Let’s schedule a phone call.
“Who is Jerry Marlow?”
Thank you for asking!
I’m the Chief Writing Officer
at wasyourcartotaledorstolen.com.
I also do all the research,
concept development, writing,
design, graphics, and programming;
but, if wasyourcartotaledorstolen.com
ever grows into an organization
of more than one person,
then I will retain the role
of Chief Writing Officer.
“And what exactly does
a Chief Writing Officer do?”
you might ask.
Many of the duties
of a Chief Writing Officer
are eye-poppingly similar
to the duties
of an organization’s
Chief Data Quality Officer.
An organization’s Chief Data Quality Officer
does whatever he or she can
to ensure that an organization’s data
is of high quality.
High quality data is:
-
Accurate,
-
Current,
-
Precise,
-
Accessible, and
-
Useful for the purpose at hand.
Likewise, high quality writing is:
-
Accurate,
-
Current,
-
Precise,
-
Accessible, and
-
Useful for the purpose at hand.
Of course you want
your organization’s writing
to be accurate and current.
Let’s talk about the many things
that writing can do
for your organization
if the writing is also precise, accessible,
and useful for the purpose at hand.
Who_is_Jerry_MarlowChapter
Precise writing can bump up
your organization’s energy level.
If a Chief Writing Officer
can write, edit, nudge,
educate, coax, and inspire
members of an organization
toward a culture
of more precise language,
then the Chief Writing Officer
can bump up the energy level
of the organization.
“Yeah?
“How does that work?”
When you wander
into the thermodynamics
of information theory,
you learn that more precise information
has more thermodynamic energy
than less precise information has.
In a statistical sense,
the more precise language is,
the less probable
the information conveyed is.
The less probable information is,
the more unexpected the information is.
The more unexpected information is,
the more surprise value the information has.
All else being equal,
the more surprising information is,
the more value the information has
for its recipient.
Claude Shannon,
a pioneer in information theory
who worked at Bell Labs,
went so far as to proclaim:
“Information is surprise.”
Imagine how much more
intellectually exciting
your job would be
if your colleagues
communicated with you
in precise and surprising language.
Imagine how much more alive
your brain would become.
Imagine how much more fun
your job would be!
You might even discover that you have
a new work-life-balance problem:
Your job has become more enjoyable
than your personal life.
Perhaps to their detriment,
many organizations have evolved
cultures of imprecise language.
In such cultures,
people may think, speak, and write
not in precise, improbable,
unexpected, valuable, surprising,
high-energy words and sentences,
but in whatever words and sentences
they think their listeners and readers
expect to hear or read.
Instead of sharing
precise information and valuable insights
that might spark vigorous verbal exchanges,
members of such cultures may use
familiar words, phrases,
cliches, metaphors, and analogies
to comfort one another,
to dampen enthusiasm,
to lower expectations,
and to maintain the status quo.
Dispiriting discussions and conversations
may climax with the question:
“Are you comfortable with that?”
Is your CEO’s appetite for risk
such that she or he wants employees
to do only
what makes them feel comfortable?
I advocate the cultivation
of an organizational culture
that produces writing that is:
-
Accurate,
-
Current,
-
Precise,
-
Accessible, and
-
Useful for the purpose at hand.
Yet, in many organizations,
people no longer even utter
the word “writing.”
Instead, in knowing tones,
they speak of “creating content.”
The origins of this energy-sucking,
imagination-killing malaise
trace back several decades
to the dawn of the internet.
Back then, of a sudden,
technologically adept people
created a vast network
that could deliver digital containers
all around the world
faster and cheaper
than FedEx and UPS could deliver
cardboard boxes and envelopes.
Our new technological mandarins
needed only one missing ingredient.
They needed something to put
into their empty digital containers.
They needed, as they thought of it:
“Content!”
What is content?
Content is anything
that can fill up
an empty digital container.
Where writers seek to inform,
energize, inspire, and persuade;
content creators seek to fill up
empty digital containers.
In many organizations,
a culture of content creation
has produced a methodology
of cross plagiarization.
In this methodology,
a firm’s content creators access,
gather, synthesize, homogenize,
and re‑purpose their competitors’ content.
Content creators mix in a list of words
that they got from their firm’s
SEO (search‑engine‑optimization) guru.
If a content creator
wishes to take no responsibility
for the style or structure
of his or her work product,
then he or she may run the content mix
through an AI app
that churns out sentences.
Content creators then fill
their firms’ empty digital containers
with plagiarized,
synthesized,
homogenized,
re‑purposed,
SEO‑words‑injected,
AI‑stylized
content.
Readers, listeners, clients, and customers
then get the low‑energy, low‑value,
expected, comfortable, SEO‑injected,
homogenized, consensus view
of the product, service, pitch,
or proposal at hand.
Energy?
More like entropy.
Surprises?
None to be found.
Value?
Maybe as a Bayesian prior
to give you a picture
of what the less clever among us
assume to be the current state
of the phenomenon under inquiry.
In my writing, editing, and coaching;
my goals are not reader comfort,
listener comfort, and organizational comfort.
My goal is not to fill empty digital containers
with low‑value consensus views of anything.
I value
insight, empathy, courage,
originality, and audacity.
My goals are reader action,
listener action, and organizational action.
In much of business writing,
actions are transactions.
To lead readers and listeners into action,
I tap into their motivations and goals.
I lay out concepts and facts.
I engineer my writing against
my target audience’s decision criteria.
I write boldly and with clarity.
I mix words, graphics, images, and exhibits
in whatever way will accomplish
my or my client’s communication goals
most expeditiously.
I lead readers and listeners
down a cognitive, emotional, visual,
psychological, and behavioral path
that, if I’m successful,
inspires readers and listeners
to spring into action!
I do whatever I can to make it easy
for people who listen to what I write
and for people who read what I write
to take action
and achieve their goals.
I want listeners and readers to succeed!
Precise_writing_can_bump_up_your_energy_levelSubchapter
A culture of precise writing
can make your employees
more courageous and more outspoken.
When an organization’s employees
are informed, energized, and successful;
they discover their inner courage.
Courageous employees
express their points of view!
Courageous employees
share information and ideas
with colleagues!
Courageous employees
initiate collaborations
with people in the organization
outside their own silos!
Courageous employees
propose new ways for the organization
to achieve its goals!
If you think your organization’s employees
could use a boost
to their energy levels
and to their courage,
then maybe your organization
could use a Chief Writing Officer?
If you want to send my resume to HR
or to a colleague,
you can download a copy
from jerrymarlow.com/resume.
Precise_writing_can_make_you_more_courageousSubchapter
When precise writing is accessible,
your colleagues, clients, and customers
can comprehend your messages
quickly, easily, and accurately.
In data management,
accessible means
that the people who need data
can get that data
quickly, easily, cheaply,
and in a form that they can use efficiently.
In spoken and written communication,
accessible means
that people who need information
can understand that information
quickly, easily, and accurately.
