1. Behavioral Finance and Technical Analysis
CHAPTER 12
Behavioral Finance and Technical Analysis

3. Behavioral Finance, Introduction
Sooner or later, you are going to make an investment decision that winds up costing you a lot of money.
Why is this going to happen?
You made a sound decision, but you are “unlucky.”
You made a bad decision—one that could have been avoided.
The beginning of investment wisdom:
Learn to recognize circumstances leading to poor decisions.
Then, you will reduce the damage from investment blunders.

4. Behavioral Finance, Definition
Behavioral Finance The area of research that attempts to understand and explain how reasoning errors influence investor decisions and market prices.
Much of behavioral finance research stems from the research in the area of cognitive psychology.
Cognitive psychology: the study of how people (including investors) think, reason, and make decisions.
Reasoning errors are often called cognitive errors.
Some people believe that cognitive (reasoning) errors made by investors will cause market inefficiencies.

5. Behavioral Finance Efficient Market Finance Behavioral Finance
Investors don’t behave rationally.
The collective irrationality of these investors leads to an overly optimistic or pessimistic market situation.
This situation cannot be corrected via arbitrage by rational, well-capitalized investors.
Investors are rational.
Even if some investors are not rational, irrationality does not occur collectively.
Prices are correct; equal to intrinsic value.
If prices are not right, mispricing will be easily eliminated via sharp-eyed arbitrageurs.
Three Economic Conditions that Lead to Market Efficiency
Investor rationality
Independent deviations from rationality
Arbitrage
For a market to be inefficient, all three conditions must be absent. That is,
it must be that many, many investors make irrational investment decisions, and
the collective irrationality of these investors leads to an overly optimistic or pessimistic market situation, and
this situation cannot be corrected via arbitrage by rational, well-capitalized investors.
Whether these conditions can all be absent is the subject of a raging debate among financial market researchers.

6. Preview of Behavioral Finance
Information Processing
Forecasting errors
Overconfidence
Conservatism
Sample size neglect and representativeness
Behavioral biases
Framing
Mental accounting and Loss Aversion
Regret avoidance
Prospect theory
Limits to arbitrage
Fundamental risk
Implementation costs
Model Risk

7. Information processing: Forecasting Errors
Too much weight is placed on recent experiences.
Often called Memory Bias or Recency Bias
Example: The P/E Effect
When forecasts of a firm's future earnings are high, perhaps due to favorable recent performance, investors overly become optimistic about the firm’s true growth prospects.
This results in a high initial P/E due to the excessive optimism built into stock price and poor subsequent performance when invests recognize their error.

8. Information processing: Overconfidence
Investors overestimate their abilities and the precision of their forecasts.
Be honest: Do you think of yourself as a better than average driver?
If you do, you are not alone. About 80 percent of drivers 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?
“The investor’s chief problem, and even his worst enemy, is likely to be himself.” -- Benjamin Graham

9. Overconfidence How does overconfidence affect investment decisions?
Investors tend to invest too heavily in shares of the company for which they work.
This loyalty can be very bad financially.
Your earning power (income) depends on this company.
Your retirement nest-egg also depends on this company.
Another examples of the lack of diversification is investing too heavily in the stocks of local companies.
Perhaps you know someone personally who works there.
Perhaps you read about them in your local paper.
Basically, you are unduly confident that you have a high degree of knowledge about local companies.

10. Overconfidence: Trading Frequency
If you are overconfident about your investment skill, it is likely that you will trade too much.
Researchers have found that investors who make relatively more trades have lower returns than investors who trade less frequently.
Researchers have found that the average household earned an annual return of 16.4 percent. But, researchers have also found that households that traded the most earned an annual return of only 11.4 percent.
The moral is clear: Excessive trading is hazardous to your wealth.

11. Overconfidence and Trading Frequency Is Overtrading “a Guy Thing?”
Psychologists have found that men are more overconfident than women in the area of finance. So,
Do men trade more than women?
Do portfolios of men under-perform the portfolios of women?
Researchers show that the answer to both questions is yes.
Men trade about 50 percent more than women.
Researchers show that both men and women reduce their portfolio returns when they trade excessively.
The portfolio return for men is 94 basis points lower than portfolio returns for women.
The portfolio return for single men is 144 basis points lower than the portfolio return for single women.
Accounting for the effects of marital status, age, and income, researchers also show that men invest in riskier positions.

