Chapter 7: Stock Price Behavior and Market Efficiency

Module 3 Key Points

Chapter 7: Stock Price Behavior and Market Efficiency

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  1. Introduction to Market Efficiency

Market efficiency: Relation between stock prices and information available to investors indicating whether it is possible to “beat the market;” if a market is efficient, it is not possible except by luck.

Efficient market hypothesis (EMH): Theory asserting that, as a practical matter, the major financial markets reflect all relevant information at a given time.

The primary question is: Can you, or can anyone, consistently “beat the market?”

(Note that the duck on slide 7-5 is trying to “beat the market” with his hammer. In PowerPoint 2007, the duck is animated (This is the only animated slide in the supplements). The duck attempts to beat the market, but fails. This is intended to provide a bit of levity to the subject.)

  1. What does “Beat the Market” Mean?

Excess return: A return in excess of that earned by other investments having the same risk.

To judge if an investment “beat the market,” we need to know if the return was high or low relative to the risk involved. We need to determine if the investment has earned a positive excess return in order to say it “beat the market.”

  1. Foundations of Market Efficiency

Three economic forces can lead to market efficiency. These conditions are so powerful that any one of them can result in market efficiency.

These conditions are:

1. Investor Rationality. If every investor always made perfectly rational investment decisions, earning an excess return would be difficult. If everyone is fully rational, equivalent risk assets would all have the same expected returns. Put differently, no bargains would be there to be had, because relative prices would all be correct.

2. Independent Deviations from Rationality. Even if the investor rationality condition does not hold, the market could still be efficient. Suppose that many investors are irrational, and a company makes a relevant announcement about a new product. Some investors will be overly optimistic, while some will be overly pessimistic, but the net effect might be that these investors cancel each other out. In a sense, the irrationality is just noise that is diversified away. As a result, the market could still be efficient (or nearly efficient). What is important here is that irrational investors do not have similar beliefs.

3. Arbitrage. Suppose there are many irrational traders and further suppose that their collective irrationality does not balance out. In this case, observed market prices can be too high or too low relative to their risk. Now suppose there are some well-capitalized, intelligent, and rational investors. This group of traders would see these high or low market prices as a profit opportunity and engage in arbitrage—buying relatively inexpensive stocks and selling relatively expensive stocks. If these rational arbitrage traders dominate irrational traders, the market will still be efficient. We sometimes hear the expression “Market efficiency doesn’t require that everybody be rational, just that somebody is.”

Forms of Market Efficiency

Weak-form efficient market: A market in which past prices and volume figures are of no use in beating the market.

Semistrong-form efficient market: A market in which publicly available information is of no use in beating the market.

Strong-form efficient market: A market in which information of any kind, public or private, is of no use in beating the market.

“A market is efficient with respect to some particular information if that information is not useful in earning a positive excess return.” So, a market can only be determined to be efficient with respect to specific information. The three forms include:

  • Weak-form efficiency, with respect to information reflected in past price and volume figures.
  • Semistrong-form efficiency, with respect to any publicly available information.
  • Strong-form efficiency, with respect to any information, both public and private.

To be clear, if the information allows an investor to earn excess returns on an investment, the market is not efficient with respect to that information. Therefore, if an investor uses past price information to earn an excess return, then the market is not weak-form efficient. If an investor uses a firm’s financial statements to earn an excess return, the market is not semistrong-form efficient. Finally, if an investor uses inside information to earn an excess return, the market is not strong-form efficient.

  1. Why Would a Market Be Efficient?

The driving force toward market efficiency is simply competition and the profit motive.

Consider a large mutual fund such as the Fidelity Magellan Fund (one of the largest equity funds in the United States, with about $45 billion under management). Suppose Fidelity was able, through its research, to improve the performance of this fund by 20 basis points for one year only. How much would this one-time 20-basis point improvement be worth? The answer is 0.0020 times $45 billion, or $90 million. Thus, Fidelity would be willing to spend up to $90 million to boost the performance of this one fund by as little as one-fifth of 1 percent for a single year only.

This example shows that even relatively small performance enhancements are worth tremendous amounts of money and thereby create the incentive to unearth relevant information and use it.

  1. Some Implications of Market Efficiency

If markets are efficient:

  • Security selection is less important; investors may as well hold index funds to minimize their costs.
  • There is little need for professional money managers.
  • Investors should not try to time the market. (In fact, successful market timing is very difficult to achieve, even ignoring market efficiency.)

Here it may be helpful to restate the implications of market efficiency with respect to the forms of market efficiency, as follows:

  • Weak-form efficiency: If weak-form efficiency holds, then technical analysis is of no use, and the efforts of technical analysts are of no benefit to investors.
  • Semistrong-form efficiency: If semistrong-form efficiency holds, then fundamental analysis using publicly available information is of no benefit, and most of the financial analysts and mutual fund managers are not providing any value.
  • Strong-form efficiency: If strong-form efficiency holds, then inside information is of no value, suggesting that there should be no restrictions on insider trading.
  1. Does Old Information Help Predict Future Stock Prices?

In its weakest form, the efficient market hypothesis is the simple statement that stock prices fully reflect all past information. If this is true, this means that studying past price movements in the hopes of predicting future stock price movements is really a waste of time. There is also a very subtle prediction at work here. That is, no matter how often a particular stock price path has related to subsequent stock price changes in the past, there is no assurance that this relationship will occur again in the future.

Researchers have used sophisticated statistical techniques to test whether past stock price movements are of any value in predicting future stock price movements. This turns out to be a surprisingly difficult question to answer clearly and without qualification. In short, although some researchers have been able to show that future returns are partly predictable by past returns, the predicted returns are not economically important, which means that predictability is not sufficient to earn an excess return.