To make information accessible,
I mostly write in sentences
that are short and easy to understand.
I do not create logical puzzles such as:
“Blah, blah, blah cannot be underestimated.”
Logical puzzles slow readers down.
Logical puzzles squander readers’
attention, time, and energy.
(Though, I confess, I do love the song
“Is You Is or Is You Ain’t My Baby?”)
In data processing, to interpret
what the value in a data field means,
the software may have to look up
the meaning of the value
in a metadata lookup table.
In listening and reading,
you perform a mental or physical lookup
whenever you ask yourself:
“Who was the antecedent for that pronoun?”
“Which concept was the former concept?
Which concept was the latter concept?”
Performing mental and physical lookups
consumes both time and energy.
To make the sentences that I write
cognitively accessible and efficient,
I minimize the number
of mental lookups
that the reader’s brain
has to perform.
When communication calls
for a long, long sentence;
I organize the sentence
so that the meaning of the sentence emerges
as phrases cascade
from the beginning of the sentence
to the end of the sentence.
I do not want my reader
to have to lug a growing,
increasingly messy
cognitive load
as she or he waits
for connections to click into place.
Yuck!
The very thought of such a sentence
gives me the heebie‑jeebies!
If your organization communicates
in writing with the public,
then you may want to keep in mind
that twenty percent of American adults
do not read proficiently.
If your organization’s writing
is easily accessible,
then your organization can reach more
of these often‑neglected readers.
To make wasyourcartotaledorstolen.com
as efficiently accessible as possible,
I use a technique
that I developed for writing speeches.
When they read a speech to an audience,
many people sound terrible!
Their spoken words
do not flow in natural phrases.
Emphasis falls in weird places.
Intonation is either flat or sounds artificial.
In short, when many people
read a speech out loud,
they sound like they’re reading it.
Why do so many people sound so bad
when they read a speech out loud?
When we speak naturally,
we cluster words into meaningful phrases.
To let each phrase register with the listener,
we pause— however briefly—
before we speak the next phrase.
When a person reads a speech out loud,
to sound natural,
he or she has to sort
into meaningful phrases
words that, usually, are strung along
on paper pages,
on a screen,
or on a teleprompter
without any grouping
into meaningful phrases.
For many a speaker
who is reading a speech out loud,
from the instant
that the speaker’s eyes and brain
register the meaning
of a string of words
until the instant
that those words come out
of the speaker’s mouth,
the time interval
is not enough time
for the speaker’s brain
to group the words
into meaningful, natural‑sounding phrases.
To make it easy
for people to sound natural
when they read
on paper pages,
on a screen,
or on a teleprompter
a speech that I have written;
I break the lines of the speech
into meaningful, natural phrases.
I display the speech
on paper pages,
on the screen,
or on the teleprompter
at the font size that,
for the speaker, produces
the most natural flow of speech.
I minimize the amount
of cognitive work
that the speaker has to do
to sound natural, brilliant, and charismatic.
If folks for whom I write speeches
do not speak with as much charisma
as they would like,
then I am happy to coach them
on how to develop
a powerful and charismatic speaking voice.
For several years,
I studied voice with Carl Stough,
a breathing and voice coach
who worked with Olympic athletes,
with opera singers, with pop singers,
with business leaders, and with musicians.
When a person speaks with a voice
that is weak, muffled,
grating, barely audible,
or otherwise less than fully charismatic;
most often, he or she,
consciously or subconsciously,
has an erroneous and counterproductive
mental model
of the physics and physiology
of speech.
If an uncharismatic speaker
learns an accurate and productive
mental model
of the physics and physiology
of speech
and does some exercises
to undo decades
of self‑defeating
physiological speaking habits,
then greater speaking joy and charisma
are almost certain to emerge.
If your speaking voice
is not as charismatic
as you would like,
then we should talk.
I believe that the same technique
of semantic line breaks
that works so well for people
who are reading speeches out loud
also makes the written word
more accessible to all readers.
Semantic line breaks
minimize the amount of cognitive work
that any reader has to do—
even if he or she
is not communicating
his or her strategic vision
to thousands of employees,
but is reading to himself or herself
on his or her cellphone
messages such as
how to get a fair valuation
of his or her total‑loss vehicle.
If I have successfully made
the concepts, information, and solutions
in wasyourcartotaledorstolen.com
efficiently accessible,
then you may even have found
reading wasyourcartotaledorstolen.com
to be a little spooky.
You zip right along with almost no effort.
Your brain is in cruise mode.
There are no traffic jams.
There are no obstacles in your path.
You are miraculously cruising down
the open highway
of an earlier America
in a 1966 Pontiac GTO convertible.
Your future is so bright
that you have to wear shades!
What do you think?
Have you found yourself zipping
through the writing
on wasyourcartotaledorstolen.com
with almost no effort?
Would you like for your organization’s writing to be equally precise and accessible?
If yes and yes, then
I can make that happen.
Readers_comprehend_accessible_messages_more_quickly_easily_and_accuratelySubchapter
High-quality data is useful
for the purpose at hand.
High-quality writing helps
members of your organization
accomplish their goals.
In the design, provisioning,
and management
of data flows,
every work unit
of every organization
faces multiple interrelated questions:
What decisions do members
of the work unit
need to make and implement?
What data do members
of the work unit
need to make those decisions
and to implement those decisions?
Are members of the work unit
getting that data?
What is the best format
in which to present that data?
Are members of the work unit
getting their data
in the best format?
How are the answers
to these questions
changing and evolving?
In sophisticated organizations,
to answer these questions
and to help data providers
provide data users
with increasingly useful data,
data-quality teams
routinely convene meetings
between data providers
and data users.
In the presence of the data providers,
data-quality team members
ask data users
questions such as these:
“Are you getting the data
that you need
to make good decisions
and to implement those decisions?”
“Could the data be presented
in more useful formats?”
“If yes, how so?”
“What additional data
that we could generate internally
would you find useful?”
“In what format or formats
would you like to receive
this internal data?”
“What additional data
that we could buy
or otherwise obtain externally
would you find useful?”
“In what format or formats
would you like to receive
that external data?”
These discussions allow data providers
to provide data users
with data
that is increasingly useful
for the data users’ purposes.
Likewise, if a Chief Writing Officer
initiates similar feedback conversations
between the intended readers
of written information
and the people who write that information,
then these conversations will allow writers
to provide their intended readers
with writing
that is increasingly useful
for the readers’ purposes.
High_quality_writing_helps_people_accomplish_their_goalsSubchapter
Many people appreciate the ways
in which I maximize
the efficiency, effectiveness,
and sheer enjoyment
of spoken and written communication.
Here is a small sampling
of responses and reactions
to my work.
For a major financial institution,
I wrote and produced
a multi-media presentation
that communicated to employees
the institution’s transaction flows,
senior management’s strategic vision,
and what the people
in the audience could do
to help the institution realize
senior management’s strategic vision.
