978-1259720697 Chapter 8 Lecture Note

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Chapter 8

Behavioral Finance and the

Psychology of Investing

Slides

8-1. Chapter 8

8-2. Behavioral Finance and the Psychology of Investing

8-3. Learning Objectives

8-4. Behavioral Finance, Introduction

8-5. Behavioral Finance, Definition

8-6. Three Economic Conditions that Lead to Market Efficiency

8-7. Prospect Theory

8-8. Investor Behavior Consistent with Prospect Theory Predictions

8-9. Frame Dependence, I.

8-10. Frame Dependence, II.

8-11. Frame Dependence, III.

8-12. Frame Dependence, IV.

8-13. Anchoring and Loss Aversion

8-14. Do You Suffer from “Get-Evenitis?” Part I.

8-15. Do You Suffer from “Get-Evenitis?” Part II.

8-16. Do You Suffer from “Get-Evenitis?” Part III.

8-17. Mental Accounting

8-18. The House Money Effect, I.

8-19. The House Money Effect, II.

8-20. The House Money Effect, III.

8-21. Overconfidence: A Significant Error in Investor Judgment

8-22. Overconfidence and Portfolio Diversification

8-23. Overconfidence and Trading Frequency, I.

8-24. Overconfidence and Trading Frequency, II. Is Overtrading “a Guy Thing?”

8-25. The Illusion of Knowledge

8-26. The Snakebite Effect

8-27. Misperceiving Randomness and Overreacting to Chance Events

8-28. A Coin Flipping Experiment

8-29. A Coin Flipping Experiment, Graphed

8-30. The Hot-Hand Fallacy, I.

8-31. The Hot-Hand Fallacy, II.

8-32. The Hot-Hand Fallacy, III.

8-33. The Gambler’s Fallacy

8-34. Heuristics

8-35. Herding, I.

8-36. Herding, II.

8-37. Overcoming Bias

8-38. Sentiment-Based Risk and Limits to Arbitrage, I.

8-39. Sentiment-Based Risk and Limits to Arbitrage, II.

8-40. Sentiment-Based Risk and Limits to Arbitrage, III.

8-41. Sentiment-Based Risk and Limits to Arbitrage, IV.

8-42. Sentiment-Based Risk and Limits to Arbitrage, V.

8-43. Technical Analysis

8-44. Why Does Technical Analysis Continue to Thrive?

8-45. The Market Sentiment Index, I.

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8-46. The Market Sentiment Index, II.

8-47. Dow Theory

8-48. Elliott Waves, I

8-49. Elliott Waves, II

8-50. Support and Resistance Levels

8-51. Market Diaries, A Collection of Technical Indicators

8-52. Technical Indicators, Notes

8-53. Relative Strength

8-54. Charting

8-55. Charting: Open-High-Low-Close (OHLC)

8-56. Charting: Price Channels

8-57. Charting: Head and Shoulders

8-58. Charting: Moving Averages

8-59. Home Depot (HD) Moving Average

8-60. Other Technical Indicators

8-61. Useful Internet Sites

8-62. Chapter Review, I.

8-63. Chapter Review, II.

Chapter Organization

8.1 Introduction to Behavioral Finance

8.2 Prospect Theory

A. Frame Dependence

B. Loss Aversion

C. Mental Accounting and House Money

8.3 Overconfidence

A. Overconfidence and Trading Frequency

B. Overtrading and Gender: “It’s (Basically) a Guy Thing”

C. What is a Diversified Portfolio to the Everyday Investor?

D. Illusion of Knowledge

E. Snakebite Effect

8.4 Misperceiving Randomness and Overreacting to Chance Events

A. The “Hot-Hand” Fallacy

B. The Gambler’s Fallacy

8.5 More on Behavioral Finance

A. Heuristics

B. Herding

C. How Do We Overcome Bias?

8.6 Sentiment-Based Risk and Limits to Arbitrage

A. Limits to Arbitrage

B. The 3COM/Palm Mispricing

C. The Royal Dutch/Shell Price Ratio

8.7 Technical Analysis

A. Why does Technical Analysis Continue to Thrive?

B. Dow Theory

C. Elliott Waves

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D. Support and Resistance Levels

