human Face of Big Data
(Sausalito, CA: Against All Odds Productions, 2012), contends that a mod-
ern person processes more information in a single day than the typical
sixteenth-century person processed in an entire lifetime. I am not sure if
there is a scientific way of proving such a contention, but it does seem at
least plausible.
In terms of investing, the mismatch between how our mental processes
have evolved and the tasks that we expect them to carry out becomes an
issue because, by and large, we are still using mental strategies that served
our Stone Age ancestors well but that serve us investing denizens of the
“Information Age” much less well.
The study of human bias in economic decision making is a big topic—
called behavioral economics or behavioral finance—and it is not possible to
cover it fully here. I will give a few examples here and suggest how you might
work to counteract theses biases in your intelligent investing, but you are
encouraged to study up on these issues themselves. It is a fascinating topic,
and the more you learn, the more you will realize how much behavioral
biases affect everyone’s decision-making processes.
Here I will discuss three issues:
1. Love of symmetry
2. Confidence and overconfidence
3. Humans’ kinky perception of risk
Love of Symmetry
Here is the chart of an asset that has had a smart 8.3 percent return in just
50 trading days. Is this thing likely to keep going up from here or fall back
down after its relatively rapid rise?
Understanding and Overcoming Investing Pitfalls •  115
38.50
38.00
37.50
37.00
36.50
36.00
35.50
35.00
34.50
34.00
33.50
16 11 16 21 26 31 36 41 46 51
Trading Days
Price per Share
Y ou would be correct if you answered, “Neither of the above. ” This is a
chart I created using the random-number-generator function in Excel. Be-
cause Excel recalculates the values on the sheet any time a change is made,
I could not get the next value in this series—the series changed as soon as
I asked Excel to calculate the next day’s return.
I have presented similar series to various groups, including groups
of traders. It is fascinating to hear the predictions regarding this series and
the reasoning behind the predictions. Usually, the crowd settles on an an-
swer that is acceptable to most people (e.g., “It will probably go higher, but
I’ d set a stop loss at $37.25 and aggressively buy if it goes down to $35.50”).
1
Why do so many people see patterns where no patterns exist? Why
do so many people put their faith in so-called technical analysis (which
is neither technical nor analysis) even though they are just as likely to be
successful consulting a Magic 8 Ball for investment advice?
To understand this, we need to realize that there are two separate
human mental processes for analyzing and solving problems: X-system and
C-system.
The X-system is in control of refleXive thought processes, and these
processes take place in some very primitive areas of the brain. This system
116  •   The Intelligent Option Investor
is extremely good at perceiving patterns and symmetry and can operate
very quickly to solve common problems. It is also capable of multitask-
ing. The C-system is in control of refleCtive thought processes, and these
processes take place in parts of the brain associated with higher reasoning.
This system works slowly to solve complex problems about which we have
limited experience. Its ability to multitask is limited.
For an illustration of these two systems, consider this problem: you
are walking in a house and are confronted with the following object:
Y our X-system recognizes this object as a door, quickly retrieves information
about how to use objects of this type from your memory, and directs you
Understanding and Overcoming Investing Pitfalls •  117
to rotate the metal handle downward to open the door and move into the
next room. Y ou can solve this problem extremely quickly, with no conscious
thought, even while you are doing something else, like speaking with a friend.
Now let’s say that when you grab the handle and rotate it, rather than
the door opening, the handle comes off in your hand. What do you do? Y our
mind automatically switches from X-system mode to C-system mode, and
you begin to solve the problem of the closed door in a logical, systematic way.
Y ou would stop talking to your friend, push the door to see if it will open with-
out the latch, bend down to take a look at the handle mechanism, and so on.
Throughout the process of attempting to solve this problem, you
may switch back and forth between X-system and C-system processing,
using your C-system as the controller and the X-system to check on prior
solutions to similar problems you may have faced.
With this example, you likely have a good intuitive feel for the char-
acteristics of the X- and C-systems, but for completeness’s sake, here is a
grid describing them:
X-System C-System
Reflexive Reflective
Good for recognizing symmetry and
patterns and for solving commonly


ng your C-system as the controller and the X-system to check on prior
solutions to similar problems you may have faced.
With this example, you likely have a good intuitive feel for the char-
acteristics of the X- and C-systems, but for completeness’s sake, here is a
grid describing them:
X-System C-System
Reflexive Reflective
Good for recognizing symmetry and
patterns and for solving commonly
experienced problems
Good for analyzing complex, multistep
problems outside previous experience
Operates quickly Operates slowly
Separate processes do not interfere with
one another, allowing for multitasking
Separate processes do interfere with one
another, making multitasking difficult
or impossible
Uses amygdala, basal ganglia, and
temporal cortex—the areas of the brain
associated with “fight or flight,” reward
training, identification of objects, and
behavior
Uses anterior cingulate cortex, prefrontal
cortex, medial temporal lobe, including
the hippocampus—the areas of the
brain associated with higher-order
functions such as planning and control
Didactic style: analogy Didactic style: mathematical proof
Psychologically comfortable and easy Psychologically uncomfortable and difficult
The X-system is more psychologically comfortable to us (or to most of us)
because it is the part of the brain we as a species have been using during most of
our evolutionary history. The pattern-recognition portion of our brain is highly
118  •   The Intelligent Option Investor
developed—so much so that even though computers such as Deep Blue can go
toe to toe with chess grand masters, no computer has yet been designed that
would be able to recognize a fork that is rotated 30 percent off center or a series
of random items placed in front of it. Even the greatest computer “mind” can-
not carry out a pattern-recognition task that is simple even for human infants.
In investing, humans tend to lean on this X-system pattern recognition
and try to use shortcuts to analysis based on it. We have mental models for cer-
tain kinds of companies, certain kinds of information, and certain situations,
and we attempt to escape uncomfortable, analytical C-system processing by
allowing our X-system to match current conditions with those mental models.
When presented with a stimulus (e.g., bad quarterly earnings numbers),
our tendency is to reflexively react rather than to analyze the information.
This tendency is made more visceral because the X-system that is processing
this stimulus is tied into the “fight or flight” response. We would rather act
first, even if acting proves to be a detriment rather than a benefit.
This is a phenomenally difficult—I think impossible—bias to complete-
ly overcome. Although this bias can be extremely detrimental to us and our
investing process, our highly developed X-system is also incredibly useful to
us in our daily lives—allowing us to navigate the difficult problems present-
ed by doors, car operation, and so on. I discuss how to recognize and work
around X-system biases, how to use the X-system when it is useful to do so,
and how to frame investment decisions using C-system processes in the valu-
ation example of Oracle that can be found on the Intelligent Option Investor
website. For now, let’s look at another behavioral bias—overconfidence.
Confidence and Overconfidence
Scientific research has shown that humans do not feel comfortable with
C-system-style analysis and tend to doubt the results of these processes. As men-
tioned earlier, C-system processes do not seem intuitive and certainly do not jibe
with the satisfying off-the-hip decision making that seems to be prized culturally.
In what may seem like a counterintuitive reaction to this feeling of
discomfort with C-system processes, you often find analysts and investors
attempting to collect every scrap and shred of detail regarding a company’s
operations before making an investment decision. This phenomenon may
have something to do not only with a certain discomfort with C-system
Understanding and Overcoming Investing Pitfalls •  119
processes but also with a natural human discomfort with the unknown. All
investments are made in an environment of uncertainty, and uncertainty
is an unsettling psychological state for humans to find themselves in.
To ameliorate the discomfort from uncertainty, people have a tendency
to attempt to gain control of the uncontrollable by not leaving any stone
unturned in their analyses.