At its climax,
in the name of senior management,
the presentation
challenged the people in the audience
to manage opportunity.
One employee later said to her manager,
“I went back to my office
and asked myself
what I could do differently.”
Another employee said to his manager,
“I never knew my job was so important.
I can’t wait to get back to work!”
People_like_how_I_maximize_efficiency_and_effectiveness_of_writingSubchapter
To make Black‑Scholes
options pricing theory
easy to understand
for the mathematically challenged,
I designed and programmed
a visual simulator.
Black‑Scholes options pricing theory
changed the world of finance.
Once you have an intuitive understanding
of Black‑Scholes options pricing theory,
many other principles of modern finance,
principles of risk management,
and principles of financial decision‑making
become easily understandable.
However, for many people,
the mathematics
of Black‑Scholes options pricing theory—
stochastic calculus—
makes it difficult
to gain such an understanding.
To make Black‑Scholes
options pricing theory
intuitively understandable
for people who have not yet
mastered stochastic calculus,
I designed and programmed
a visual simulator.
The simulator embodies the math,
the assumptions, and the relationships
of Black‑Scholes options pricing theory.
When you play around with the simulator,
you can see the math, the assumptions,
and the relationships at work.
You can gain an intuitive understanding
of Black‑Scholes options pricing theory
without having to deal with the math.
In case you wish to get a feel
for Black‑Scholes options pricing theory
and how the simulator
makes Black‑Scholes
intuitively accessible,
next up is a sequence of screen captures
from the simulator
and an explanation beneath each one.
If you prefer to skip over this mini tutorial
on Black‑Scholes options pricing theory,
click here.
If you’re reading this essay
on your cellphone,
save the mini Black‑Scholes tutorial
for when you can step through it
on a device that has a larger screen.
When your display screen is big enough
to simultaneously display
an entire screen capture
and all the text
immediately below the screen capture,
do not scroll.
Instead, click the right arrow
at the top of the screen capture.
Once you've read
through all the screen captures,
click back to the first one;
then click through the visuals.
You’ll get a better sense
of the fluidity of the visuals.
Or, if you only want to see the visuals,
quickly click through the screen captures.
3333Subchapter
Let’s say that a particular stock is trading today at $8.00.
You are 99.7% certain that, at the end of the coming year (365 days from today), the price of the stock will not be higher than $64.00 or lower than $1.00. (The stock pays no dividends.)
If you enter this information into the simulator and click Calculate Your Forecast,
3334Subchapter
the simulator translates your end‑of‑period price range and 99.7% confidence level into a forecast of expected continuously compounded return,
median continuously compounded return,
and standard deviation of expected volatility.
The simulator draws your end‑of‑period forecast
as a normal bell‑shaped probability distribution out to three standard deviations on an axis of continuously compounded rates of return.
Continuously compounded rates of return are also called geometric rates of return.
Black‑Scholes options pricing theory uses geometric rates of return.
The simulator also draws your end‑of‑period forecast as a normal bell‑shaped probability distribution on a lognormal price axis.
3335Subchapter
In a normal probability distribution, 99.7% of values lie within three standard deviations of the forecast median.
Whenever the simulator fills in the area of an outline probability distribution, it fills in the area out to four standard deviations. The two red tiny squares above the outline probability distribution and the two red tiny squares below the outline probability distribution are out at four standard deviations.
The example forecast that I’m using to explain Black‑Scholes options pricing theory is not a realistic forecast for a real stock.
I’m using this forecast to make it easy for you to think in geometric rates of return.
If a stock price has a geometric rate of return of 69.315%, the stock price doubles.
If a stock price has a geometric rate of return of ‑69.315%, the stock price loses half its value.
Our return axis and price axis are drawn in increments of 69.315%.
Our forecast has a standard deviation of volatility of 69.315%.
Once you have, over a given time horizon, a forecast of expected continuously compounded return
and standard deviation of expected volatility,
your_forecast_in_sdsSubchapter
you can simulate potential price paths over your time horizon.
From a given forecast,
3336Subchapter
you can get an almost infinite number of different potential price paths.
The simulator tabulates each price‑path outcome with a tiny square.
Instead of drawing price paths and tabulating each price path’s outcome with a tiny square, you can skip drawing the price paths.
3337Subchapter
You can just simulate price‑path outcomes and tabulate each price‑path outcome with a tiny square.
When you simulate many price‑path outcomes and tabulate each one with a tiny square,
3338Subchapter
the tiny squares build a histogram.
A bell-shaped curve is also called a probability density function.
The area under the bell‑shaped curve represents the likelihood of different price‑path outcomes.
When you simulate enough price‑path outcomes for the tiny squares to fill the areas of your forecast bell‑shaped curves of expected continuously compounded return
and standard deviation of expected volatility,
3339Subchapter
the shape of the histogram approximates the bell shape of your forecast.
This is as it should be.
Why?
Because we are working with a circular relationship:
Your forecast drives the price‑path simulations.
Black‑Scholes options pricing theory assumes that stock prices evolve through a process called geometric Brownian motion.
To simulate each daily stock‑price change, the simulator
converts the investment‑horizon price forecast into a one‑day probability distribution and takes a random sample (pseudorandom sample, if you’re a purist) of that one‑day forecast.
At the end of the investment horizon, a tiny square tabulates the final outcome of each price path’s daily price‑changes.
Both your outline forecast and the histogram are probability distributions.
Both have the same expected return and standard deviation of volatility.
Both are drawn to the same scale.
Both have the same area.
Instead of or in addition to drawing your end‑of‑period forecast as an outline shape or building it as a histogram, we can
3340Subchapter
draw your forecast over its time horizon on the price axis as a price cone.
The simulator draws price cones out to four standard deviations.
With a price cone, there is a 99.7% probability
3341Subchapter
that a given price path will stay within three‑standard‑deviations of the median of the price cone.
In effect, a price cone is a wavefront of day‑by‑day end‑of‑period bell‑shaped curves.
Thus far, we’ve seen that, if, over a given time horizon, we have the current asset price of a stock, we have a forecast of expected return and standard deviation of volatility, then we can simulate potential price paths for the stock over the time horizon of our forecast.
Now, let’s introduce
3342Subchapter
a call option into our simulations.
Let’s introduce a call option that has a strike price of $16.00.
Black‑Scholes options pricing theory values what are called European options. European options are options that can be exercised only at expiration.
Our call option is a European option.
Our call option expires in one year (365 days).
The price of the call option is $1.0394.
In the simulator, the yellow horizontal line represents the strike price at $16.00.
The distance between the yellow horizontal line and the green horizontal line represents the $1.0394 price of the call option.
If, at the end of one year,
introduce_a_call_optionSubchapter
the market price of the underlying stock is less than or equal to the strike price (finishes out of the money), then the payoff of the call option is zero.
Here, the investor has a loss of $1.04— the amount of money that she or he paid for the call option.