E. Technical Indicators

F. Relative Strength Charts

G. Charting

H. Fibonacci Numbers

I. Other Technical Indicators

8.8Summary and Conclusions

Selected Web Sites

www.tim-richardson.net/misc/estimation_quiz.html (self-test for

overconfidence)

www.behaviouralfinance.net (terms and concepts of behavioral finance)

www.dowtheory.com (more information about Dow theory)

www.thedowtheory.com (more information about Dow theory)

www.elliottwave.com (more information about the Elliott wave)

www.stockcharts.com (information on charting – select “Chart School”)

More technical analysis charts and explanations:

bigcharts.marketwatch.com

www.incrediblecharts.com

www.psychonomics.com (see research section on behavioral finance and

building portfolios)

www.chartsmart.com

finance.yahoo.com

english.borsanaliz.com (triangles)

www.marketvolume.com (for information on market volume importance)

Annotated Chapter Outline

8.1 Introduction to Behavioral Finance

Be honest: Do you think of yourself as a better than average driver? If you do,

you are not alone. About 80 percent of the people who are asked this question

will say “yes.” Evidently, we tend to overestimate our abilities behind the wheel. Is

the same thing true when it comes to making investment decisions?

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How errors in judgment, and other aspects of human behavior, affect investors

and asset prices falls under the general heading of “behavioral finance.” Errors in

reasoning are often called cognitive errors. Some proponents of behavioral

finance believe that cognitive errors by investors will cause market inefficiencies.

8.2 Prospect Theory

Prospect theory, developed in the late 1970s, is a collection of ideas that

provides an alternative to classical, rational economic decision making. The

foundation of prospect theory rests on the idea that investors are much more

distressed by prospective losses than they are happy about prospective gains.

Investors seem to be willing to take more risk to avoid the loss of a dollar than

they are to make a dollar profit. Also, if an investor has the choice between a

sure gain and a gamble that could increase or decrease the sure gain, the

investor is likely to choose the sure gain.

A. Frame Dependence

If an investment problem is presented in two different (but really equivalent)

ways, investors often make inconsistent choices. That is, how a problem is

described, or framed, seems to matter to people. Some people believe that

frames are transparent; that is, investors should be able to see through the way

the question is asked.

An example of this is the change in 401(k) plans that allowed employers to

automatically enroll participants, while allowing them to opt out. The employees

still had the same choice (participate or not), but participation significantly

increased with the change.

B. Loss Aversion

When you add a new stock to your portfolio, it is human nature for you to

associate the stock with its purchase price. As the price of the stock changes

through time, you will have unrealized gains or losses when you compare the

current price to the purchase price.

Through time, you will mentally account for these gains and losses, and how you

feel about the investment depends on whether you are ahead or behind. This

behavior is known as mental accounting. (See more on this in the next section.)

When you engage in mental accounting, you unknowingly have a personal

relationship with each of your stocks. As a result, selling one of them becomes

more difficult. It is as if you have to “break up” with this stock, or “fire” it from your

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portfolio. As with personal relationships, these “stock relationships” can be

complicated and, believe it or not, make selling stocks difficult at times.

In fact, you may have particular difficulty selling a stock at a price lower than your

purchase price. If you sell a stock at a loss, you may have a hard time thinking

that purchasing the stock in the first place was correct. You may feel this way

even if the decision to buy was actually a very good decision. A further

complication is that you will also think that if you can just somehow “get even,”

you will be able to sell the stock without any hard feelings. This phenomenon is

known as loss aversion, which is the reluctance to sell investments such as

shares of stock after they have fallen in value. Loss aversion is also called the

“break-even” or “disposition effect,” and those suffering from it are sometimes

said to have “get-evenitis.”

C. Mental Accounting and House Money

Do you treat bonuses different than regular income? Do you invest differently in

your IRA than in other accounts (for non-tax related reasons)? If so, you may be

exhibiting mental accounting.