This may seem sensible, but in fact, studies have shown that more
information does not help you to make better decisions—just the opposite,
in fact. The first study showing this bias was done by a psychologist at the
University of Oregon named Paul Slovic, who studied the accuracy and con-
fidence of professional horserace handicappers.
2 Similar studies have been
performed on other groups—medical doctors and stock brokers among
them—and the results from subsequent studies have been very similar.
Professor Slovic gave professional handicappers varying amounts of in-
formation about horses running in a series of races and then asked them to
make a prediction about the first-place finisher in each race. The handicappers
were then asked to assess the confidence they had in their predictions. Slovic
had the actual race results and compared the professionals’ confidence with
their actual accuracy. The results can be represented graphically as follows:
30%
20%
10%
0%
51 02 04 0
Number of Items of Information
Accuracy vs. Confidence of Professional Handicappers
Confidence and Accuracy
(Accuracy measured by correct first-place selections)
AccuracyC onfidence
120  •   The Intelligent Option Investor
This is an incredible graph. The horizontal line represents the accuracy
of the expert predictions. The dotted line represents the confidence of the
experts depending on the amount of information they had.
The fact that the predictive efficacy line remains horizontal and the
confidenc


curacy measured by correct first-place selections)
AccuracyC onfidence
120  •   The Intelligent Option Investor
This is an incredible graph. The horizontal line represents the accuracy
of the expert predictions. The dotted line represents the confidence of the
experts depending on the amount of information they had.
The fact that the predictive efficacy line remains horizontal and the
confidence line increases so sharply indicates an interesting and, think-
ing about it, frightening facet of human behavior. Namely, even though
the predictions made by the experts who had the most data were no
more accurate in reality than those of their colleagues who had limit-
ed data, the ones with access to more and more data became more and
more confident, to the extent that they were massively overconfident.
Accuracy remains just under 20 percent, but confidence goes up to
30 percent—a 10 percentage point difference in perception (confidence)
versus reality (accuracy)!
This behavioral bias has two large negative effects on investors. First
is a tendency to spend too much time looking at too many nonmaterial
minutiae until finally one cannot come to a decision regarding whether or
not to invest—or, as it is colloquially known, analysis paralysis.
I think of the attempt to gather a huge amount of increasingly detailed
information about an investment prospect as a sort of cosmic bargaining.
The analyst or investor who spends hundreds of hours looking at very de-
tailed information not material to the valuation is doing something akin to
making a burnt offering of old. The analyst or investor is, in some sense,
making a prayer to the market gods: “I will sacrifice a lot of time and
mental effort learning about this company. Please reward me with positive
returns this year. ”
In the attempt to bargain with the great unseen hand of the mar -
ket, an analyst spends more and more time collecting increasingly less and
less important information about the potential investment until the cost
of collecting the extra information greatly outweighs the benefit of having
gathered it. The big problem with very detailed analyses is that the closer
one looks at a given problem, the more involved that problem becomes.
Every fact has some supporting details, and each supporting detail has a
few scenarios that may be associated with it. To do a really thorough job,
you must look at each scenario in turn. Ah! But these scenarios turn out to
be interrelated, so you must think about not only first-order changes in the
scenarios but also secondary and tertiary ones as well. Soon the analyst or
Understanding and Overcoming Investing Pitfalls •  121
investor’s spreadsheet model winds up being 45 tabs deep, and it still seems
like there is more work that needs to be done before a decision can be
made (“Where were those numbers regarding the depreciation of fixed as-
sets at the Malaysian subbranch?! How can I invest if I don’t know that?!”).
At this point, the analysis has become thoroughly paralyzed, and frequently
the investor will decide (after putting in all that hard work) just to drop
the whole thing because he or she “can’t get his or her head around” the
valuation.
Another cost to gathering a great amount of detailed information is
more subtle but no less dangerous. Let’s say that the analyst has worked
through all those secondary and tertiary scenarios and decides that the
firm in question is undervalued. The company is trading for $X and is
worth “$Y at a minimum. ” What is the analyst’s confidence level in that
$Y valuation? If the scientific studies I mentioned earlier hold true, the
analyst is 50 percent more confident than the position warrants. This is an
unhealthy dose of overconfidence.
The investor hits the “Buy” button and hopes for the best. However,
after a few quarters, some of the operational metrics at the firm begin to
falter. The Capex project that was forecast to take 5 percent of sales in year
one ends up taking closer to 9 percent. Sales are a bit lower than expected,
and costs are a bit higher. But the investor has thought about all these pos-
sibilities and is still very confident in the valuation; these discrepancies
are thus looked at like anomalies that will soon be corrected with another
quarter or two of results. The situation can drag on for an extended time
until suddenly the investor is confronted with the possibility that the firm
is running out of cash, its new product line has failed, or whatever. The in-
vestor, once so confident, now has to face the unpleasant task of realizing a
loss (why he or she may not want to realize a loss is discussed in the section
“Humans’ Kinky Perception of Risk” later).
“Love is blind. ” Unfortunately, overconfidence in an investment opin-
ion can make one just as blind as love.
I believe that two facets of intelligent option investing can help to
ameliorate these biases. First, recall that there are at most four—and most
often only three—drivers determining company valuation. While you are
reading about a company and analyzing its value, it is wise to constantly
ask yourself two questions:
122  •   The Intelligent Option Investor
1. Is what I’m analyzing related to one of the drivers of company value?
2. Is what I’m analyzing material to the valuation?
Sure, there is some sort of satisfaction in knowing everything there
is to know about coal-processing technology or oil reservoir structure and
engineering, but recognize that this satisfaction is purely personal and is
not going to make a bit of difference to the valuation. Understanding these
kinds of technical details might help a tiny bit in understanding competi-
tive dynamics in an industry, but the cost of learning them almost always
exceeds the benefit from the knowledge. For any technical points you are
trying to learn about as a layperson, there are likely two armies of engi-
neers, specifically trained in that field, arguing with one


ce to the valuation. Understanding these
kinds of technical details might help a tiny bit in understanding competi-
tive dynamics in an industry, but the cost of learning them almost always
exceeds the benefit from the knowledge. For any technical points you are
trying to learn about as a layperson, there are likely two armies of engi-
neers, specifically trained in that field, arguing with one another about
whatever point you are learning about. No matter how large your band-
width is, it is not likely that you will be able to make a more informed deci-
sion than those people. And if the final result is, “Company A will likely
be able to produce coal at a slightly cheaper cost than Company B because
of the geology where Company A has its mines, ” this is a fact that can be
reasonably ensured by a few minutes on Wikipedia rather than by checking
out books from the local university’s engineering library.
Second, the online valuation example shows how you can create
rational valuation ranges for a company, and I believe that those ranges
can be very helpful. Estimating valuation ranges rather than tying them-
selves to point estimates of a specific stock value can help investors to re-
main more objective about information coming in and more observant of
changing conditions. For example, if an investor sees one group of valua-
tion ranges clustered near $30 and one group clustered near $50, the inves-
tor can objectively assess operational data coming in over time and decide
which set of projected economic results the actual results will match. The
investor may have thought the economic results underlying the $50 cluster
were more likely, but as time goes on, he or she may see that the results
leading to the $30 cluster are closer to the truth. In this case, the investor
can be confident and happy about making accurate projections (because
the investor projected both the $30 level and the $50 level), even if he or
she is not particularly pleased with the investment outcome. This may be
the psychological slack required to combat the last behavioral bias we will
discuss—humans’ kinky perception of risk.
Understanding and Overcoming Investing Pitfalls •  123
Humans’ Kinky Perception of Risk
Take a look at the following questions: First question: you have a choice
between playing two games with the following monetary payoffs. Which
game would you play?