When you lose all your money and you do your calculations in continuously compounded rates of return— also known as geometric rates of return— you have a loss of negative infinity.
if_call_finishes_out_of_the_moneySubchapter
If, at the end of one year, the market price of the underlying stock is greater than the strike price (finishes in the money) but the payoff of the call option is less than the cost of the call option, then the investor has a loss.
The loss is the difference between the payoff and the amount of money that the investor paid for the call call option.
call_in_the_money_but_a_lossSubchapter
If, at the end of one year, the market price of the underlying stock is greater than the strike price plus the cost of the call option, then the investor has a profit.
The profit is equal to the payoff minus the cost of the call option.
call_profit_equals_payoff_minus_price_of_call_optionSubchapter
As we’ve seen, a given forecast for a stock can produce many different price paths.
Different price paths for the underlying stock can produce radically different call‑option payoffs.
Different call option payoffs produce different returns on the investment in the call option.
Just as we can tabulate each price‑path outcome and each stock return with a tiny square, we can tabulate the return on each call‑option payoff with a tiny square.
for_different_price_paths_radically_different_call_payoffsSubchapter
Just as we can build a histogram of the end‑of‑period stock prices and build a histogram of the end‑of‑period stock returns, we can build a histogram of returns on investing in this call option.
if_we_simulate_many_call_option_payoffsSubchapter
The histogram of returns on investing in this call option takes on a shape that is radically different from the shape of the histograms for the underlying stock.
building_histogram_of_call_option_returnsSubchapter
In this simulation of 2,000 potential price‑path outcomes and their corresponding call‑option payoffs, only 16% of the price‑path outcomes produced a positive payoff.
Only 14% of the price‑path outcomes produced a call‑option payoff that was profitable.
Instead of simulating 2,000 random price‑path outcomes and tabulating with tiny squares the call‑option returns that those price‑path outcomes produce,
complete_histograms_for_stock_and_call_optionSubchapter
we can sweep through the forecasts for the underlying stock and, as we do so, tabulate with tiny squares the returns of investing in the option.
When the simulator sweeps through a forecast, it sweeps out to four standard deviations.
Here, the two tiny green squares at the tops of the probability distributions tabulate outcomes that are more than three standard deviations above the stock‑forecast median return.
The tiny amber squares above the forecast medians tabulate outcomes that are more than two and less than three standard deviations above the stock‑forecast median return.
sweep_call_option_forecast_three_four_sdsSubchapter
The tiny magenta squares above the forecast medians tabulate outcomes that are more than one and less than two standard deviations above the stock‑forecast median return.
The call option stike price of $16 is one standard deviation above the stock‑forecast median return.
Magenta squares above the green profit line produce payoffs that are greater than the cost of the call option. These payoffs produce a positive return on investing in the call option.
Magenta squares below the green profit line produce a payoff that is less than the cost of the call option. These payoffs produce a negative return on investing in the call option.
sweep_call_option_forecast_down_to_strikeSubchapter
As we continue to sweep through the stock forecasts, all the remaining stock‑price outcomes are less than the call‑option strike price of $16. The payoffs are zero. The call‑option geometric rates of return are negative infinity. In the call‑option probability distribution, these little squares get dumped into the negative‑infinity return bucket.
The tiny orange squares fill the area of the underlying stock’s bell‑shaped curves from one standard deviation above the forecast median to one standard deviation below the forecast median.
The magenta tiny squares below the stock‑forecast median returns are more than one and less than two standard deviations below the median.
The amber tiny squares below the stock‑forecast median returns are more than two and less than three standard deviations below the median.
The two green tiny squares below the stock‑forecast median returns are more than three standard deviations below the median.
sweep_call_option_forecast_through_zero_payoffsSubchapter
At long last, we are ready to address the question that Black‑Scholes options pricing theory addresses:
What is the value of this call option?
The financial value of any asset is the probability‑weighted present value
of the asset’s expected future cash flows.
To value this call option, we can sweep through the forecast for the underlying stock and calculate the cumulative probability‑weighted present value of all the option payoffs.
If we once again divide the area of the stock‑forecast probability distribution into 2,000 tiny squares,
to_value_this_call_option_calculate_pwpv_of_call_payoffsSubchapter
then the highest end‑of‑period stock price that we might expect would be $89.32.
An end‑of‑period stock price of $89.32 minus the strike price of $16.00 produces a payoff of $73.32.
Because we’ve divided the areas of our probability distributions into 2,000 tiny squares,
highest_end_of_period_stock_priceSubchapter
the probability of this payoff is 1 in 2,000 or 0.0005.
The probability‑weighted future value of this one payoff is $73.32 times 0.0005 which is $0.03666123.
This payoff is one year in the future. We want its value as of today.
To get this payoff’s value as of today,
p_of_this_call_option_payoffSubchapter
we divide the payoff’s future value by the exponent of our one‑year forecast’s expected continuously compounded rate of return.
The probability‑weighted present value of this one payoff is $0.02883211.
We’re going to perform these calculations for all 2,000 potential stock‑price outcomes and add up all the option payoffs’ probability‑weighted present values.
We save our first value
divide_call_option_payoff_by_exp_rSubchapter
in a bucket or register labeled cumulative probability‑weighted present value.
The second highest end‑of‑period stock price that we might expect
save_call_option_payoff_pvSubchapter
would be $72.25.
It produces a payoff of $56.25.
This payoff adds $0.02211960 to our option’s cumulative probability‑weighted present value.
second_highest_call_option_payoffSubchapter
The third highest end‑of‑period stock price that we might expect would be $65.06.
It produces a payoff of $49.06.
This payoff adds $0.01929092 to our option’s cumulative probability‑weighted present value.
third_highest_call_option_payoffSubchapter
As we continue to sweep through the forecasts for the underlying stock, each end‑of‑period stock price outcome that produces a positive payoff adds to the call option’s cumulative probability‑weighted present value.
continue_to_sweep_adds_to_call_valueSubchapter
As the end‑of‑period stock‑price outcomes get closer and closer to the call option’s strike price, the option payoffs grow smaller and smaller.
They add less and less value to the option’s cumulative probability‑weighted present value.
call_payoffs_grow_smaller_and_smallerSubchapter
An end‑of‑period stock price outcome of $16.02 adds only $0.00000721 to the option’s cumulative probability‑weighted present value.
two_cents_call_payoffSubchapter
Once the end‑of‑period stock price outcomes reach the strike price, the payoffs are zero. They add no additional value to the call option’s cumulative probability‑weighted present value.
call_payoff_at_strike_price_equals_zeroSubchapter
As we continue to sweep through the forecasts for the underlying stock, the cumulative probability‑weighted present value of the call option remains the same: $1.03733795.
sweep_below_strike_adds_zero_value_to_callSubchapter
This call option’s cumulative probability‑weighted present value, calculated in this way, is $1.03733795.
call_s_cumulative_pwpv_calculated_in_this_waySubchapter
The value of this call option is the cumulative probability‑weighted present value of all the option payoffs produced by the stock‑price outcomes that fill the area above the strike price in the price forecast for the underlying stock.
call_value_is_cumulative_pwpv_of_payoffs_above_strikeSubchapter
To calculate this value, we used numerical methods:
We divided the area of the stock‑price forecast into 2,000 tiny squares.