Casinos in Las Vegas (and elsewhere) know all about a concept called “playing

with house money.” The casinos have found that gamblers are far more likely to

take big risks with money that they have won from the casino (i.e., the “house

money”). Also, casinos have found that gamblers are not as upset about losing

house money as they are about losing the money they brought with them to

gamble.

It may seem natural for you to feel that some money is precious because you

earned it through hard work, sweat, and sacrifice, whereas other money is less

precious because it came to you as a windfall. But these feelings are plainly

irrational because any dollar you have buys the same amount of goods and

services no matter how you obtained that dollar. The lessons are:

Lesson One. There are no “paper profits.” Your profits are yours.

Lesson Two. All your money is your money. That is, you should not

separate your money into bundles labeled “house money” and “my

money.”

8.3 Overconfidence

A serious error in judgment you can make as an investor is to be overconfident.

We are all overconfident about our abilities in many areas (recall our question

about your driving ability at the beginning of the chapter).

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Concerning investment behavior, the classic example is diversification, or the

lack of it. Investors tend to invest too heavily in the company for which they work.

When you think about it, this loyalty can be very bad financially. This is because

both your earning power (your income) and your retirement nest egg depend on

one company.

A. Overconfidence and Trading Frequency

If you are overconfident about your investment skill, you are likely to trade too

much. Researchers have investigated this and the moral is clear: excessive

trading is hazardous to your wealth.

B. Overtrading and Gender: “It’s (Basically) a Guy Thing”

In a very clever study published in 2001, Professors Brad Barber and Terrance

Odean examined the effects of overconfidence. Possible effects of

overconfidence are that it leads to more trading, which in turn leads to lower

returns.

If investors could be divided into groups that differed in overconfidence, then

these effects could be examined. Barber and Odean use the fact that

psychologists have found that men are more overconfident than women in the

area of finance.

Barber and Odean find that men trade about 50 percent more than women. They

find that both men and women reduce their portfolio returns through excessive

trading. However, men do so by 94 basis points more per year than women. The

difference is even bigger between single men and single women. Single men

trade 67 percent more than single women, and single men reduce their return by

144 basis points compared to single women.

Using four risk measures, and accounting for the effects of marital status, age,

and income, Professors Barber and Odean also find that men invested in riskier

portfolios than women.

C. What is a Diversified Portfolio to the Everyday Investor?

It is clear to researchers that most investors have a poor understanding of what

constitutes a well-diversified portfolio. Researchers have discovered that the

average number of stocks in a household portfolio is about four, and the median

is about three.

D. Illusion of Knowledge

Overconfidence is often related to a belief that your own information is superior to

that held by others. This is referred to as the illusion of knowledge.

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E. Snakebite Effect

The opposite of overconfidence is the snakebite effect, which refers to the

unwillingness of investors to take a risk following a loss.

8.4 Misperceiving Randomness and Overreacting to Chance Events

Cognitive psychologists have discovered that the human mind is a pattern-

seeking device. As a result, we can conclude that causal factors or patterns are

at work behind sequences of events even when the events are truly random. In

behavioral finance, this is known as the representativeness heuristic, which says

that if something is random, it should look random. This bias often manifests

itself in investors mistaking good companies for good investments.

A. The “Hot-Hand” Fallacy

Basketball fans generally believe that success breeds success, i.e., players have

a “hot hand.” But, researchers have found that the hot hand is an illusion. That is,

players really do not deviate much from their long-run shooting averages,

although fans, players, announcers, and coaches think they do.

Lecture Tip: A more recent study may support the existence of a hot-hand. See

https://www.gsb.stanford.edu/insights/jeffrey-zwiebel-why-hot-hand-may-be-real-

after-all.

The clustering illusion is our human belief that random events that occur in

clusters are not really random.

B. The Gambler’s Fallacy

People commit the gambler’s fallacy when they assume that a departure from

what occurs on average, or in the long run, will be corrected in the short run.

Another way to think about the gambler’s fallacy is that because an event has not

happened recently, it has become “overdue” and is more likely to occur.

People sometimes refer (wrongly) to the “law of averages” in such cases.