• Game 1: 75 percent chance of winning $6,000 and a 25 percent
chance of winning $0
• Game 2: 100 percent certainty of winning $4,000
Make a note of your choice. Second question: you have a choice between
playing two games with the following monetary payoffs. Which game
would you play?
• Game 3: 75 percent chance of losing $6,000 and a 25 percent chance
of losing $0.
• Game 4: 100 percent certainty of losing $4,000
What was your answer to this question?
Mathematically, you should choose to play games 1 and 4—these
are the rational choices. Most people irrationally would choose to play
games 2 and 3. The expected payout of game 1 = 75 percent × $6,000 +
0 = $4,500. As such, game 1’s outcome generates a higher expected payoff
than game 2. If you chose game 2 in this instance, it would indicate that
you are risk averse.
Reversing the conditions of the games to generate losses instead of
profits, you can see that game 3 yields an expected loss ($4,500) that is
greater than the expected loss of game 4 ($4,000). If you chose to play
game 3 over game 4, this would indicate that you are risk seeking rather
than risk averse.
Psychologists Amos Tversky and Daniel Kahnemann—two research-
ers who began the systematic study of behavioral biases—found that peo-
ple tend to be risk averse with respect to gains and risk seeking with respect
to losses and have coined the term prospect theory to describe this ten-
dency.
3 To understand risk aversion and risk seeking, let’s look at a simple
betting example.
Y ou offer a test subject a choice of either receiving a certain payment
of a certain amount or receiving an amount based on the result of a fair
124  •   The Intelligent Option Investor
bet such as a coin toss. If the coin comes up heads, the subject wins $100;
if it comes up tails, the subject walks away with no payment. The expected
payoff from the fair bet from a mathematical perspective is
$100 × 50% + $0 × 50% = $50
Economists describe risk preferences for individuals on the basis of
the fixed payment the individual would accept in order not to subject the
payout to a risky outcome. The three risk preferences are
• Risk neutral
• Risk averse
• Risk seeking
The risk-neutral investor is completely rational. The mathematical expected
payoff is $50, so the risk-neutral approach is not to accept any guaranteed
payment other than $50 in lieu of making the bet. If you were to diagram
the value the rational risk-neutral investor would assign to the expected
value of a risky outcome (using what economists call a utility curve ), you
would get the following:
0
0
Expected Value of a Risky Outcome
Risk-Neutral Utility Function
Value Placed on a Safe Outcome
Because $50 is not a great deal of money to some people, they can and
do remain risk neutral at this monetary level. Increase the potential payout
Understanding and Overcoming Investing Pitfalls •  125
to $1 million, and I guarantee that people will most happily demonstrate
risk aversion.
Risk aversion is demonstrated by someone who would be willing to
accept a guaranteed amount of less than the mathematically calculated ex-
pected payout in order to avoid putting the total payout at risk. For exam-
ple, if you would prefer to accept a sure $45 instead of a 50 percent chance
of winning $100, you are risk averse. The utility curve for a risk-averse
investor would be represented like this:
0
0
Expected Value of a Risky Outcome
Risk-Averse Utility Function
Value Placed on a Safe Outcome
Most mentally healthy people with relatively low blood-alcohol levels
are ri


otal payout at risk. For exam-
ple, if you would prefer to accept a sure $45 instead of a 50 percent chance
of winning $100, you are risk averse. The utility curve for a risk-averse
investor would be represented like this:
0
0
Expected Value of a Risky Outcome
Risk-Averse Utility Function
Value Placed on a Safe Outcome
Most mentally healthy people with relatively low blood-alcohol levels
are risk averse to a greater or lesser extent. As the amount in question
becomes material (however the person in question defines materiality), the
tendency toward risk aversion becomes much stronger.
Risk-seeking behavior is seen in gambling addicts and people with
high enough blood-alcohol levels that they should not be operating
heavy machinery. It is, of course, the converse of risk aversion: a risk
seeker requires a higher guaranteed payment than the mathematically
expected payout in order to forgo the bet. For instance, a risk seeker
would not want to stop betting unless he or she was offered $60 or more
for an expected-value bet of $50. The utility curve for a risk-seeking
investor looks like this:
126  •   The Intelligent Option Investor
0
0
Expected Value of a Risky Outcome
Risk-Seeking Utility Function
Value Placed on a Safe Outcome
Risk seeking may seem implausible for anyone whose problems are not the
feature of a daytime psychology talk show, but as you will see, each and
every person reading this now likely displays risk seeking many times in
an investing career.
If you read an Economics 101 textbook, you will learn that peo-
ple are either risk neutral (professional economists always try hard to
show that they are risk neutral because they generally pride themselves
on being rational), risk averse, or risk seeking. In fact, we all display
each of these profiles at different times depending on the situation.
The unfortunate fact, discovered by Tversky and Kahnemann, is that
humans tend to display the least helpful of each profile in different
situations.
When we are winning, we tend to be risk averse. We have made
20 percent on an investment in a short time, and our tendency is to “take
our money off the table” and realize our gains. The thing we fail to realize
when we feel the pride and satisfaction of hitting the “Sell” button is that at
the moment we close the position, our money is again sitting idle, and we
are faced with the prospect of having to find another risky investment to
replace the one we just closed.
Conversely, when we are losing, we tend to be risk seeking. For
example, let’s say that we have lost 60 percent on an investment. Is our
natural tendency to sell that position? No. Because the value of our stake
Understanding and Overcoming Investing Pitfalls •  127
has fallen so much, we sense that any small movement up will be a big
improvement to the present situation. We “let it ride” and hope for a lucky
break. This is the action of someone who realizes that he or she has little
to lose (because so much is lost already) and everything to gain—which,
of course, is the very definition of desperation (and the day-to-day modus
operandi of many hedge fund employees).
This variable risk profile is depicted by the following graph. The top-
right quadrant shows a risk-averse profile—one would rather cap one’s
gains than let them ride. The bottom-left quadrant shows a risk-seeking
profile—one would rather bet than realize one’s losses.
Prospect theory utility curve
x
U(x)
Note how the curve in the upper right-hand quadrant looks like
the risk-averse utility curve and that everything in the lower left-hand
quadrant looks like the risk-seeking utility curve. This is an astounding
graph, but perhaps an actual, visceral example would carry an even larger
impact.
Think of the fellow who got in on the Google initial public offering,
buying at $85 per share. A few months later, after more than doubling his
money, he happily sells at just above $200 and again puts his capital at risk
in another investment—starting over from square one in terms of making
an investment decision.
128  •   The Intelligent Option Investor
250
Google (GOOG) Closing Price
A
200
150
100
50
0
8/19/2004 9/19/2004 11/19/2004 12/19/2004 1/19/200510/19/2004
This investor’s thought at point A: “I am an investing genius! I just
made a 100 percent return in a couple months—time to take my money off
the table. ” However, after selling the shares and feeling the sense of relief
that he had reduced his risk exposure to Google, he eventually grows dis-
mayed about being hasty in realizing his gain:
800
Google (GOOG) Closing Price
B
C
D
E
F
A
700
600
500
400
300
200
100
0
8/19/2004 2/19/2005 2/19/20068 /19/20068 /19/20078/19/2005 2/19/2007
Understanding and Overcoming Investing Pitfalls •  129
The investor’s reasoning may have gone like this:
A Original sale realizing profits
B “I did the right thing.”
C “I left a little on the table, but it’ll come back soon, and I’ll buy some more then.”
D “Should I short Google?!”
E “Aaaaaaaaaaaargh!”
F Second purchase
Finally, after his mail carrier comments that she is retiring early after
selling her Google position for $675 per share and a person at the country
club buys a new Lexus using his Google sale proceeds, our kinked utility
curve investor does the thing that social creatures tend to do when faced
with uncertainty and remorse—follow the herd. He is happy that his limit
order to buy at $695 is filled at midday and happier still that he made a gain
of 3 percent after buying the shares.