We calculated the probability‑weighted present value of the option payoff of each tiny square above the strike price in the stock‑price forecast.
We summed those probability‑weighted present values.
The Black‑Scholes formula for pricing European call options also calculates the cumulative probability‑weighted present value of all the option payoffs produced by the stock‑price outcomes that fill the area above the strike price in the price forecast for the underlying stock.
Except the Black‑Scholes forumla does not use numerical methods.
to_calculate_call_value_used_numerical_methodsSubchapter
The Black‑Scholes formula for valuing European options uses stochastic calculus.
Stochastic is the property of being well‑described by a random probability distribution such as a normal bell‑shaped curve.
Integral calculus evaluates areas under curves.
Stochastic integral calculus evaluates areas under probability distributions such as bell‑shaped curves.
The Black‑Scholes value of this call option is $1.039446.
Our numerical‑methods value of this call option is $1.03733795.
Stochastic integral calculus produces a more accurate value than does dividing the area of each probability distribution into 2,000 tiny squares and using numerical methods.
Had we divided the area of each probability distribution into, say, 10,000 tiny squares, then our numerical methods would have produced a value that was closer to the Black‑Scholes value.
Even so, with 2,000 tiny squares, both the numerical‑methods value and the Black‑Scholes value round to $1.04.
to_value_call_black_scholes_uses_stochastic_calculusSubchapter
Our stock forecast has an expected continuously compounded rate of return of 24.023%.
Black‑Scholes options pricing theory assumes that the expected return of every stock forecast is equal to the marketplace continuously compounded risk‑free rate of return.
In the Black‑Scholes formula, the continuously compounded risk‑free rate is represented by r.
In our calculations, we used the same continuously compounded rate of return for both the stock forecast’s expected return and the risk‑free rate— 24.023%.
In finance, “expected return” means forecast average return.
When a stock forecast uses geometric rates of return, the expected or average forecast return is not the median (middle) return.
The expected or average forecast return
= forecast median return + .5(standard deviation of volatility)²
In our example stock forecast,
Expected CC Return
= 0% + .5(69.315%)²
= 0% + .5(48.046%)
= 24.023%
to_value_call_expected_return_equals_rfrSubchapter to_value_call_black_scholes_uses_stochastic_calculus.png
What is the expected or forecast‑average return of our call option?
Even though the call‑option probability distribution has a shape that is radically different from the stock‑option forecast’s bell‑shaped curve, the forecast average return for our call option (and for every other option priced with the Black‑Scholes formula) is also equal to the risk‑free rate of return.
While many of the option payoffs produce a cash flow that is much greater than the one‑year price‑path gains in the stock price, 84% of this underlying stock’s price‑path outcomes produce an option payoff of $0.00.
The forecast average return for our call option averages in all those $0.00 payoffs.
call_option_expected_return_equals_rfrSubchapter sweep_call_option_forecast_through_zero_payoffs.png
We’ve shown that the Black‑Scholes value of a call option is the cumulative probability‑weighted present value of all the option payoffs produced by the stock‑price outcomes that fill the area above the strike price in the price forecast for the underlying stock.
If you make a market in stock options, then you may wish to know that the Black‑Scholes value of an option is also the cost to set up a delta hedge against your sale of an option.
Delta hedging allows market makers to offset risks that they expose themselves to when they sell an option.
call_black_scholes_value_is_cost_to_set_up_delta_hedgeSubchapter to_value_call_black_scholes_uses_stochastic_calculus.png
Now, let’s look at a put option and its valuation.
Let’s use the same underlying stock that we used for our call option.
Let’s use the same time horizon and forecast for the underlying stock.
Let’s value a put option that has a strike price of $4.00.
I’m guessing that you’re stepping through these pages as a learning exercise. To aid your learning, I repeat for the valuation of the put option a lot of what I said about the valuation of the call option.
Our put option is a European option.
Our put option expires in one year (365 days).
The price of the put option is $0.1374.
In the simulator, the yellow horizontal line represents the strike price at $4.00.
The distance between the yellow horizontal line and the green horizontal line represents the $0.1374 price of the put option.
If, at the end of one year,
introduce_a_put_optionSubchapter
the market price of the underlying stock is greater than or equal to the strike price (finishes out of the money), then the payoff of the option is zero.
Here, the investor has a loss of $0.14— the amount of money that she or he paid for the put option.
When you lose all your money and you do your calculations in continuously compounded rates of return— also known as geometric rates of return— you have a loss of negative infinity.
if_put_finishes_out_of_the_moneySubchapter
If, at the end of one year, the market price of the underlying stock is less than the strike price (finishes in the money) but the payoff of the put option is less than the cost of the option, then the investor has a loss.
The loss is the difference between the payoff and the amount of money that the investor paid for the put option.
put_in_the_money_but_a_lossSubchapter
If, at the end of one year, the market price of the underlying stock is less than the strike price minus the cost of the put option, then the investor has a profit.
The profit is equal to the payoff minus the cost of the option.
put_profit_equals_payoff_minus_price_of_put_optionSubchapter
As we’ve seen, a given forecast for a stock can produce many different price paths.
Different price paths for the underlying stock can produce radically different put‑option payoffs.
Different option payoffs produce different returns on the investment in the option.
Just as we can tabulate each price‑path outcome and each stock return with a tiny square, we can tabulate the return on each put‑option payoff with a tiny square.
for_different_price_paths_radically_different_put_payoffsSubchapter
Just as we can build a histogram of the end‑of‑period stock prices and build a histogram of the end‑of‑period stock returns, we can build a histogram of returns on investing in this put option.
The histogram of returns on investing in this put option takes on a shape that is radically different from the shape of the histograms for the underlying stock.
if_we_simulate_many_put_option_payoffsSubchapter
In this simulation of 2,000 potential price‑path outcomes and their corresponding put‑option payoffs, only 16% of the price‑path outcomes produced a positive payoff.
Only 15% of the price‑path outcomes produced an option payoff that was profitable.
Instead of simulating 2,000 random price‑path outcomes and tabulating with tiny squares the put option returns that those price‑path outcomes produce,
complete_histograms_for_stock_and_put_optionSubchapter
we can sweep through the forecasts for the underlying stock and, as we do so, tabulate with tiny squares the returns of investing in the put option.
we_can_sweep_put_option_forecast_four_sdsSubchapter
Here, the two tiny green squares at the bottoms of the probability distributions tabulate outcomes that are more than three standard deviations below the stock‑forecast median return.
The tiny yellow squares below the forecast medians tabulate outcomes that are more than two and less than three standard deviations below the stock‑forecast median return.
sweep_put_option_forecast_three_four_sdsSubchapter
The tiny blue squares below the forecast medians tabulate outcomes that are more than one and less than two standard deviations below the stock‑forecast median return.