Roulette is a random gambling game where gamblers can make various bets on

the spin of the wheel. There are 38 numbers on an American roulette table, two

green ones, 18 red ones, and 18 black ones. One possible bet is to bet whether

the spin will result in a red number or in a black number. Suppose a red number

has appeared five times in a row. Gamblers will often become confident that the

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next spin will be black, when the true chance remains at about 50 percent (of

course, it is exactly 18 in 38).

8.5 More on Behavioral Finance

A. Heuristics

With all the information available, we must find some way to make an informed

decision, knowing that we can never possibly evaluate all possible data.

Heuristics, or rules of thumb, allow us to do this. Unfortunately, investors often

choose subjective criteria for these rules.

B. Herding

Herding is when investors tend to follow one another, similar to a school of fish.

This has been documented among individual investors and analysts, particularly

as it relates to earnings forecasts and ratings.

C. How Do We Overcome Bias?

The most important thing is to know what potential biases exist. Beyond that,

diversify, avoid situations (or media) that unduly influence you, and create

objective criteria.

8.6 Sentiment-Based Risk and Limits to Arbitrage

It is important to realize that the efficient markets hypothesis does not require

every investor to be rational. As we have noted, all that is required for a market to

be efficient is that at least some investors are smart and well-financed. These

investors are prepared to buy and sell to take advantage of any mispricing in the

marketplace. This activity is what keeps markets efficient. Sometimes, however,

a problem arises in this context.

A. Limits to Arbitrage

The term limits to arbitrage refers to the notion that under certain circumstances,

rational, well-capitalized traders may be unable to correct a mispricing, at least

not quickly. The reason is that strategies designed to eliminate mispricings are

often risky, costly, or somehow restricted. Three important such problems are:

Firm-specific risk. This issue is the most obvious risk facing a would-be

arbitrageur. Suppose that you believe that the observed price on General

Motors stock is too low, so you purchase many, many shares. Then, some

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unanticipated negative news drives the price of General Motors stock

even lower.

Noise trader risk. A noise trader is someone whose trades are not based

on information or financially meaningful analysis. Noise traders could, in

principle, act together to worsen a mispricing in the short run. Noise trader

risk is important because the worsening of a mispricing could force the

arbitrageur to liquidate early and sustain steep losses. Noise trader risk is

also called sentiment-based risk.

Implementation costs. These costs include transaction costs such as bid-

ask spreads, brokerage commissions, and margin interest. In addition,

there might be some short-sale constraints.

B. The 3Com/Palm Mispricing

On March 2, 2000, 3Com sold 5 percent of one of its subsidiaries to the public

via an IPO. At the time, the subsidiary was known as Palm. 3Com planned to

distribute the remaining Palm shares to 3Com shareholders at a later date.

Under the plan, if you owned 1 share of 3Com, you would receive 1.5 shares of

Palm. So, after 3Com sold part of Palm via the IPO, investors could buy Palm

shares directly, or they could buy them indirectly by purchasing shares of 3Com.

What makes this case interesting is what happened in the days that followed the

Palm IPO. If you owned one 3Com share, you would be entitled, eventually, to

1.5 shares of Palm. Therefore, each 3Com share should be worth at least 1.5

times the value of each Palm share. We say “at least” because the other parts of

3Com were profitable.

As a result, each 3Com share should have been worth much more than 1.5 times

the value of one Palm share. But, things did not work out this way. The day

before the Palm IPO, shares in 3Com sold for $104.13. After the first day of

trading, Palm closed at $95.06 per share. Multiplying $95.06 by 1.5 results in

$142.59, which is the minimum value one would expect to pay for 3Com. But the

day Palm closed at $95.06, 3Com shares closed at $81.81, more than $60 lower

than the price implied by Palm.

A 3Com price of $81.81 when Palm is selling for $95.06 implies that the market

values the rest of 3Com’s businesses (per share) at $81.81 − $142.59 = −$60.78.

Given the number of 3Com shares outstanding at the time, this means the

market placed a negative value of about −$22 billion for the rest of 3Com’s

businesses. Of course, a stock price cannot be negative. This means, then, that

the price of Palm relative to 3Com was much too high.