12. Information Processing: Conservatism
Investors are slow to update their beliefs and under react to new information.
They might initially underreact to news about a firm, so that prices will fully reflect new information only gradually.
Such a bias would give rise to momentum in stock market returns.

13. Information Processing: Sample Size Neglect and Representativeness
Investors are too quick to infer a pattern or trend from a small sample.
“The Law of Small Numbers”
You believe that a small sample of outcomes always resembles the long-run distribution of outcomes.
If your investment guru has been right five out of seven times recently, you might believe that his long-run average of being correct is also five out of seven.
Related to recency bias.
Also related to the hot hand’s fallacy and gambler’s fallacy (to be discussed later)

14. Information Processing: The Hot-Hand Fallacy
“Success Breeds Success.”
Suppose we look at the recent shooting by two basketball players named LeBron and Shaquille.
Assume both of these players make half of their shots.
LeBron: has just made two shots in a row.
Shaquille: has just missed two shots in a row.
Researchers have found that if they ask basketball fans which player has the better chance of making their next shot:
91 out of 100 will say LeBron.
They say this because they think LeBron has a “hot-hand.”
But, researchers have found that the “hot hand” is an illusion.
Players do not deviate much from their long-run shooting averages.
However, fans, players, announcers, and coaches think that they do.

15. The Hot-Hand Fallacy (cont’d)
Cognitive psychologists have studied the shooting percentage of one NBA team for a season and found:
A detailed analysis of the shooting data reveals that, statistically speaking, all shooting percentages in this table are the “same.”
It is true that basketball players shoot in streaks. But, these steaks are within bounds for long-run shooting percentages.

16. The Hot-Hand Fallacy (cont’d)
It is an illusion that basketball players are either “hot” or “cold.”
If you believe in the “hot hand,” you will likely reject this fact because you “know better” from watching shooters.
You are being fooled by randomness—randomness often appears in clusters.
Mutual fund investing and the clustering illusion.
Every year, funds that have had exceptionally good performance receive large inflows of money.
There is a universal disclaimer: “Past performance is no guarantee of future results.” Nonetheless, investors chase past returns.
Clustering Illusion: Our human belief that random events that occur in clusters are not really random.
For example, it strikes most people as very unusual if heads come up four times in a row during a series of coin flips.
However, if a fair coin is flipped 20 times, there is about a 50 percent chance of flipping four heads in a row.
If you flip four heads in a row, do you have a “hot hand” at coin flipping?

18. Information Processing: The Gambler’s Fallacy
Gambler’s Fallacy: Assuming that a departure for what occurs on average will be corrected in the short run.
Another way to think about the gambler’s fallacy: because an event has not happened recently, it has become “overdue” and is more likely to occur.
People sometimes refers incorrectly to the “law of averages.”
Example: The odds on a US Roulette table never change.
For each spin:
There is an 18 in 38 chance for a red number to “hit”
There is an 18 in 38 chance for a black number to “hit”
There is a 2 in 38 chance for a green number to “hit”
You suffer from the Gambler’s Fallacy if you think that it is more likely for a black number to “hit” after a series of red numbers have hit.

19. Behavioral Biases: Framing
If an investment problem is presented in two different (but really equivalent) ways, investors often make inconsistent choices.
Framing: How the risk is described, “risky losses” vs. “risky gains”, can affect investor decisions.
That is, how a problem is described, or framed, seems to matter to people.
Biases result in less than rational decisions, even with perfect information.
Try this: Jot down your answers in the following two scenarios.

20. Framing (cont’d)
Scenario One. Suppose we give you $1, Then, you have the following choice to make:
A. You can receive another $500 for sure.
B. You can flip a fair coin. If the coin-flip comes up “heads,” you get another $1,000, but if it comes up “tails,” you get nothing.
Scenario Two. Suppose we give you $2, Then, you have the following choice to make:
A. You can lose $500 for sure.
B. You can flip a fair coin. If the coin-flip comes up “heads,” you lose another $1,000, but if it comes up “tails,” you lose nothing.

21. Framing (cont’d) What were your answers?
Did you: choose option A in the first scenario and choose option B in the second scenario?
If you did, you are guilty of focusing on gains and losses, and not paying attention to what is important—the impact on your wealth.
However, you are not alone.
About 85 percent of the people who are presented with the first scenario choose option A.
About 70 percent of the people who are presented with the second scenario choose option B.