The put option stike price of $4 is one standard deviation below the stock‑forecast median return.
Blue squares below the green profit line produce payoffs that are greater than the cost of the put option. These payoffs produce a positive return on investing in the put option.
Blue squares above the green profit line produce a payoff that is less than the cost of the put option. These payoffs produce a negative return on investing in the put option.
sweep_put_option_forecast_up_to_strikeSubchapter
As we continue to sweep up through the stock forecasts from bottom to top, all the remaining stock‑price outcomes are greater than the put‑option strike price of $4. The payoffs are zero. The put‑option geometric rates of return are negative infinity. In the put‑option probability distribution, these little squares get dumped into the negative‑infinity return bucket.
The tiny orange squares fill the area of the underlying stock’s bell‑shaped curves from one standard deviation below the forecast median to one standard deviation above the forecast median.
The blue tiny squares above the stock‑forecast median returns are more than one and less than two standard deviations above the median.
The yellow tiny squares above the stock‑forecast median returns are more than two and less than three standard deviations above the median.
The two green tiny squares above the stock‑forecast median returns are more than three standard deviations above the median.
sweep_put_option_forecast_through_zero_payoffsSubchapter
What is the value of this put option?
The financial value of any asset is the probability‑weighted present value
of the asset’s expected future cash flows.
Just as we did with a call option, to value this put option, we can sweep through the forecast for the underlying stock and calculate the cumulative probability‑weighted present value of all the put‑option payoffs.
If we once again divide the area of the stock‑forecast probability distribution into 2,000 tiny squares,
to_value_this_put_option_calculate_pwpv_of_put_payoffsSubchapter
then the lowest end‑of‑period stock price that we might expect would be $0.72.
A strike price of $4.00 minus an end‑of‑period stock price of $0.72 produces a payoff of $3.28.
Because we’ve divided the areas of our probability distributions into 2,000 tiny squares,
lowest_end_of_period_stock_priceSubchapter
the probability of this payoff is 1 in 2,000 or 0.0005.
The probability‑weighted future value of this one payoff is $3.28 times 0.0005 which is $0.00164175.
This payoff is one year in the future. We want its value as of today.
To get this payoff’s value as of today,
p_of_this_put_option_payoffSubchapter
we divide the payoff’s future value by the exponent of our one‑year forecast’s expected continuously compounded rate of return.
The probability‑weighted present value of this one payoff is $0.00129115.
We’re going to perform these calculations for all 2,000 potential stock‑price outcomes and add up all the put‑option payoffs’ probability‑weighted present values.
We save our first value
divide_put_option_payoff_by_exp_rSubchapter
in a bucket or register labeled cumulative probability‑weighted present value.
The second lowest end‑of‑period stock price that we might expect
save_put_option_payoff_pvSubchapter
would be $0.89
It produces a payoff of $3.11.
This payoff adds $0.00122458 to our put option’s cumulative probability‑weighted present value.
second_lowest_put_option_payoffSubchapter
The third lowest end‑of‑period stock price that we might expect would be $0.98.
It produces a payoff of $3.02.
This payoff adds $0.00118607 to our put option’s cumulative probability‑weighted present value.
third_lowest_put_price_path_outcomeSubchapter
As we continue to sweep up through the forecasts for the underlying stock, each end‑of‑period stock price outcome that produces a positive payoff adds to the put option’s cumulative probability‑weighted present value.
continue_to_sweep_adds_to_put_valueSubchapter
As the end‑of‑period stock‑price outcomes get closer and closer to the put option’s strike price, the option payoffs grow smaller and smaller.
They add less and less value to the option’s cumulative probability‑weighted present value.
put_payoffs_grow_smaller_and_smallerSubchapter
An end‑of‑period stock price outcome of $3.98 adds only $0.00000857 to the put option’s cumulative probability‑weighted present value.
two_cents_put_payoffSubchapter
Once the end‑of‑period stock price outcomes reach the strike price, the payoffs are zero. They add no additional value to the put option’s cumulative probability‑weighted present value.
(In the simulator displays, some dollar amounts are rounded to the nearest penny. Other dollar amounts are carried out to eight decimal places.)
put_payoff_at_strike_price_equals_zeroSubchapter
As we continue to sweep through the forecasts for the underlying stock, the cumulative probability‑weighted present value of the put option remains the same: $0.13742629.
sweep_above_strike_adds_zero_value_to_putSubchapter
This put option’s cumulative probability‑weighted present value, calculated in this way, is $0.13742629.
put_s_cumulative_pwpv_calculated_in_this_waySubchapter
The value of this put option is the cumulative probability‑weighted present value of all the option payoffs produced by the stock‑price outcomes that fill the area below the strike price in the price forecast for the underlying stock.
put_value_is_cumulative_pwpv_of_payoffs_below_strikeSubchapter
To calculate this value, $0.13742629, we used numerical methods:
We divided the area of the stock‑price forecast into 2,000 tiny squares.
We calculated the probability‑weighted present value of the put‑option payoff of each tiny square below the strike price in the stock‑price forecast.
We summed those probability‑weighted present values.
The Black‑Scholes formula for pricing European put options also calculates the cumulative probability‑weighted present value of all the option payoffs produced by the stock‑price outcomes that fill the area below the strike price in the price forecast for the underlying stock.
Except the Black‑Scholes forumla does not use numerical methods.
to_calculate_put_value_used_numerical_methodsSubchapter
The Black‑Scholes formula for valuing European put options uses stochastic calculus.
Stochastic is the property of being well‑described by a random probability distribution such as a normal bell‑shaped curve.
Integral calculus evaluates areas under curves.
Stochastic integral calculus evaluates areas under probability distributions such as bell‑shaped curves.
The Black‑Scholes value of this put option is $0.137388.
Our numerical‑methods value of this put option is $0.13742629.
Stochastic integral calculus produces a more accurate value than does dividing the area of each probability distribution into 2,000 tiny squares and using numerical methods.
Had we divided the area of each probability distribution into, say, 10,000 tiny squares, then our numerical methods would have produced a value that was closer to the Black‑Scholes value.
Even so, with 2,000 tiny squares, both the numerical‑methods value and the Black‑Scholes value round to the same value at four decimal places— $0.1374.
to_value_put_black_scholes_uses_stochastic_calculusSubchapter
Our stock forecast has an expected continuously compounded rate of return of 24.023%.
Black‑Scholes options pricing theory assumes that the expected return of every stock forecast is equal to the marketplace continuously compounded risk‑free rate of return.
In the Black‑Scholes formula, the continuously compounded risk‑free rate is represented by r.
In our calculations, we used the same continuously compounded rate of return for both the stock forecast and the risk‑free rate— 24.023%.
In finance, “expected return” means forecast average return.
When a stock forecast uses geometric rates of return, the expected or average forecast return is not the median (middle) return.