To profit from this mispricing, investors would purchase shares of 3Com and

short shares of Palm. In a well-functioning market, this action would force the

prices into alignment quite quickly.

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But, the non-Palm part of 3Com had a negative value for about two months, from

March 2, 2000, until May 8, 2000. Even then, it took approval by the IRS for

3Com to proceed with the planned distribution of Palm shares before the non-

Palm part of 3Com once again had a positive value.

C. The Royal Dutch/Shell Price Ratio

Another fairly well known example of a mispricing involves two large oil

companies. In 1907, Royal Dutch of the Netherlands and Shell of the United

Kingdom agreed to merge their business enterprises and pay dividends on a 60-

40 basis. So, whenever the stock prices of Royal Dutch and Shell are not in a 60-

40 ratio, there is a potential opportunity to make an arbitrage profit. If, for

example, the ratio were 50-50, you would buy Royal Dutch, and short sell Shell.

In the textbook, we plot the daily deviations from the 60-40 ratio of the Royal

Dutch price to the Shell price. If the prices of Royal Dutch and Shell are in a 60-

40 ratio, there is a zero percentage deviation. If the price of Royal Dutch is too

high compared to the Shell price, there is a positive deviation. If the price of

Royal Dutch is too low compared to the price of Shell, there is a negative

deviation. There have been large and persistent deviations from the 60-40 ratio.

In fact, the ratio was seldom at 60-40 for most of the time from 1962 through mid-

2005 (when the companies merged).

8.7 Technical Analysis

Technical analysis: Techniques for predicting market direction based on

(1) historical price and volume behavior and (2) investor sentiment.

The previous chapters discussed fundamental analysis; this chapter now

addresses technical analysis. Technical analysts search for bullish or bearish

signals, or indicators, about stock prices and market direction. The field of

technical analysis is huge, with many books written on the subject. This chapter

just touches on the highlights of some of the methods. Technical analysis is also

very popular in futures markets. Therefore, much of this discussion also applies

to those markets.

A. Why does Technical Analysis Continue to Thrive?

One possible reason that technical analysis still exists is that an investor can

derive thousands of successful technical analysis systems by using historical

security prices. Past movements of security prices are easy to fit into a wide

variety of technical analysis systems. As a result, proponents of technical

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analysis can continuously tinker with their systems and find methods that fit

historical prices. This process is known as “backtesting.” Alas, successful

investment is all about future prices.

Another possible reason that technical analysis still exists is simply that it

sometimes works. Again, given a large number of possible technical analysis

systems, it is possible that many of them will work (or appear to work) in the short

run.

The Market Sentiment Index (MSI) is an example of a contrarian technical

analysis tool. This index is the ratio of the number of bearish investors to the total

number of investors. The MSI has a maximum value of 1.00, and a minimum

value of 0.00.

The following saying is useful when you are trying to remember how to interpret

the MSI: “When the MSI is high, it is time to buy; when the MSI is low, it is time to

go.” Note that there is no theory to guide investors as to what level of the MSI is

“high” and what level is “low.” This lack of precise guidance is a common problem

with a technical indicator like the MSI.

B. Dow Theory

Dow Theory: This is a method to predict market direction. It relies on the

Dow Industrial and the Dow Transportation averages.

The basis of the Dow Theory is that there are three forces at work in the stock

market:

Primary direction or trend

Secondary reaction or trend

Daily fluctuations

This theory indicates that the primary direction is either bullish or bearish, and it

reflects the long-run direction of the market. The secondary reactions are

temporary departures from the primary direction, and the daily fluctuations are

just noise and can be ignored. When the DJIA and DJTA are taken together, the

two indexes can confirm each other and the primary trend, or depart from each

other and be interpreted as a secondary reaction. Even though this method is not

as popular today, its basic principles underlie many modern approaches to

technical analysis.

C. Elliott Waves

In the early 1930s, an accountant named Ralph Nelson Elliott developed the

Elliott wave theory. Elliott discovered what he believed to be a persistent and

recurring pattern that operated between market tops and bottoms. His theory was

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that these patterns, which he called “waves,” collectively expressed investor

sentiment. Through use of sophisticated measurements that he called “wave

counting,” a wave theorist could forecast market turns with a high degree of

accuracy.