Our hapless investor’s bad sense of timing is good for us because his
purchase of Google shares at the local 2007 market peak and ownership
through the fall allow us to simultaneously follow the psychological pain
he suffered on the stock chart and the utility function curve:
800
Google (GOOG) Closing Price
B
C
A
700
600
500
400
300
200
100
0
11/1/2007 2/1/2008 5/1/2008 8/1/2008 11/1/2008
130  •   The Intelligent Option Investor
Thus an inve


od for us because his
purchase of Google shares at the local 2007 market peak and ownership
through the fall allow us to simultaneously follow the psychological pain
he suffered on the stock chart and the utility function curve:
800
Google (GOOG) Closing Price
B
C
A
700
600
500
400
300
200
100
0
11/1/2007 2/1/2008 5/1/2008 8/1/2008 11/1/2008
130  •   The Intelligent Option Investor
Thus an investor in Google at $695 feels pain extremely quickly when
the value of the position drops slightly to the $620 per share level, let’s say;
this is indicated at position A in the diagram. However, as the price continues
to decline (let’s say to the $450 per share level indicated by position B ),
human decision makers have a tendency to say something like, “If only I
could get $475 for my shares, I’ d sell right now. ” If and when the shares do in-
deed reach $475, the curvature of the line in this quadrant implies that now
the investor will require yet a higher guaranteed price (e.g., $525 per share)
before he closes the bet. At some point, which may be one representing a
significant loss of principal, the investor is largely inured to the prospect
of further losses, and if the stock price goes far enough down, the investor
is no longer tempted to bet on a small rise in price. This is the point that
people usually sell—just as the $50 stock they bought is trading for $1.50
on the Pink Sheets!
This psychological effect is dreadfully difficult to overcome—
perhaps impossible. However, again, I believe that the most important
first step is having a rational, educated estimate of the fair value range of
a company and understanding the drivers that go into the values making
up that range.
Let’s say that you bought a stock for $30 after having determined a
low-end valuation of $39 and the high-end valuation around $50. Now a
quarterly earnings announcement reports good numbers—data suggesting
that the valuation cluster around $50 is closer to correct—and the stock
advances by 10 percent—to $33.
Under these conditions, you are less likely to excitedly take your
profits after the 10 percent up day because you know that the stock still
has about 50 percent to go before it gets to your best-case valuation range.
Again, understanding the drivers of valuation and having an appreciation
for (and humility in the face of) the uncertainty involved in any projection
of future conditions (as reflected by a valuation range) constitute the best
way I have found to combat the deep-seated bias related to the kinks in our
perception of risk.
Now we’ll look briefly at structural impediments to rational investing
before pulling together all the lessons learned so far to see how to invest
intelligently using options.
Understanding and Overcoming Investing Pitfalls •  131
Structural Impediments
We know that we have an enemy living inside of us in the form of the behav-
ioral biases discussed earlier. If this weren’t bad enough, we are attempting to
invest intelligently in an environment not conducive to intelligence. In other
words, not only must we battle an enemy within, but enemies without as well.
The enemies without are comprised of the forces arrayed against us—
the owners of capital attempting to invest intelligently. These forces are part
of the very structure that has developed to trade, manage, custody, ana-
lyze, and report on securities that is such an integral part of the investing
process. They consist of the many explicit messages we as investors receive
every week telling us that we should “trade like a pro” and the implicit mes-
sages that we don’t know what we are doing so we should put our faith in
this expert or the next if we hope to be successful.
At the heart of these structural issues is the distinction between prin-
cipals and agents.
Principals versus Agents
Y ou cannot talk about structural impediments without making the distinc-
tion between principals and agents. Principals are the owners of capital
who invest in risky projects or assets with the expectation of generating a
positive return. Principals can be like you and me—individuals with finite
lives—or can be legal entities such as endowments or companies—which
are theoretically perpetual actors. Agents, on the other hand, are the inter-
mediaries who act on behalf of principals in return for salaries and who are
paid for out of the capital of principals.
Any time a person is compensated for doing something, his or her
own interests are on the line. When our own interests are on the line, we
look for opportunities to protect and advance them. Unless a great deal of
thought is put into how investment performance is measured and assessed
and how compensation is awarded to agents as a result of that performance,
in the process of advancing their own agendas, agents actually may end
up working at cross-purposes to their principals. This tension between
agents—who must work within the constraints of their industry to keep
132  •   The Intelligent Option Investor
their jobs and advance their careers—and principals—who by and large
are simply looking to save enough money to live comfortably in retirement
and pass something on to their descendants—lies at the root of what I term
structural impediments.
To investigate these structural impediments, we first need to figure
out who is playing this investment game and what the rules are. To do this,
I’ll introduce the teams: the buy side and the sell side—both of which are
agents—and the principals. With this knowledge, we can better avoid the
structural pitfalls established by the agents largely for their own benefit.
The Buy Side
The buy side consists of agents hired by principals to invest and manage
the principals’ capital on their behalf. The most well-known buy-side play-
ers are mutual funds and hedge funds, but insurance companies, pension
funds, and endowments also fit into this category. I tend to think of hedge
funds and mutual funds


itfalls established by the agents largely for their own benefit.
The Buy Side
The buy side consists of agents hired by principals to invest and manage
the principals’ capital on their behalf. The most well-known buy-side play-
ers are mutual funds and hedge funds, but insurance companies, pension
funds, and endowments also fit into this category. I tend to think of hedge
funds and mutual funds as being different in approach from the others, so
we’ll look at these two groups separately.
Perhaps the attitude of mutual and large hedge fund players can best
be summed up by the words of a professional money manager, who once
told me, “Erik, no one ever got fired for not making money; they got fired
for losing money. ” Most people unfamiliar with the money-management
industry think that performance is paramount for the managers. In fact,
investment performance is only a slightly inconvenient means to an end
for money managers. For the owner of a hedge or mutual fund, the real
name of the game is assets under management (AUM). AUM is the total
amount of money a fund manages on behalf of its clients, and it is the main
source of wealth for the owners of a fund. Mutual funds charge a load that
represents a percentage of money clients leave with them to manage but are
not usually directly rewarded for the performance of the fund. In the case of
mutual funds, AUM is all important, and investment performance is merely
a marketing tool. If fund A can generate good enough performance to stand
prominently in the pack of other funds (i.e., “x percent of our funds beat
their Lipper averages”), and rating companies such as Morningstar give the
fund a positive rating, present customers of fund A are less likely to take
their money to another fund, and customers of lower-performing funds
Understanding and Overcoming Investing Pitfalls •  133
will move their money to be managed by fund A. Of course, at the annual
bonus time, fund employees are compensated in rough proportion to the
performance of their investment recommendations, so there is an incentive
for analysts and portfolio managers to perform well. However, if an analyst
is interested in keeping his or her revenue stream coming in in the form of
salary, the analyst quickly learns that the best route is usually the safest one.
This leads to a phenomenon known as closet indexing , where an in-
vestment fund’s portfolio is so diversified that it effectively takes on a risk-
return profile equivalent to the index (or whatever benchmark the fund is
using to measure relative performance). A 2011 study by Martijn Cremers
and colleagues concluded the following (italics added by author):
In this paper we examine the prevalence of explicit and implicit
(closet) indexing in equity mutual fund management across 30
countries. We find that although little explicit indexing exists
as a proportion of assets under management [N.B.: There are
few low-load index funds in proportion to “actively managed”
funds] in almost all countries, a large amount of closet indexing
exists. That is, equity fund managers in many countries choose
portfolios that track their stated benchmark closely.