The expected or average forecast return
= forecast median return + .5(standard deviation of volatility)²
In our example stock forecast,
Expected CC Return
= 0% + .5(69.315%)²
= 0% + .5(48.046%)
= 24.023%
to_value_put_expected_return_equals_rfrSubchapter to_value_put_black_scholes_uses_stochastic_calculus.png
What is the expected or forecast‑average return of our put option?
Even though the put‑option probability distribution has a shape that is radically different from the stock‑option forecast’s bell‑shaped curve, the forecast average return for our put option (and for every other option priced with the Black‑Scholes formula) is also equal to the risk‑free rate of return.
While many of the option payoffs produce a cash flow that is much greater than the one‑year price‑path gains in the stock price, 84% of this underlying stock’s price‑path outcomes produce an option payoff of $0.00.
The forecast average return for our put option averages in all those $0.00 payoffs.
put_option_expected_return_equals_rfrSubchapter
The Black‑Scholes value of a put option is the cumulative probability‑weighted present value of all the option payoffs produced by the stock‑price outcomes that fill the area above the strike price in the price forecast for the underlying stock.
If you make a market in stock options, then you may wish to know that the Black‑Scholes value of an option is also the cost to set up a delta hedge against your sale of an option.
Delta hedging allows market makers to offset risks that they expose themselves to when they sell an option.
put_black_scholes_value_is_cost_to_set_up_delta_hedgeSubchapter to_value_put_black_scholes_uses_stochastic_calculus.png
You’ve seen that, in Black‑Scholes options pricing theory, stock prices evolve over time through a process called geometric Brownian motion.
(Here, we use a one‑year time horizon of 252 trading days per year.)
In a Black‑Scholes valuation, the expected return on a stock
ivol_geometric_brownian_motionSubchapter
is equal to the continuously compounded risk‑free rate.
In a Black‑Scholes valuation, over a given time horizon, a price forecast for a stock that pays no dividends consists of the current stock price, the marketplace risk‑free rate of interest, and the standard deviation of the volatility of the stock’s price.
Given this information,
ivol_stock_expected_return_equal_to_rfrSubchapter
you can draw a stock-price forecast as a bell-shaped curve on an axis of geometric returns, on an axis of lognormal prices, or on both.
Given this information
ivol_can_draw_price_forecasts_as_bell_shaped_curvesSubchapter
plus a strike price for a European call or put option,
ivol_given_forecast_plus_strike_can_calculate_option_valueSubchapter
you can use the mathematics of Black‑Scholes options pricing theory to calculate the value of the option.
In other words, if you have a price forecast for a stock, then you can use that forecast to calculate the Black‑Scholes values of options written on that stock.
Going in the other direction,
ivol_can_use_math_of_bsopt_to_calculate_option_valueSubchapter
if you know the current price of a stock that pays no dividends, know the marketplace risk‑free rate of interest, know the Black‑Scholes value of a European call or put option written on that stock for a given time to expiration; then you can use the mathematics of Black‑Scholes options pricing theory
ivol_going_other_way_if_you_know_black_scholes_valueSubchapter
to extract the standard deviation of price volatility that was used to calculate the option’s Black‑Scholes value. Price volatility extracted in such a way is called implied volatility.
In other words, if you know the Black‑Scholes value of an option written on a stock, then you can extract the price forecast for that stock.
ivol_use_bsopt_to_extract_implied_volSubchapter
You can draw that forecast as a bell-shaped curve.
Here, we have a stock that pays no dividends and is trading at $100.
The geometric or continuously compounded risk‑free rate is 5%
A call option with a strike price of $110 expires in one year (252 trading days).
The market price of the call option is $9.23.
This option price implies a stock‑price volatility of 36%.
Once you draw an implied forecast, you can decide whether or not you agree with that forecast.
ivol_use_ivol_to_draw_forecastsSubchapter
This option price implies that, at the end of one year, at three standard deviations from the forecast median, the stock price may be as high as $X or as low as $Z.
For whatever reason, you may believe that,
3383Subchapter
at the end of one year, the stock price is more likely to be somewhere between $360 and $35.
You can translate
ivol_you_may_believe_diffeerent_high_and_lowSubchapter
your expectations into a forecast.
You can
ivol_translate_your_expectations_into_a_forecastSubchapter
calculate the cumulative probability‑weighted present value of this one‑year call option based on your forecast.
Based on your forecast, this call option has a cumulative probability‑weighted present value of $15.76.
(To get the value of the option according to your forecast, the simulator discounts the future value of each option payoff at your forecast’s average return. You may wish to discount the future value of each option payoff at your cost of capital.)
ivol_calculate_option_s_cpwpv_based_on_your_forecastSubchapter
For the risk‑neutral or market‑equilibrium forecast that the price of the call option implies, the probability of profit is .23.
The probability of the option finishing in the money is .29.
The option’s expected return is 5.0% geometric which is equivalent to a 5.1% holding period return.
ivol_for_bs_forecast_er_popSubchapter
For your forecast, the probability of profit is .36.
The probability of the option finishing in the money is .43.
The option’s expected return is 72.7% geometric which is equivalent to a 106.8% holding‑period return.
Blah, blah, blah.
ivol_for_your_forecast_er_popSubchapter
Blah, blah, blah.
Blah, blah, blah.
Black‑Scholes options pricing theory makes a number of assumptions.
Some of these assumptions do not accurately characterize the trading of stock options in the financial marketplace.
Black‑Scholes options pricing theory values European-style options— options that can be exercised only at expiration.
Most stock options can be exercised at any time up to and at expiration.
Black‑Scholes options pricing theory assumes that the volatility of a stock’s price will not change over an option’s time to expiration.
A stock’s price volatility may change at any time.
Black‑Scholes options pricing theory assumes that a return forecast or price forecast for a stock is a normal bell-shaped curve drawn on an axis of geometric rates of return or drawn on a lognormal price axis.
Marketplace pricing data implies that stock forecasts drawn on these axes have tails that are fatter than the tails of normal bell-shaped curves.
Black‑Scholes options pricing theory assumes that stock prices evolve through a price process that is well characterized by geometric Brownian motion.
Geometric Brownian motion may not accurately characterize the process by which stock prices evolve.
Nonetheless, even with these inaccuracies, Black‑Scholes options pricing theory provides a useful way to think about stock options.
Just keep in mind that the assumptions that underlie Black‑Scholes options pricing theory are approximations— not precise characterizations of marketplace behavior.
Let’s look at one way that you might use Black‑Scholes options pricing theory to guide an investment decision.
Apple
$235
5% geometric rfr
Blah, blah, blah.
3390Subchapter
Blah, blah, blah.
If you wish to jump back and click through the screen captures again, click here.
Blah, blah, blah.
3391Subchapter
Under Black‑Scholes options pricing theory, that forecast for a stock is a bell‑shaped curve drawn on a lognormal price axis or a bell‑shaped curve drawn on an axis of continuously compounded rates of return.