Elliott’s main theory was that there was a repeating eight-wave sequence. The

first five waves, which he called “impulsive,” were followed by a three-wave

“corrective” sequence. The impulse waves are labeled numerically, 1 through 5,

while the corrective waves are labeled A, B, and C. The basic Elliott wave theory

gets very complicated because, under the theory, each wave can subdivide into

finer wave patterns that are classified into a multitude of structures.

Notwithstanding the complex nature of the Elliott wave theory, it is still a widely

followed indicator.

D. Support and Resistance Levels

Support level: Price or level below which a stock or the market as a

whole is unlikely to fall.

Resistance level: Price or level above which a stock or the market as a

whole is unlikely to rise.

The basic idea is that most stocks have a support level (price the stock is unlikely

to go below) and a resistance level (price the stock is unlikely to go above),

which can also be viewed as psychological barriers. When a stock goes down to

a minimum level, the “bargain hunters” will view the stock as “cheap” and buy the

stock, thereby supporting the price. When a stock goes up to a maximum level,

investors are likely to consider it “topped out” and sell their stock, which will slow

down the price increase.

E. Technical Indicators

There are many technical indicators available to analysts, including: the

advance/decline line, the closing tick, the closing arms (Trin), and block trades.

The advance/decline line shows the cumulative difference between advancing

issues and declining issues. For example, Table 8.3 contains advance and

decline information for the October 12, 2015, to October 16, 2015, trading week.

The closing tick is the difference between the number of shares that closed on an

uptick, and those that closed on a downtick. The closing arms is the ratio of

average trading volume in declining issues to average trading volume in

advancing issues. Block trades refers to trades in excess of 10,000 shares.

Lecture Tip: The closing arms, or Trin, is an interesting indicator to discuss. One

easy way for students to remember Trin is from the acronym tr(end) in(dicator). It

is useful to do a numerical example of the Trin because we think of declines over

advances, but tend to forget the volume in the formula:

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Arms = (Declining volume / Declines) / (Advancing volume / Advances)

A caveat: some sources reverse the numerator and the denominator when they

calculate this ratio.

If you follow the Closing Arms, you will see that it is a very volatile indicator.

F. Relative Strength Charts

Relative strength: A measure of the performance of one investment

relative to another.

Relative strength charts measure the performance of one investment or market

relative to another. A common technique is to review how a stock did relative to

its industry or the market as a whole.

G. Charting

Technical analysts rely heavily on charts. They study past market prices or

information, looking for trends or indicators that may signal the direction of the

market or a particular stock. There are many charting techniques—this chapter

reviews four of them.

Open-High-Low-Close Charts (OHLC)

Open-High-Low-Close chart: Plot of high, low, and closing prices

An open-high-low-close chart is a bar chart showing the opening, high price, low

price, and closing price each day for a stock or index. The Wall Street Journal

presents these charts daily for the Dow averages. Technical analysts use these

charts to look for patterns.

A candlestick chart is an extension, where boxes are drawn to connect the

opening and closing prices. Clear boxes represent up days, and darkened boxes

represent down days.

Chart Formations

There are hundreds of chart formations and patterns that chartists look for. Two

of the most popular are “price channels” and the “head-and-shoulders” pattern.

The real difficulty with using these chart formations is trying to identify the

patterns, then interpreting what they actually suggest.

Price Channel

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The price channel is a chart pattern using OHLC data that can slope upward,

downward, or sideways.

Head and Shoulders

This chart pattern belongs to a group of price charts known as reversal patterns.

These chart patterns signal that a reversal from the main trendline is possibly

going to occur.

Moving Average Charts.

Moving average: An average daily price or index level, calculated using a fixed

number of previous days’ prices or levels, updated each day.

Moving averages are used to identify short- and long-term trends. The moving

average lines can be computed for various time periods and compared to a

graph of the actual stock prices, or to other moving averages for the stock.