Or, to put it simply, whether an investor puts money into an active
fund or an index fund, the investor mainly just gets the performance of
the index. In addition, bonuses and salary increases are apportioned out
on an annual basis, meaning that the natural investing time horizon for
an analyst or money manager is only one year. Almost everyone in the
industry feels a sense of excitement and relief at the beginning of a new
year because they know they are starting out with a fresh slate. Clearly, the
agents—the employees and owners of the funds—are not acting in the best
interests of the principals (because they are charging fees but not provid-
ing much or any benefit), and the agents’ investing time horizons are not,
by and large, aligned with the investing time horizons of the principals
(agents start again with a fresh slate every year whereas principals worry
only about the value of their investment assets at some point in time, like
college admission or retirement).
The same sort of dynamic occurs in the hedge fund industry, al-
though with a bit of a twist. Large hedge funds usually are set up in a
134  •   The Intelligent Option Investor
“2-and-20” arrangement, where 2 percent of a client’s money every year
goes immediately to the manager (this is the load in a mutual fund), and
20 percent of profits (or profits over some benchmark) are apportioned out
on a periodic basis. The owners of these prominent funds usually set up
their businesses in such a way as to receive all the moneys based on AUM
and leave the lion’s share of the risky, performance-based payout to the
portfolio managers and analysts hired to manage the money. The owners
of large hedge funds, in other words, have compensation structures that are
very similar to those of the owners of large mutual funds and so are con-
cerned mainly with clients not moving their money to other hedge funds.
For the owners of these funds, performance is, in a sense, just a necessary
evil to their goal of generating wealth by safekeeping the wealth of others.
The owners of small hedge funds and the managers/analysts of all
hedge funds lead a much more tenuous existence. This business is extremely
competitive, and the continuation of these agents’ salary- and bonus-gen-
erated revenue streams is extremely sensitive to recent performance. Small
hedge fund owners are beholden to hedge funds of funds (HFoF)—another
intermediary that funnels principals’ capital to different hedge funds in re-
turn for a fee—and their money is extremely “fast. ” If a small fund manager
does not outperform the appropriate benchmark in a given quarter or can-
not convince the HFoF that performance lagged in the last quarter for some
reason that will reverse itself in spades in the next quarter, it i


ds of funds (HFoF)—another
intermediary that funnels principals’ capital to different hedge funds in re-
turn for a fee—and their money is extremely “fast. ” If a small fund manager
does not outperform the appropriate benchmark in a given quarter or can-
not convince the HFoF that performance lagged in the last quarter for some
reason that will reverse itself in spades in the next quarter, it is very likely
that the HFoF will pull its money from the fund. Similarly, a portfolio man-
ager working for a large fund must, at least on an annual basis, prove to the
hedge fund owner that his or her performance has been good enough or will
soon be good enough to deserve a continued allotment of the clients’ capital.
Strangely enough, as more and more hedge funds flood the market,
soaking up opportunities to generate alpha (excess returns), hedge funds
have come to display returns that are highly correlated with the underly-
ing index. A recent research report published by Morgan Stanley told this
tale in figures—the correlation between the Standard and Poor’s 500 Index
(S&P 500) and an index of hedge funds reached around 90 percent in mid-
2013.
4 This does not mean that an individual hedge fund will engage in
closet indexing as a mutual fund might, but it does mean that if you invest
your money in multiple hedge funds to try to generate better performance,
your returns will start looking a lot like the returns of the index at large.
Understanding and Overcoming Investing Pitfalls •  135
Turning now to the next buy-side group—insurance companies,
pension funds, and endowments—we see a different business model and
different motivations for employees. In general, these buy-side businesses
have much less pressure to generate superlative returns and exist as a sort
of appendage of another primary business. Life insurance companies
invest their clients’ money but generally promise very limited returns—
structuring agreements with clients in such a way as to ensure that if their
investment decisions are at least minimally competent, they will be able to
fulfill their promises to clients. As such, investments tend to be a default se-
lection of blue chip equities and high-quality bonds. In this environment,
the portfolio manager is not measured so much on his or her investment
prowess but rather on his or her ability to allocate to bonds and stocks in
a sensible enough proportion to be able to satisfy the insurance company’s
obligations to its clients when they come due. The real risk to the insurance
company is not collecting enough fees or promising its clients too much.
The investment horizon for these funds is something like 10 to 20 years.
Pension funds are much the same in terms of investment philosophy—
if a portfolio manager allocates assets sensibly between high-grade corporate
bonds and blue chip stocks, his or her career is basically safe. It is rare to find
private sector entities now that even offer pensions to their employees and
tougher still to think of examples of pensions that are adequately or overfunded
(meaning that they have enough funds to meet their future obligations). Again,
the investment horizon for these entities is a long 10 to 20 years.
Until rather recently, university endowments were very similar to in-
surance or pension funds, but they naturally have much longer investment
time horizons because the money is usually not promised to any specific
purpose in some limited time frame. Endowments usually allocate to a
wider range of asset classes—including hedge funds, private equity funds,
real estate, and so on—and several gifted portfolio managers at Harvard
and Y ale have done this to enormous effect on behalf of their schools in
recent years. However, in general, asset selection or allocation risks are low
for managers in this environment. Rather, the risks are much more related
to the ability of managers to satisfy their schools’ boards of governors that
they are managing the school assets with propriety and foresight.
One undeniable fact to all buy-side firms is that as the entity grows
larger, it becomes harder and harder to invest in anything but very large
136  •   The Intelligent Option Investor
and liquid stocks. Even if you have a small cap position that increases by
100 percent in a single year, if your investment base is so large that the win-
ning position’s size is only 0.005 percent of the total AUM at the beginning
of the year, it only represents 0.01 percent of the portfolio at the end of the
year—hardly moving the needle in terms of excess performance.
To summarize the players in tabular format:
Player Clients Are . . . Time Horizon Risk
Investment
Paradigm
Hedge funds Demanding,
fast money
3 months to
1 year
Owner: Losing
clients
Managers: Not
making risky
enough bets
Anything that pro-
vides alpha
Mutual funds Docile and
uninformed
1 year Breaking from the
herd and see-
ing AUM drop
Closet indexing
Insurance
companies
and pension
funds
Largely
unaware
of their
investments
10 to 20 years Not charging
clients enough
(insurance); not
retiring before
the pension is
discontinued/
defaulted on
(pensions)
AAA bonds and blue
chip stocks—risk
aversion
Endowments Not born yet 10 years to
100 years
Losing
confidence
of board of
governors
Wide asset-class
level allocation
with long-term
perspective
Look back at this table. As a principal owner of capital, is there any-
thing listed in the risk column that speaks to the risk of investing that you
yourself have experienced or feel is most pressing to you?
The Sell Side
The sell side consists of companies whose job it is to connect principals
(through their agents) who have capital with the financial markets.
Understanding and Overcoming Investing Pitfalls •  137
Broker-dealers are the sell-side counterparties for institutional investors,
whereas stock brokers and online brokers are the counterparties for indi-
vidual ones.
The operative p


o you?
The Sell Side
The sell side consists of companies whose job it is to connect principals
(through their agents) who have capital with the financial markets.
Understanding and Overcoming Investing Pitfalls •  137
Broker-dealers are the sell-side counterparties for institutional investors,
whereas stock brokers and online brokers are the counterparties for indi-
vidual ones.
The operative principle for this business is best summed up in the old
adage, “Bears make money, and Bulls make money. Pigs get slaughtered. ”
In other words, sell-siders do not care if the market goes up or down be-
cause their revenues depend only on investors accessing the market. The
only way to lose this game is to get too greedy and take a risk position in a
security that subsequently loses value.