22. Framing (cont’d) But, the two scenarios are actually identical.
In each scenario:
You end up with $1,500 for sure if you pick option A.
You end up with a chance of either $1,000 or $2,000 if you pick option B.
So, you should pick the same option in both scenarios.
Which option you prefer is up to you.
But, if you are focusing on wealth, you should never pick option A in one scenario and option B in the other.
A fully rational investor is presumed to care only about his or her overall wealth, not the gains and losses associated with individual pieces of that wealth.
The reason people do is that the phrasing, or framing, of the question causes people to answer the questions differently.

23. Framing (cont’d)
Another example of Frame Dependence: Company-sponsored retirement plan, such as 401(k)
Two options
You can voluntarily join or “opt into” 401(k) plan
Or, you are automatically signed up, but later you can voluntarily “opt out” of the plan
The end result shows that more employees participated in the plan under the “opt out” option.

24. Behavioral Biases: Mental Accounting
the tendency to segment money into mental “buckets”
the tendency for people to separate their money into separate accounts based on a variety of subjective criteria, like the source of the money and intent for each account.
If you are engaging in mental accounting:
You spend regular income differently from bonuses rather than rationally viewing every dollar as identical.
You invest prudently in your retirement account while taking wild risks with a separate stock account.
The house money effect (to be discussed later)
Mental accounting helps explain why many investors designate some of their dollars as "safety" capital which they invest in low-risk investments, while at the same time treating their "risk capital" quite differently.

25. Mental “Buckets”
Let’s say you have bought a $150 ticket to a concert. When you show up at the door and realize you have lost your ticket, do you buy another?
Probably not.
But let’s say you hadn’t bought the ticket yet, and you show up at the door of the concert hall to buy your ticket. Unfortunately, you realized you’ve lost $150 in cash since you walked from your car. LUCKILY, you still have enough in your wallet though to cover the cost of the ticket. Do you buy the ticket?
Probably yes.
Behavioral economists say that mental accounting works like this: let’s say you have bought a $150 ticket to a concert. When you show up at the door and realize you have lost your ticket, do you buy another? Probably not.
But let’s say you hadn’t bought the ticket yet, and you show up at the door of the concert hall to buy your ticket. Unfortunately, you realized you’ve lost $150 in cash since you walked from your car. LUCKILY, you still have enough in your wallet though to cover the cost of the ticket. Do you buy the ticket? Yes.
Why?
Both scenarios are a loss of $150. However, in the second scenario you separate the losing of the $150 from the purchasing of the ticket. In the first you consider the cost of the event as a total of $300 and retch at the high cost.
It turns out that Mental Accounting is a huge contributor to the low rates of savings in the US (in 1999 it was 4% in the US and over 15% in Japan).
We have a tendency to value money differently depending on where it comes from. If you win $50 in the lottery – considered “found” money – you more likely to spend that on garbage than the $50 you earned on the job. If you get  tax refund – you’ll run out and spend it instead of saving.
We are victims of the concept of “relative cost”. You want to spend $10,500 dollars but the used car dealer says  it’ll be $500 extra this time of year. Mental accounting tells you that $500 in compared to the cost of the car isn’t that bad. But you want to buy a washing machine for $500 – you show up and they say it’s now $1000. Do you buy it? No. Mental accounting tells me that’s crazy talk.
Plastic equals fake money:It sounds like the oldest most overused money trick but it still holds true – people do spend more with credit cards. When the weight of your wallet doesn’t change, you are less likely to think you’ve really spent anything.

26. The House Money Effect
Las Vegas casinos have found that gamblers are far more likely to take big risks with money that they have won from the casino (i.e., “house money”).
Also, casinos have found that gamblers are not as upset about losing house money as they are about losing their own gambling money.
It may seem natural for you to separate your money into two buckets:
Your very precious money earned through hard work, sweat, and sacrifice.
Your less precious windfall money (i.e., house money).
But, this separation is plainly irrational.
Any dollar you have buys the same amount of goods and services.
The buying power is the same for “your money” and for your “house money.”