If the shorter-term moving averages crosses through the longer-term moving

average to the upside, this is referred to as a Golden Cross. This occurrence is

considered to be a strong bullish indicator. A Death Cross occurs when the

shorter-term moving average crosses the longer-term average to the downside.

Given its name, you can probably guess that this is a bearish indicator.

Lecture Tip: A fun exercise for students is to gather stock price data for a

company, download it into a spreadsheet, compute several moving averages,

and then graph the results. If the students collect daily stock prices, they can

compute 30-day and 90-day moving averages. Then, they can plot these moving

averages against the daily stock prices. It is very interesting to discuss the

interpretation of these moving average graphs, and it’s an easy way for the

students to become involved in technical analysis.

Bollinger Bands

John Bollinger created Bollinger bands in the early 1980s. The purpose of

Bollinger bands is to provide relative levels of high and low prices. Bollinger

bands represent a 2-standard deviation bound calculated from the moving

average (this is why Bollinger bands do not remain constant). In the Microsoft

example, the Bollinger bands surround a 20-day moving average. The Bollinger

bands are the maroon bands that appear in the top chart of Figure 8.9. Bollinger

bands have been interpreted in many ways by their users. For example, when

the stock price is relatively quiet, the Bollinger bands are tight, which indicates a

possible pent-up tension that must be released by a subsequent price

movement.

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MACD

MACD stands for moving average convergence divergence. The MACD indicator

shows the relationship between two moving averages of prices. The MACD is

derived by dividing one moving average by another and then comparing this ratio

to a third moving average, the signal line. In the Microsoft example, the MACD

uses a 12-day and a 26-day moving average and a 9-day signal line. The

convergence/divergence of these three averages is represented by the solid

black bars in the third chart of Figure 8.9. The basic MACD trading rule is to sell

when the MACD falls below its signal line and to buy when the MACD rises

above its signal line.

Money Flow

The idea behind money flow is to identify whether buyers are more eager to buy

the stock than sellers are to sell it. In its purest form, money flow looks at each

trade.

To calculate the money flow indicator, the technician multiplies price and volume

for the trades that occur at a price higher than the previous trade price. The

technician then sums this money flow. From this sum, the technician subtracts

another money flow: the accumulated total of price times volume for trades that

occur at prices lower than the previous trade.

Traders using money flow look for a divergence between money flow and price. If

price remains stable but money flow becomes highly positive, this is taken as an

indicator that the stock price will soon increase. Similarly, if the stock price

remains stable but the money flow becomes quite negative, this is taken as an

indicator that the stock price will soon decrease.

H. Fibonacci Numbers

The infinite Fibonacci series grows as follows:

1,1,2,3,5,8,13,21,34,55,89,144,233,377,610,987 . . .

Note that the series begins with 1,1 and grows by adding the two previous

numbers together (for example, 21 + 34 = 55). The ratio of a number in this

sequence to its predecessor converges to

φ=(

√

5+1)/2≈1. 618

Most market technicians are interested in Φ because:

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(Φ – 1)/Φ = 0.618/1.618 = .382

1/Φ = 1.000/1.618 = .618 = Φ – 1

Market technicians use these numbers to predict support and resistance levels.

For example, as a stock increases in value over time, it will occasionally pull back

in value. Suppose a stock has increased from $40 to $60, and has recently

begun to fall a bit in value. Using the (φ − 1) / φ ratio, market technicians would

predict the primary support area would occur at $52.36 ($60 − $40 = $20; $20 × .

382 = $7.64; $60 − $7.64 = $52.36). A similar calculation that uses the 1 / φ ratio

of .618 instead of .382 results in the secondary support area of $47.64.

If the stock were to pierce this secondary support level and close below it, the

rally would be declared over. Market technicians would then begin to look for

opportunities to sell the stock short if it subsequently rallied.

I. Other Technical Indicators

There are many other technical indicators. A few presented in the text include:

odd-lot indicator, hemline indicator, the Super Bowl indicator, and the Triple

Crown effect. The last three indicators illustrate that the world is full of odd

coincidences.

8.8 Summary and Conclusions

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