When you simulate potential price paths of a stock, the simulator tabulates the price outcome of each price path with a tiny square.
When you simulate lots of price paths, the tiny squares build a histogram.
Under the math, assumptions, and relationships of Black‑Scholes options pricing theory, the shape of the histogram approximates the shape of the bell‑shaped curve drawn from the forecast.
If, into the simulator, you enter a call option’s time to expiration, price, and strike price, you can simulate potential payoffs of that call option.
If you sweep through all the price outcomes in the stock’s bell-shaped forecast, you can find the cumulative probability-weighted present value of all the potential option payoffs.
tabulates stock-price gains and losses with tiny squares. that, cumulatively, create a histogram.
When you simulate several thousand potential price paths, the histogram shows graphically that, over a given time horizon, every financial forecast, is a probability distribution.
In Black‑Scholes options pricing theory, over a given time horizon, stock-price gains and losses create a histogram that approximates a bell-shaped curve drawn on a log-normal price axis.
The value of any financial asset is the probability-weighted present value of the asset’s potential payoffs or cash flows.
The Black‑Scholes value of a call option on a stock is the probability weighted present value of all the option’s potential payoffs above the option’s strike price in the probability distribution of the underlying stock’s future prices.
Blah, blah, blah.
To accompany the simulator,
I wrote a book.
Of the book, a sophomore
at Tsinghua University in China
wrote to me,
“Your book is friendly
and easy to understand.
I like your writing style.
You express complex ideas
in easy words.”
Of the simulator and book,
an accountant wrote in an amazon review:
“Having a degree in mathematics
and a professional accountancy qualification
did not prepare me for the explanations
of Black Scholes to be found
in most text books.
“They may have gotten a Nobel Prize
for their option-pricing model
but Black and Scholes
were never going to get an award
for clarity of explanation.
“Having grappled
with this area for a few months,
I decided I needed
a little more innovative help;
hence my purchase
of Jerry Marlow’s interactive tutorial.
“Two days later, I feel I could go
for the next Nobel Prize myself!
“So many things click into place
so quickly, it’s marvelous.”
praise_for_bsmeSubchapter
Blah, blah, blah.
A real estate broker asked me
to write a speech
for him to give
at a real estate convention.
In the speech, I explained
how the brokers and agents
in the audience could best tap
into the global market
for luxury residential properties.
The day after he gave the speech,
I asked my client,
“How did it go?”
“Standing ovation!”
A year later, my client told me
that brokers who were in the audience
were still telling him
that his speech changed
how they sell luxury residential properties
to high‑net‑worth individuals
and families around the globe.
Through a tech contracting agency,
Citi hired me to create PowerPoint guides
to the tools, data flows,
and data-management capabilities
of Citi’s relational database
that stores trading and reference data
on Citi’s three-million-plus
capital-markets trades a day.
Systems managers and developers
were to write drafts.
I was to edit the drafts.
One systems manager dragged his feet
on sending me a draft.
To plead with this systems manager
to send me a draft,
my manager sent him this text message:
“Hari, I know you folks are drowned
in work to the head level every day.
“You don’t have much time
to spend on anything non-critical.
“The work Jerry does is marvelous.
“Users read the stuff and educate themselves.
“We don’t have to get on
to these stupid calls and explain
how our systems behave anymore.
“I feel it’s worth putting in
one extra weekend or night
and provide the documentation to Jerry.
“It pays every day on day to day basis.”
8651Subchapter
Sometimes I can be helpful
even when I don’t know
what I’m talking about.
During my gig at Citi Capital Markets,
one interaction that I initiated
illuminates an unorthodox way
in which a Chief Writing Officer
can create value in your organization.
Another systems manager was tardy
in sending me a draft of an overview
of the entire Citi Capital Markets
data-management process.
What to do?
How could I be helpful?
I’m not terribly shy.
So I decided that I would imagine
how the whole
three-million-plus-trades-a-day
data-management process
probably works
and write a draft of an overview
on that basis.
I wrote the draft.
Wherever I knew I needed a number
but didn’t know what the number was,
I inserted a blank.
When I finished the draft,
I sent it to the senior manager
who initiated the writing project
and to whom both I
and the tardy systems manager
ultimately reported.
The senior manager, in turn,
sent the draft
to the tardy systems manager
who was supposed to write the draft.
After the tardy systems manager
had read my imagined draft,
I spoke with him.
Here, as best I can recollect,
is what he said:
“First off, I could not believe
that someone would circulate a draft
that had so many mistakes in it.
“Then, as I corrected the mistakes,
I suddenly realized
how to write the document
that I had been struggling
to write for years—
but couldn’t get started on.
“I realized
that I should begin the story
where you started it—
at the trading desks.
“I should use the flow
that you established
in your draft.
“I could realize the potential of that flow!”
I’ve seen this predicament more than once.
An expert knows so much
about his or her area of expertise
that he or she cannot find
the beginning of the story.
The expert cannot find the flow.
If I can help your organization’s experts
find the beginning
and find the flow
for the stories that they want to tell,
then your experts likely will be able
to quickly write their stories
in ways that are not only marvelous
but that, in their lucidity
and in their usefulness,
are almost miraculous.
I_help_experts_find_their_flowSubchapter
Would you like for your organization
to enjoy the benefits
of writing
that is more precise, more accessible,
and more useful for the purpose at hand?
Would you like
for your organization’s writing
to bump up your organization’s energy level
and to encourage your employees to be
more courageous and more outspoken?
Would you like
for your organization’s writing
to allow your colleagues,
clients, and customers
to comprehend
your organization’s messages
more quickly, more easily,
and more accurately?
Would you like
for your organization’s writing
to have a more powerful flow?
If you would,
then I would like to help you
achieve your goals.
For a given writing assignment,
I can help you
and others in your organization
define your communication objectives:
Whom do you want to persuade
to do what?
Whom do you want to educate
how to do what?
To learn your organization’s subject matter,
I can review your organization’s
existing documentation.
I can interview your organization’s
in-house experts.
I can conduct additional research
on my own.
I can write a draft that plugs
into your intended readers’ knowledge level,
goals, objectives, concerns,
and decision criteria.
I can edit the writing of others
in ways that increase precision,
clarity, and energy.
I can manage drafts
through review and collaboration
in ways that are far more creative,
inspiring, productive, and audacious
than bogging people down
in “Track Changes.”
I can work with you
as a consultant,
on a project of multiple months duration,
or in a staff position.
Let me help you get
your colleagues, clients,
customers and investors
excited about your organization,
excited about your products,
excited about your services,
and excited about your mission.
If you are in a leadership position,
let’s get your audiences excited about you!
Let’s see if we can get you
a standing ovation!
Be in touch.
Or email jerrymarlow@jerrymarlow.com.
I look forward to speaking with you.
Jerry Marlow
Would_you_like_to_enjoy_the_benefits_of_high_quality_writingSubchapter
Nota bene
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