27. Behavioral Biases: Regret Avoidance
Regret Avoidance: Investors blame themselves more when an unconventional or risky bet turns out badly.
Buying a blue chip stocks that turns down is not as painful as experiencing the same losses on an unknown start-up companies.
Related to the snakebite effect (to be discussed later)

28. The Snakebite Effect Are you an avid skateboarder?
If not, maybe you never tried it, or maybe it simply has never appealed to you.
For others, you might have tried skateboarding, but gave it up because of a bad experience (say, a broken leg, arm, both, or worse).
If the second category is yours, you could well have experienced the snakebite effect.
As it relates to investing, the snakebite effect refers to the unwillingness of investors to take a risk following a loss. This effect is sometimes considered to have the opposite influence of overconfidence.
The snakebite effect makes people less confident in investing following a loss.
This particular phenomenon has been well documented in fund flows to mutual funds.
More money tends to be liquidated from mutual funds following some significant market declines.
In other market declines, less money than “normal” tends to flow toward mutual funds.
Unfortunately, this action is the opposite of what rational investor do—i.e., “buy low, sell high.”

29. Behavioral Biases: Prospect Theory
Prospect theory provides an alternative to classical, rational economic decision-making.
The foundation of prospect theory: investors are much more distressed by prospective losses than they are happy about prospective gains.
Researchers have found that a typical investor considers the pain of a $1 loss to be about twice as great as the pleasure received from the gain of $1.
Investors are risk-taking to avoid loss, while they are risk-averse with regard to gains.
Also, researchers have found that investors respond in different ways to identical situations.
The difference depends on whether the situation is presented in terms of losses or in terms of gains.

30. Prospect Theory Conventional view: Utility depends on level of wealth.
Behavioral view: Utility depends on changes in current wealth.

31. Anchoring and Loss Aversion
Anchoring: Associating a stock with its purchase price, i.e., fixating on a reference price.
Loss Aversion: A reluctance to sell investments after they have fallen in value. Also known as the “breakeven” effect or “disposition” effect.
If you are engaging in loss aversion:
You associate a stock with its purchase price.
You unknowingly creates a personal relationship with a stock.
You find it is difficult to sell a stock at a price lower than your purchase price.
If you sell a stock at a loss:
It may be hard for you to think 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.
If you suffer from Loss Aversion, you will think that if you can just somehow “get even,” you will be able to sell the stock.
It is sometimes said that you have “get-evenitis.”

32. Loss Aversion
Consider the following two investments: Investment One. A year ago, you bought shares in Fama Enterprises for $40 per share. Today, these shares are worth $20 each.
Investment Two. A year ago, you bought shares in French Company for $5 per share. Today, these shares are worth $20 each.
What will you do? Will you: (1) sell one of these stocks; (2) sell both of these stocks; (3) hold one of these stocks; or, (4) hold both of these stocks?

33. Loss Aversion (cont’d)
Suppose you are considering a new investment in Fama Enterprises.
Does your rational analysis say that it is reasonable to purchase shares at $20?
If the rational answer is no, then you should sell.
If the rational answer is yes, then you do not suffer from loss aversion.
However, if you argued to yourself that if shares in Fama Enterprises were a good buy at $40, then they must be a steal at $20, you probably have a raging case of loss aversion.

34. Loss Aversion (cont’d)
There are two Important lessons from this Example.
Lesson One: The market says that shares in Fama Enterprises are worth $20. The market does not care that you paid $40 a year ago.
Lesson Two: You should not care about your purchase price of Fama Enterprises. You must evaluate your shares at their current price.
How about the shares in French Company?
Once again, the lessons are the same.
The market says that French Company shares are worth $20 today.
The fact that you paid $5 a year ago is not relevant.
Get-Evenitis can be destructive. Famous example: Nicholas Leeson causing the collapse of the 233-year-old Barings Bank.
Note: For both investments, there will be tax effects. Your careful analysis should acknowledge the existence of taxes and transaction fees, and their impact on your net sale proceeds.

35. Do You Suffer from “Get-Evenitis
Do You Suffer from “Get-Evenitis?” An Infamous Story of the Barings Bank’s Collapse
Nicholas Leeson, 28-year-old trader in the 233-old Barings Bank did a series of gambling with the Bank’s money on Japanese Yen futures and other risky bets.
His strategy was “double-up and catch-up” to get “evenitis.”
Barings Bank become insolvent in 1995, when Leeson lost over the equivalent of US$ 1.3 billions.

36. The Fact on Loss Aversion
Research shows that
Individual investors are typically about 1.5 times more likely to sell a stock that has gone up in price then they are to sell a stock that has fallen in price.
With mutual funds, when investors choose to sell, they are more than 2.5 times as likely to sell a winning fund than a losing fund.
What does this all mean?
Investors tend to sell a winning stock too early, while they sell a losing stock too late!