5
Sell-side players basically make money in proportion to how often
their clients come to the market. As such, the sell side has a vested interest
in getting its clients to trade as often as possible. Sell-side research groups
hire very smart graduates from top universities and industry insiders
who basically act as marketing arms for the firms’ sales and trading desks.
The more short-term “catalysts” the research group can find that might
prompt a client to make a stock purchase or sale, the better for them.
Research groups’ bonuses are determined in large part by feedback from
the sales and trading desk. Because the sales and trading team only makes
money if a client trades, research that advocates long holding periods and
infrequent trading is certainly not welcome, no matter how efficacious it
might be.
The main duty of the people on the sales desks is to prompt clients
to make a trading decision and to trade with them (rather than another
bank), so salespeople spend a good bit of time making cold calls to hedge
fund traders to give them some market “color” and point out opportunities
to make short-term trades.
The End Result
The buy and sell sides interact with one another in such a way as to create
an investing environment that values short-termism and dependence on
large-capitalization stocks. The problem is that individual investors get
wrapped up in these machinations and end up trying to act like agents
when they are in fact principals. Agents, as we have seen, get paid a salary
and bonus on the basis of various short-term factors that are, at best, neutral
and, at worst, damaging to the interests of principals. Buy-side agents, as
138  •   The Intelligent Option Investor
we have seen, are either relatively disinterested in investment performance
(e.g., insurance companies and pension funds) or are interested only in
relative outperformance over a very short time frame (e.g., hedge funds
and mutual funds). Sell-side agents make money in proportion to trading
volume and frequency, so they are happy to facilitate the enormous trade
in a blue chip securities on behalf of a pension fund or the hundreds or
thousands of individual trades in a day on behalf of an aggressive active
hedge fund.
None of these agents are considering the economic value that may be
created by the company in which they are investing, and in the attempt to
maximize their own compensation, they are happy to ignore the long-term
view in favor of a trade that will work within 90 days. Individual investors
read sell-side research, and because the research analysts are so intelligent
and well informed about various minutiae of a given company or industry,
they think that the analysts’ recommendations will help them in the long
term. Business news channels offer a constant stream of pundits from both
buy and sell sides pontificating about things that matter to them—short-
term opportunities to generate a small advantage for the quarter—and that
individual investors wrongly assume should be important to them as well.
An experienced technical analyst can find an investment opportu-
nity in any chart pattern. A sell-side investment banker can always talk
about why one company looks cheap in comparison with another in the
same industry based on some ratio analysis that has a shelf life of about
two weeks. Discount brokerages are happy to supply individual investors
with sophisticated software and data packages that are “free” as long as the
investors make a certain number of trades per month, and they encourage
their clients to “trade like a pro. ”
The end result of these structural factors is that individual investors
get caught in a mental trap that if they are doing anything different from
what they see their highly paid agents doing, they must be doing some-
thing wrong. This is reinforced by one behavioral bias I mentioned in pass-
ing earlier—herding—the human tendency to try to find safety in following
the lead of others rather than risk independent action.
In general, any information or strategy that does not hone in on the
long-term economic value of a company should be considered by intel-
ligent investors to be a red herring and ignored. No individual investor is
Understanding and Overcoming Investing Pitfalls •  139
being compensated with respect to short-term or relative performance, so
information that is purported to give them advantages in this realm should
be taken with a grain of salt.
Now that you have a good idea of the theory behind options from
Part I and the theory of how to assess rational valuation ranges for a stock
without falling into behavioral or structural traps from Part II, let’s apply
this knowledge to the practical task of investing. Part III discusses how to
apply the principles of intelligent stock valuation to option investing and
shows how to tilt the balance of risk and reward in our favor.
This page intentionally left blank
141
Part III
IntellIgent OptIOn
InvestIng
Now that you understand how options work and how to value companies,
it is time to move from the theoretical to the practical to see how to apply
this knowledge to investing


to
apply the principles of intelligent stock valuation to option investing and
shows how to tilt the balance of risk and reward in our favor.
This page intentionally left blank
141
Part III
IntellIgent OptIOn
InvestIng
Now that you understand how options work and how to value companies,
it is time to move from the theoretical to the practical to see how to apply
this knowledge to investing in the market. With Part III of this book, we
make the transition from theoretical to practical, and by the time you finish
this part, you will be an intelligent option investor.
To invest in options, you must know how to transact them; this is the
subject of Chapter 7. In it, you will see how to interpret an option pricing
screen and to break down the information there so that you can under -
stand what the option market is predicting for the future price of a stock. I
also talk about the only one of the Greeks that an intelligent option investor
needs to understand well—delta.
Chapter 8 deals with a subject that is essential for option investors—
leverage. Not all option strategies are levered ones, but many are. As such,
without understanding what leverage is, how it can be measured and used,
and how it can be safely and sanely incorporated into a portfolio, you can-
not be said to truly understand options.
Chapters 9–11 deal with specific strategies to gain, accept, and mix
exposure. In these chapters I offer specific advice about what strike prices
are most effective to select and what tenors, what to do when the expected
outcomes of an investment materially change, and how to incorporate
each strategy into your portfolio. Chapter 11 also gives guidance on so-
called option overlay strategies, where a position in a stock is overlain by
an option to modify the stock’s risk-reward profile (e.g., protective puts for
hedging and covered calls for generating income).
142  •   The Intelligent Option Investor
Unlike some books, this book includes only a handful of strategies,
and most of those are very simple ones. I shun complex positions for two
reasons. First, as you will see, transacting in options can be very expensive.
The more complex an option strategy is, the less attractive the potential
returns become. Second, the more complex a strategy is, the less the inher-
ent directionality of options can be used to an investor’s advantage.
Simple strategies are best. If you understand these simple strategies
well, you can start modifying them yourself to meet specific investing sce-
narios when and if the need arises. Perhaps by using these simple strategies
you will not be able to chat with the local investment club option guru
about the “gamma on an iron condor, ” but that will be his or her loss and
not yours.
Chapter 12 looks at what it means to invest intelligently while under-
standing the two forms of risk you assume by selecting stocks in which to
invest: market risk and valuation risk.
143
Chapter 7
FIndIng MIsprIced
OptIOns
All our option-related discussions so far have been theoretical. Now it
is time to delve into the practical to see how options work in the market.
After finishing this chapter, you should understand
1. How to read an option chain pricing screen
2. Option-specific pricing features such as a wide bid-ask spread,
volatility smile, bid and ask volatility, and limited liquidity/
availability
3. What delta is and why it is important to intelligent option investors
4. How to compare what the option market implies about future
stock prices to an intelligently determined range
In terms of where this chapter fits into our goal of becoming intelligent
option investors, obviously, even if you have a perfect understanding of
option and valuation theory, if you do not understand the practical steps
you must take to find actual investment opportunities in the real world, all
the theory will do you no good.
New jargon introduced in this chapter includes the following:
Closing price Bid implied volatility
Settlement price Ask implied volatility
Contract size Volatility smile
Round-tripping Greeks
Bid-ask spread Delta
144  •   The Intelligent Option Investor
Making Sense of Option Quotes
Let’s start our practical discussion by taking a look at an actual option
pricing screen. These screens can seem intimidating at first, but by the end
of this chapter, they will be quite sensible.
