Secret 64UNRAVELING THE MYSTERIES OF BAYSIAN ANALYSIS

When you have a good feel for the future price action of the

underlying stock, you must convert that feeling into some concrete

figures that will tell you which option strategy to select in

order to be profitable in the options game. To do this, a short

course on probabilities and probability theory is necessary. Don’t

panic. We’ll make it an easy course, and ultimately the mysteries

of the Baysian Analysis will be unraveled.

First, we’re going to look at the area called subjective probabilities,

which really means a good guess on the odds of something

happening based on your intuition, knowledge and past

experiences.

For instance, when you decide on the probability

that the Green Bay Packers will beat the New Orleans Saints or

the chances of having a thunderstorm this afternoon, you are

using subjective probability. You are saying to yourself, “Well,

given what I know about the situation, I feel there is a 70%

chance that I’m going to need an umbrella today.”

Baysian Analysis converts intuitive feelings into concrete

numbers. For example, if you feel that an IBM July

60 call option for 3 ($300) with three months left in its

life will be profitable because you feel the IBM stock price will

move upward, how do you convert that into hard numbers?

First, let us establish a game plan where we will hold the

IBM July 60 call option until expiration. Using technical analysis,

combined with an ongoing analysis of the IBM fundamentals and

plenty of homework on the other aspects of the market, we decide

there is a 10% chance that IBM will be at 70 at the end of

July, a 20% chance it will be at 65, a 40% chance it will be at 63,

and a 30% chance that IBM will not be above 60 when the call

option expires.

How did we come up with these probabilities? In a sense

they were taken out of the air. Hopefully good homework on your

part will make these probabilities more than just guesswork. The

whole theory is based on taking your intuitive feeling, homework,

and analysis and putting them down on paper.

How do we use these subjective probabilities to identify the

profitability of our strategy? Let’s add one more feature in mapping

out this strategy. The profit or loss at each price level of the

stock is as follows:

IBM Stock Price Probability Profit or

When Option Expires Loss

70 10% +$700

65 20% +$200

63 40% 0

Below 60 30% (-$300)

Note: Commissions not included.

Now we are ready to gaze into the crystal ball and find what

the future holds. To do this, we will refer to the Baysian Decision

Rule. This rule will provide our answer to the future. Rather than

scare you with the formula, let’s walk through this procedure in

a nice and easy fashion.

First, let’s take the 30% probability of losing all of our investment

and multiply it times the $300 loss: ($300) Loss x 30%

Probability IBM is at or below 60 when option expires = ($90)

Loss.

Now let’s do the same with all the other probabilities and

profits or losses at each stock price level:

When IBM Stock Price is at 63:

0 Profit or Loss

40%

0

When IBM Stock Price is at 65:

$200 Profit

20%

$40

When IBM Stock Price is at 70:

$700 Profit

10%

$70

Now let’s add up all the results of these multiplications:

– 90 ................................IBM at 60 or lower

0 ................................IBM at 63

+ 40 ................................IBM at 65

+ 70 ................................IBM at 70

+ $20 Profit or Loss (Expected Value)

The result of this multiplication and addition process is

called our Expected Value—in layman’s terms, our potential

profit or loss. The profit or loss is the average profit or loss if we

were to enter the same exact strategy thousands of times and determine

the average return. In our example, the return on average

would be $20 in the long run for a $300 investment, and let’s

emphasize THE LONG RUN.

Now you have a clear picture of the profitability of the strategy

that initially looked pretty lucrative. Once you laid it out on

paper and applied the Baysian Decision Rule, however, your long

run profitability looks very thin.

This procedure, which takes only a few minutes to complete,

can give you an invaluable glance at the future. Again, remember

the subjective probability must be developed through your own

analysis of potential stock prices.

In order to be successful using subjective probability, you

have to take a realistic look at the stock or futures price action

and not let emotion and enthusiasm for the stock or futures

cloud your judgment. Now with a probability calculator and a

simulator, you can get much more concrete numbers to carry

out this analysis.

Altogether, the mysteries of Baysian Analysis have, I hope,

been unraveled, and you can find its magic helpful.