37. Advice from Warren Buffett
“The stock does not know you own it.“
“You have feelings about it, but it has no feelings about you.“
“The stock does not know what you paid.”
“People should not get emotionally involved with their stocks.”

38. Behavioral Biases: Limits to Arbitrage
Behavioral biases would not matter if rational arbitrageurs could fully exploit the mistakes of behaviorally biased investors.
Traders profit-seeking investors would correct any misalignment of prices.
However, behavioral advocates argue that in practice, several factors limit the ability to profit from mispricing.

39. Limits to Arbitrage (cont’d)
Fundamental Risk:
“Markets can remain irrational longer than you can remain solvent.”
Intrinsic value and market value may take too long to converge.
Implementation Costs:
Transactions costs and restrictions on short selling can limit arbitrage activity.
Model Risk:
One always has to worry that an apparent profit opportunity is more apparent than real.
What if you have a bad model and the market value is actually correct?

40. Limits to Arbitrage: the Law of One Price
Siamese Twin Companies
Royal Dutch should sell for 1.5 times Shell
Have deviated from parity ratio for extended periods
Example of fundamental risk
Equity Carve-outs
3Com and Palm
Arbitrage limited by availability of shares for shorting
Closed-End Funds
May sell at premium or discount to NAV
Can also be explained by rational return expectations

41. Pricing of Royal Dutch Relative to Shell (Deviation from Parity)

42. Other Judgment Errors Sunk cost fallacy Endowment effect
Money illusion
Self-attribution bias
Wishful thinking bias
False consensus
Availability bias ……

43. Bubbles and Behavioral Economics
Bubbles are easier to spot after they end.
Dot-com bubble
In a 6-year period beginning in 1995, the NASDAQ index increased by a factor of more than 6.
Former Fed Chairman Alan Greenspan famously characterized the dot-com boom as an example of “irrational exuberance.”
By October 2002, the index fell to less than one-fourth the peak value it had reached only 2 ½ years earlier.
As the dot-com boom developed, it seemed to feed on itself, with investors increasingly confident of their investment prowess (overconfidence bias) and apparently willing to extrapolate short-term patterns into the distant future (representativeness bias)
Housing bubble
Only 5 years later, another bubble, this time housing prices, was under way.
The bursting bubble set off the worst financial crisis in 75 years.
While bubbles are going on, it is not as clear that prices are irrationally exuberant and, indeed, many financial commentators during the dot-com bubble justified the boom as consistent with glowing forecasts for the “new economy.”

44. Technical Analysis and Behavioral Finance
Technical analysis attempts to exploit recurring and predictable patterns in stock prices.
Technicians do not deny the value of fundamental information.
But, they believe that prices adjust gradually to a new equilibrium.
Market values and intrinsic values converge slowly.
For example, investors shows the disposition effect.
Disposition effect: The tendency of investors to hold on to losing investments.
Demand for shares depends on price history
Can lead to momentum in stock prices

45. Charting
Technical analysts rely heavily on charts that show recent market prices.
Technical analysis is sometimes called “charting.”
Technical analysts are often called “chartists.”
Chartists study graphs (or charts) of past market prices (or other information).
Chartists try to identify particular patterns known as chart formations.
Chart formations are thought to signal the direction of future prices.

46. Trends and Corrections: Dow Theory
The Dow theory is a method that attempts to interpret and signal changes in the stock market direction.
Dates to turn of the 20th century.
Named after Charles Dow (co-founder of the Dow Jones Co.)
The Dow theory identifies three forces:
a primary direction or trend,
a secondary reaction or trend, and
daily fluctuations.
Daily fluctuations are essentially noise and are of no real importance.
Dow Theory is less popular today, but its basic principles underlie more contemporary approaches to technical analysis.
“Don't fight the tape.“ “The Trend is your friend"
The basic purpose of the Dow Theory is to signal changes in the primary direction. To do this, two stock market averages, the Dow Jones Industrial Average (DJIA) and the Dow Jones Transportation Average (DJTA), are monitored. If one of these departs from the primary trend, the movement is viewed as secondary. However, if a departure in one is followed by a departure in the other, then this is viewed as a confirmation that the primary trend has changed. The Dow Theory was, at one time, very well known and widely followed.