Last
0.86 -0.23
-0.14
-0.04
-0.17
-0.14
-0.06
-0.13
-0.12
-0.07
-0.09
-0.14
-0.06
-0.20
-0.26
-0.10
+0.01
0.91 0.94 21.672% 24.733% 0.8387
0.4313
0.0631
0.0000
0.0000
0.0000
0.9580
0.9598
0.9620
0.7053
0.4743
0.2461
0.0357
0.0392
0.0482
21.722%
22.988%
62.849%
72.188%
81.286%
201.771%
192.670%
175.779%
20.098%
18.997%
18.491%
25.587%
29.201%
35.855%
55.427%
123.903%
64.054%
23.311%
22.407%
21.813%
21.147%
22.144%
23.409%
54.689%
66.920%
35.642%
23.656%
23.072%
22.553%
21.460%
21.374%
21.581%
32.597%
24.854%
23.426%
20.380%
19.627%
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
0.26
0.04
0.02
0.02
0.02
13.30
12.40
11.35
1.19
0.58
0.22
0.01
0.01
0.02
11.90
12.35
10.10
1.68
1.10
0.67
0.05
0.03
0.02
0.24
0.02
10.35
9.30
8.40
1.17 19.408%
18.405%
17.721%
0.56
0.20
11.75
10.70
9.50
1.65
1.08
0.65
0.04
0.01
0.01
11.55 12.30
12.00
10.00
2.48
1.93
1.48
0.41
0.29
0.21
12.20
3.60
1.75
10.05
9.85
2.44
1.91
1.45
0.39
0.27
0.18
12.10
3.50
1.70
0.00
0.23
0.02
C0.00
C0.00
C0.00
0.09
0.45
1.15
C4.99
C5.99
C6.99
C4.99
C5.99
C6.99
C12.01
C11.01
C10.01
1.16
0.54
0.22
C0.00
C0.00
C0.00
C0.00
C0.00
C0.00
0.33
0.76
1.40
C5.03
C6.00
C6.99
C0.00
C0.01
C0.03
0.84
1.23
1.88
C12.02
C11.03
C10.04
1.65
1.06
0.66
C0.06
0.03
0.02
C12.05
C11.07
C10.10
C2.58
1.93
12.10
3.40
1.69
0.68
4.25
C7.27
1.42
0.38
C0.30
C0.22
C0.11
C0.15
C0.19
1.80
2.27
2.73
C5.57
C6.43
C7.35
Chng Bid AskA skImpl.I mpl.Bid Vol. Vol. Delta JUL 26 ´13
31
32
33
37
38
39
20
21
22
31
32
33
37
38
39
Description
Call
Last Chng Bid AskA skImpl.I mpl.Bid Vol. Vol. Delta
Put
0.9897
0.9869
0.9834
0.6325
0.4997
0.3606
0.0463
0.0266
0.0


5
1.06
0.66
C0.06
0.03
0.02
C12.05
C11.07
C10.10
C2.58
1.93
12.10
3.40
1.69
0.68
4.25
C7.27
1.42
0.38
C0.30
C0.22
C0.11
C0.15
C0.19
1.80
2.27
2.73
C5.57
C6.43
C7.35
Chng Bid AskA skImpl.I mpl.Bid Vol. Vol. Delta JUL 26 ´13
31
32
33
37
38
39
20
21
22
31
32
33
37
38
39
Description
Call
Last Chng Bid AskA skImpl.I mpl.Bid Vol. Vol. Delta
Put
0.9897
0.9869
0.9834
0.6325
0.4997
0.3606
0.0463
0.0266
0.0155
0.9712
0.9628
0.9535
0.5890
0.5118
0.4324
0.1664
0.1258
0.0923
0.9064
0.5354
0.3336
+0.01
+0.10
+0.11
0.07 0.09 22.812%2 4.853% -0.1613
-0.5689
-0.9373
-1.0000
-1.0000
-1.0000
22.469%
24.612%
85.803%
203.970%
267.488%
20.456%
19.851%
N/A
N/A
N/A
0.42
1.20
5.25
7.25
8.90
0.39
1.17
4.90
4.85
5.40
+0.02
+0.09
+0.14
-0.0420
-0.0402
-0.0380
-0.2948
-0.5261
-0.7545
-0.9652
-0.9616
-0.9524
77.739%
70.681%
63.514%
20.303%
19.170%
19.011%
41.423%
61.602%
52.378%
N/A
N/A
N/A
N/A
N/A
N/A
0.02
0.02
0.02
0.34
0.73
1.38
5.30
6.55
7.30
0.33
0.71
1.35
4.95
19.958%
18.577%
17.954%
4.65
6.70
22.720%
22.019%
21.378%
20.455%
19.050%
21.354%
0.000%
23.193%
22.845%
22.218%
21.148%
20.913%
20.899%
+0.07
+0.05
+0.16
+0.09
+0.12
+0.04
50.831%
48.233%
46.993%
23.384%
22.672%
22.106%
36.111%
30.947%
44.342%
N/A
N/A
N/A
N/A
0.02
0.03
0.05
0.82
1.25
1.82
5.55
6.30
7.55
0.01
0.80
1.23
1.79
4.95
6.15
6.85
-0.0103
-0.0131
-0.0166
-0.3679
-0.5008
-0.6402
-0.9558
-0.9757
-0.9871
22.989%
22.284%
21.453%
17.134%
37.572%
38.919%
37.587%
35.246%
23.914%
23.485%
22.925%
22.967%
26.265%
28.715%
0.11 0.13
0.17
0.19
1.78
2.25
2.80
5.80
6.85
7.85
0.13
0.17
1.75
2.22
2.76
5.70
6.50
7.40
-0.0318
-0.0406
-0.0503
-0.4120
-0.4879
-0.5665
-0.8294
-0.8690
-0.9025
34.172%
23.567%
23.145%
22.479%
21.404%
19.420%
18.411%
37.790%
35.385%
30.523%
24.198%
23.081%
0.00
+0.09
33.497%
26.033%
24.745%
0.68
4.25
7.40
0.66
4.15
7.30
-0.0906
-0.4520
-0.6521
33.203%
25.378%
24.054%
AUG 16 ´13
20
21
22
31
32
33
37
38
39
SEP 20 ´13
20
21
22
31
32
33
37
38
39
20
32
37
JAN 17 ´14
JAN 16 ´15
I pulled this screen—showing the prices for options on Oracle (ORCL)—
on the weekend of July 20–21, 2013, when the market was closed. The last
trade of Oracle’s stock on Friday, July 19, was at $31.86, down $0.15 from the
Thursday’s close. Y our brokerage screen may look different from this one, but
you should be able to pull back all the data columns shown here. I have limited
the data I’m pulling back on this screen in order to increase its readability.
More strikes were available, as well as more expiration dates. The expirations
shown here are 1 week and 26, 60, 180, and 544 days in the future—the
544-day expiry being the longest tenor available on the listed market.
Let’s first take a look at how the screen itself is set up without paying
attention to the numbers listed.
Finding Mispriced Options    • 145
Calls are on the left, puts on the right.
Strike prices
and expirations
are listed here.
You can tell the stock was down on this day because most of the call
options are showing losses and all the put options are showing gains.
All the strikes for
each selected expiry
are listed grouped
together.
This query was set up
to pull back three
strikes at the three
moneyness regions
(20–22, 29–31, 37–39).
The 1-week options
and the LEAPS did
not have strikes at
each of the prices I
requested.
Now that you can see what the general setup is, let’s drill down and
look at only the calls for one expiration to see what each column means.