When you have a good feel for the future price action of the

underlying stock, you must convert that feeling into some concrete

figures that will tell you which option strategy to select in

order to be profitable in the options game. To do this, a short

course on probabilities and probability theory is necessary. Don’t

panic. We’ll make it an easy course, and ultimately the mysteries

of the Baysian Analysis will be unraveled.

First, we’re going to look at the area called subjective probabilities,

which really means a good guess on the odds of something

happening based on your intuition, knowledge and past

experiences.

For instance, when you decide on the probability

that the Green Bay Packers will beat the New Orleans Saints or

the chances of having a thunderstorm this afternoon, you are

using subjective probability. You are saying to yourself, “Well,

given what I know about the situation, I feel there is a 70%

chance that I’m going to need an umbrella today.”

Baysian Analysis converts intuitive feelings into concrete

numbers. For example, if you feel that an IBM July

60 call option for 3 ($300) with three months left in its

life will be profitable because you feel the IBM stock price will

move upward, how do you convert that into hard numbers?

First, let us establish a game plan where we will hold the

IBM July 60 call option until expiration. Using technical analysis,

combined with an ongoing analysis of the IBM fundamentals and

plenty of homework on the other aspects of the market, we decide

there is a 10% chance that IBM will be at 70 at the end of

July, a 20% chance it will be at 65, a 40% chance it will be at 63,

and a 30% chance that IBM will not be above 60 when the call

option expires.

How did we come up with these probabilities? In a sense

they were taken out of the air. Hopefully good homework on your

part will make these probabilities more than just guesswork. The

whole theory is based on taking your intuitive feeling, homework,

and analysis and putting them down on paper.

How do we use these subjective probabilities to identify the

profitability of our strategy? Let’s add one more feature in mapping

out this strategy. The profit or loss at each price level of the

stock is as follows:

IBM Stock Price Probability Profit or

When Option Expires Loss

70 10% +$700

65 20% +$200

63 40% 0

Below 60 30% (-$300)

Note: Commissions not included.

Now we are ready to gaze into the crystal ball and find what

the future holds. To do this, we will refer to the Baysian Decision

Rule. This rule will provide our answer to the future. Rather than

scare you with the formula, let’s walk through this procedure in

a nice and easy fashion.

First, let’s take the 30% probability of losing all of our investment

and multiply it times the $300 loss: ($300) Loss x 30%

Probability IBM is at or below 60 when option expires = ($90)

Loss.

Now let’s do the same with all the other probabilities and

profits or losses at each stock price level:

When IBM Stock Price is at 63:

0 Profit or Loss

40%

0

When IBM Stock Price is at 65:

$200 Profit

20%

$40

When IBM Stock Price is at 70:

$700 Profit

10%

$70

Now let’s add up all the results of these multiplications:

– 90 ................................IBM at 60 or lower

0 ................................IBM at 63

+ 40 ................................IBM at 65

+ 70 ................................IBM at 70

+ $20 Profit or Loss (Expected Value)

The result of this multiplication and addition process is

called our Expected Value—in layman’s terms, our potential

profit or loss. The profit or loss is the average profit or loss if we

were to enter the same exact strategy thousands of times and determine

the average return. In our example, the return on average

would be $20 in the long run for a $300 investment, and let’s

emphasize THE LONG RUN.

Now you have a clear picture of the profitability of the strategy

that initially looked pretty lucrative. Once you laid it out on

paper and applied the Baysian Decision Rule, however, your long

run profitability looks very thin.

This procedure, which takes only a few minutes to complete,

can give you an invaluable glance at the future. Again, remember

the subjective probability must be developed through your own

analysis of potential stock prices.

In order to be successful using subjective probability, you

have to take a realistic look at the stock or futures price action

and not let emotion and enthusiasm for the stock or futures

cloud your judgment. Now with a probability calculator and a

simulator, you can get much more concrete numbers to carry

out this analysis.

Altogether, the mysteries of Baysian Analysis have, I hope,

been unraveled, and you can find its magic helpful.