47. Trends and Corrections: The Search for Momentum
Dow Theory
Primary trend : Long-term movement of prices, lasting from several months to several years.
Secondary or intermediate trend: short-term deviations of prices from the underlying trend line and are eliminated by corrections.
Tertiary or minor trends: Daily fluctuations of little importance.

48. Dow Theory Trends

49. Charting: Moving Averages
Moving average charts are average prices or index levels, calculated using a fixed number of previous prices, updated daily.
Because daily price fluctuations are “smoothed out,” these charts are used to identify trends.
Bullish signal: Market price breaks through the moving average line from below. Time to buy
Bearish signal: When prices fall below the moving average, it is time to sell.
Example: Suppose the technical trader calculates a 15-day and a 50-day moving average of a stock price.
If the 15-day crosses the 50-day from above, it is a bearish signal—time to sell.
If the 15-day crosses the 50-day from below, it is a bullish signal—time to buy.

50. Moving Average Line

51. Moving Average for HPQ

52. SBUX Moving Average

53. Trends and Corrections: Breadth
Breadth: Often measured as the spread between the number of stocks that advance and decline in price.

54. Sentiment Indicators: Trin Statistic
Ratios above 1.0 are bearish

55. Sentiment Indicators: Confidence Index
Barron’s computes a confidence index using data from the bond market.
The presumption is that actions of bond traders reveal trends that will emerge soon in the stock market.
Confidence index: The ratio of the average yield on 10 top-rated corporate bonds divided by the average yield on 10 intermediate-grade corporate bonds.
When bond traders are optimistic about the economy, however, they might require smaller default premiums on lower rated debt.
Hence, the yield spread will narrow, and the confidence index will approach 100%.
Therefore, higher values of the confidence index are bullish signals.

56. Sentiment Indicators: Put/Call Ratio
Calls are the right to buy.
A way to bet on rising prices
Puts are the right to sell.
A way to bet on falling prices
A rising ratio may signal investor pessimism and a coming market decline.
Contrarian investors see a rising ratio as a buying opportunity!

57. Warning! It is possible to perceive patterns that really don’t exist.
Figure 12.8A is based on the real data. The graph in panel B was generated using “returns” created by a random-number generator.
Figure 12.9 shows obvious randomness in the weekly price changes behind the two panels in Figure 12.8

58. Actual and Simulated Levels for Stock Market Prices of 52 Weeks

59. Actual and Simulated Changes in Stock Prices for 52 Weeks

60. “Support, Resistance, and Breakouts”

61. Other Technical Indicators
Bollinger Bands: 2-standard Deviation bound
MACD: Moving average convergence divergence
Fibonacci Numbers: Looking for percentage “retracement” levels.
The “odd-lot” indicator looks at whether odd-lot purchases are up or down.
Followers of the “hemline” indicator claim that hemlines tend to rise in good times.
The Super Bowl indicator forecasts the direction of the market based on who wins the game.
Two Conferences: the National Football Conference or the American Football Conference wins.
A win by the National Football Conference (or one of the original members of the National Football League) is bullish.
Traders using technical analysis are interested in timing their purchase or sale of a stock. Quite often, these traders look for support or resistance stock price levels. As strange as it may seem, one source that traders use is known as the Golden Mean. The Golden Mean also results from a series of numbers known as 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….
The series begins with 1,1 and grows by adding the two previous numbers together (for example, = 55). The ratio converges to 1.618, or Phi. Market technicians are interested in Phi because:
(Phi – 1) / Phi = / = and 1 / Phi = / = = 1 – Phi. Market technicians use Phi 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 (Phi – 1) / Phi ratio, market technicians would predict the primary support area would occur at $52.36 ($60 – $40 = $20; $20 X = $7.64; $60 – $7.64 = $52.36). A similar calculation that uses the 1 / Phi ratio of instead of 0.382, results in the secondary support area of $47.64.

62. VIX from CBOE
The CBOE Volatility Index® (VIX®) is a key measure of market expectations of near-term volatility conveyed by S&P 500 stock index option prices. Since its introduction in 1993, VIX has been considered by many to be the world's premier barometer of investor sentiment and market volatility.
VIX shows the market's expectation of 30-day volatility. It is constructed using the implied volatilities of a wide range of S&P 500 index options. 
The VIX is a widely used measure of market risk and is often referred to as the "investor fear gauge".

63. VIX – Fear Gauge

64. “The Hemline Indicator”

65. Using Behavioral Finance to Improve Investment Results