Last
C12.02 11.75
10.70
9.50
1.65
1.08
0.65
0.04
0.01
0.01 0.02
0.03
0.05
0.67
1.10
1.68
10.10
12.35
11.90 N/A
N/A
N/A
22.720%
55.427% 20
SEP 20 ´13
21
22
31
32
33
37
38
39
0.9869
0.9834
0.6325
0.4997
0.3606
0.0463
0.0266
0.0155
123.903%
64.054%
23.311%
22.407%
21.813%
21.147%
22.144%
23.409%
22.019%
21.378%
20.455%
19.050%
21.354%
C11.03
C10.04
1.65
1.06
-0.13
-0.12
-0.07
0.00
+0.01
0.66
C0.06
0.03
0.02
Chnq Bid AskA skImpl.I mpl.Bid Vol. Vol. Delta Description
Call
0.9897
Red
(loss) Green
(gain)
146  •   The Intelligent Option Investor
Last
This is the last price at which the associated contract traded. Notice that
the last price associated with the far in-the-money (ITM) strikes ($20, $21,
$22) and one of the far out-of-the-money (OTM) strikes ($37) have the
letter “C” in front of them. This is just my broker’s way of showing that the
contract did not trade during that day’s trading session and that the last
price listed was the closing price of the previous day. Closing prices are not
necessarily market prices. At the end of the day, if a contract has not traded,
the exchange will give an indicative closing price (or settlement price ) for
that day. The Oracle options expiring on August 16, 2013, and struck at
$20 may not have traded for six months or more, with the exchange simply
“marking” a closing price every day.
One important fact to understand about option prices is that they
are quoted in per-share terms but must be transacted in contracts that rep-
resent control of multiple shares. The number of shares controlled by one
contract is called the contract size . In the U.S. market, one standard con-
tract represents control over 100 shares. Sometimes the number of shares
controlled by a single contract differs (in the case of a company that was
acquired through the exchange of shares), but these are not usually avail-
able to be traded. In general, one is safe remembering that the contract size
is 100 shares.
Y ou cannot break a contract into smaller pieces or buy just part of a
contract—transacting in options means you must do so with indivisible
contracts, with each contract controlling 100 shares. Period. As such, every
price you see on the preceding screenshot, if you were to transact in one of
those options, would cost you 100 times the amount shown. For example,
the last price for the $31-strike option was $1.65. The investor who bought
that contract paid $165 for it (plus fees, taxes, and commissions, which are
not included in the posted price). In the rest of this book, when I make
calculations re


very
price you see on the preceding screenshot, if you were to transact in one of
those options, would cost you 100 times the amount shown. For example,
the last price for the $31-strike option was $1.65. The investor who bought
that contract paid $165 for it (plus fees, taxes, and commissions, which are
not included in the posted price). In the rest of this book, when I make
calculations regarding money spent on a certain transaction, you will al-
ways see me multiply by 100.
Change
This is the change from the previous day’s closing price. My broker shows
change only for contracts that were actively traded that day. It looks like
Finding Mispriced Options    • 147
the near at-the-money (ATM) strikes were the most active because of the
two far OTM options that traded; one’s price didn’t change at all, and the
other went up by 1 cent. On a day in which the underlying stock fell, these
calls theoretically should have fallen in price as well (because the K/S ratio,
the ratio of strike price to stock price, was getting slightly larger). This just
shows that sometimes there is a disconnect between theory and practice
when it comes to options.
To understand what is probably happening, we should understand
something about market makers. Market makers are employees at bro-
ker-dealers who are responsible for ensuring a liquid, orderly securi-
ties market. In return for agreeing to provide a minimum liquidity of
10 contracts per strike price, market makers get the opportunity to earn
the bid-ask spread every time a trade is made (I will talk about bid-ask
spreads later). However, once a market maker posts a given price, he or
she is guaranteeing a trade at that price. If, in this case (because we’re
dealing with OTM call options), some unexpected positive news comes
out that will create a huge rise in the stock price once it filters into the
market and an observant, quick investor sees it before the market maker
realizes it, the investor can get a really good price on those far OTM call
options (i.e., the investor could buy a far OTM call option for 1 cent and
sell it for 50 cents when the market maker realizes what has happened.
To provide a little slack that prevents the market maker from losing too
much money if this happens, market makers usually post prices for far
OTM options or options on relatively illiquid stocks that are a bit unrea-
sonable—at a level where a smart investor would not trade with him or
her at that price. If someone trades at that price, fine—the market maker
has committed to provide liquidity, but the agreement does not stipulate
that the liquidity must be provided at a reasonable price. For this reason,
frequently you will see prices on far OTM options that do not follow the
theoretical “rules” of options.
Bid-Ask
For a stock investor, the difference between a bid price and an ask price
is inconsequential. For option investors, though, it is a factor that must
be taken into consideration for reasons that I will detail in subsequent
148  •   The Intelligent Option Investor
paragraphs. The easiest way to think of the bid-ask spread is to think in
terms of buying a new car. If you buy a new car, you pay, let’s say, $20,000.
This is the ask price. Y ou grab the keys, drive around the block, and
return to the showroom offering to sell the car back to the dealership. The
dealership buys it for $18,000. This is the bid price. The bid-ask spread is
$2,000 in this example.
Bid-ask spreads are proportionally much larger for options than
they are for stocks. For example, the options I’ve highlighted here are on
a very large, important, and very liquid stock. The bid-ask spread on the
$32-strike call option (which you will learn in the next section is exactly
ATM) is $0.02 on a midprice of $1.09. This works out to a percentage bid-
ask spread of 1.8 percent. Compare this with the bid-ask spread on Ora-
cle’s stock itself, which was $0.01 on a midprice of $31.855—a percentage
spread of 0.03 percent.
For smaller, less-liquid stocks, the percentage bid-ask spread is even
larger. For instance, here is the option chain for Mueller Water (MW A):
2.5
5
7.5
10
Last
C5.30
C2.80
0.55
C0.00
Change Bid AskI mpl. Bid Vol. Impl. Ask Vol. Impl. Bid Vol. Impl. Ask Vol.Delta
2.5
5
7.5
10
2.5
5
7.5
10
12.5
DescriptionCall
Last Change BidA sk Delta
Put
C0.00
C0.00
C0.25
C2.25
C0.00
C0.00
C0.55
C2.35
C0.00
C0.10
C0.85
C2.55
C4.80
5.20 5.50 N/A 340.099% 0.9978
0.9978
0.7330
0.1316
0.9347
0.8524
0.6103
0.1516
0.9933
0.9190
0.6070
0.2566
0.1024
142.171%
46.039%
76.652%
N/A
N/A
2.95
0.55
0.10
0.20
0.10 N/A
N/A
N/A
0.10
0.30
2.35
40.733%
N/A
N/A
N/A
N/A
36.550%
38.181%
35.520%
35.509%
35.664%
2.10
0.50
0.05
0.10
0.60
2.402.30
0.05
0.15
0.15
0.85
2.60
4.90
0.70
2.45
4.60
2.70
0.500.00
5.20 5.50
3.00
0.90
0.20
2.80
0.80
0.10
5.505.10
3.102.85
1.151.05
0.400.30
0.200.05
39.708%
N/A
N/A
36.722%
N/A
38.754%
38.318%
39.127%
36.347%
36.336%
292.169% 0.0000
-0.0000
-0.2778
-0.8663
-0.0616
-0.1447
-0.3886
-0.8447
-0.0018
-0.0787
-0.3890
-0.7375
-0.8913
128.711%
53.108%
88.008%
117.369%
60.675%
42.433%
44.802%
110.810%
50.757%
42.074%
43.947%
49.401%
163.282%
75.219%
42.610%
45.215%
122.894%
64.543%
42.697%
44.728%
50.218%
C5.30
C2.80
C0.85
C0.10
C5.30
C1.10
C0.35
C0.10
3.00 +0.15
AUG 16 ´13
NOV 15 ´13
FEB 21 ´14
Looking at the closest to ATM call options for the November expiration—
the ones struck at $7.50 and circled in the screenshot—you can see that
the bid-ask spread is $0.10 on a midprice of $0.85. This works out to 11.8
percent.
Because the bid-ask spread is so very large on option contracts,
round-tripping
1 an option contract creates a large hurdle that the returns
of the security must get over before the investor makes any money. In the
case of Mueller Water, the options one buys would have to change in price
by 11.8 percent before the investor starts making any money at all. It is for
this reason that I consider day trading in options and/or using c