In addition, trading costs generally swamp attempts to build a profitable trading system on the basis of past returns. Researchers have been unable to provide evidence of a superior trading strategy that uses only past returns. That is, trading costs matter, and buy-and-hold strategies involving broad market indexes are extremely difficult to outperform.

  1. Random Walks and Stock Prices

Are stock market prices predictable? Many believe they are. But it is very difficult to predict stock market prices. In fact, considerable research has shown that stock prices change through time as if they are random. That is, stock price increases and decreases are equally likely.

When the path that a stock price follows shows no discernible pattern, then the stock’s price behavior is largely consistent with the notion of a random walk. A random walk is related to the weak-form version of the efficient market hypothesis because past knowledge of the stock price is not useful in predicting future stock prices.

Daily price changes for Intel stock are illustrated in the text. These daily price changes are not truly a random walk. To qualify as a true random walk, Intel stock price changes would have to be independent and identically distributed. Still, the graph of daily price changes for Intel stock is essentially what a random walk looks like. It is certainly hard to see any pattern in these daily price changes.

  1. How Does New Information Get into Stock Prices?

In its semistrong form, the efficient market hypothesis is the simple statement that stock prices fully reflect publicly available information. Stock prices change when traders buy and sell shares based on their view of the future prospects for the stock. The future prospects for the stock are influenced by unexpected news announcements. Prices can adjust to news announcements in three ways:

  • Efficient market reaction: The price instantaneously adjusts to, and fully reflects, new information. There is no tendency for subsequent increases or decreases to occur.
  • Delayed reaction: The price partially adjusts to the new information, but days elapse before the price completely reflects new information.
  • Overreaction and correction: The price over-adjusts to the new information; it overshoots the appropriate new price but eventually falls to the new price.

Event Studies

The textbook includes an event study for Advanced Medical Optics, Inc. (EYE) in the text. On Friday, May 25, 2007, executives of Advanced Medical Optics Inc. recalled a contact lens solution called Complete MoisturePlus Multi Purpose Solution. The company took this voluntary action after the Centers for Disease Control and Prevention (CDC) found a link between the solution and a rare cornea infection called acanthamoeba keratitis, or AK for short.

Executives at Advanced Medical Optics chose to recall their product even though they did not find evidence their manufacturing process introduced the parasite that can lead to AK.

Researchers use a technique known as an event study to test the effects of news announcements on stock prices.

  1. Informed Traders and Insider Trading

Recall that if a market is strong-form efficient, no information of any kind, public or private, is useful in beating the market. However, inside information of many types clearly would enable you to earn essentially unlimited returns. This fact generates an interesting question: Should any of us be able to earn returns based on information that is not known to the public?

In the United States (and in many other countries, though not all), making profits on nonpublic information is illegal. This ban is said to be necessary if investors are to have trust in U.S. stock markets. The United States Securities and Exchange Commission (SEC) is charged with enforcing laws concerning illegal trading activities. Here are distinctions among informed traders, illegal insider trading, and legal insider trading.

  1. Informed Trading

When an investor makes a decision to buy or sell a stock based on publicly available information and analysis, this investor is said to be an informed trader. The information that an informed trader possesses might come from reading The Wall Street Journal, reading quarterly reports issued by a company, gathering financial information from the Internet, talking to other traders, or a host of other sources.

Lecture Tip. You will notice that we do not talk about noise traders here. We left them out here because the focus of this section is on information. Talking about noise traders (and their lack of information) could deflect the discussion away from information and the types of information-based trades.

  1. Insider Trading

Illegal Insider Trading:For the purposes of defining illegal insider trading, an insider is someone who possesses material nonpublic information. Such information is both not known to the public and, if it were known, would impact the stock price. A person can be charged with insider trading when he or she acts on such information in an attempt to make a profit.

Legal “Insider Trading”: A company’s corporate insiders can make perfectly legal trades in the stock of their company. To do so, they must comply with the reporting rules made by the U.S. Securities and Exchange Commission. When they make a trade and report it to the SEC, these trades are reported to the public. In addition, corporate insiders must declare that trades that they made were based on public information about the company, rather than “inside” information. Most public companies also have guidelines that must be followed.It’s Not a Good Thing: What did Martha Do? Martha Stewart was accused, but not convicted, of insider trading. She was accused, and convicted, of obstructing justice and lying to investigators.

  1. How Efficient are Markets?
  1. Are Financial Markets Efficient?

There are four reasons why market efficiency is difficult to test:

  • The risk-adjustment problem
  • The relevant information problem
  • The dumb luck problem
  • The data snooping problem

There are three generalities based on research that are relevant to market efficiency:

  • Short-term stock price and market movements are very difficult to predict with accuracy.
  • The market reacts quickly and sharply to new information. There is little evidence that a market under (or over) reaction can be profitably exploited.
  • If the stock market can be beaten, it is not obvious, so this implies that the market is not grossly inefficient.
  1. Some Implications of Market Efficiency

Even if all markets are efficient, asset allocation is still important because the risk-return tradeoff still holds.

  1. Market Efficiency and the Performance of Professional Money Managers

There have been a number of studies that compare the performance of mutual fund managers with market indices. The results of almost every study indicate that the market indices outperform the mutual fund managers. This is further evidence in favor of market efficiency. Mutual fund managers should be experts in technical and fundamental analysis, and they should be able to use these tools to earn excess returns, if anybody can.

One such study by Fortin and Michelson [Journal of Financial Planning, February 1999] compares the performance of a large sample of mutual funds categorized by investment objective, to their respective market indexes. For example, growth funds were compared to the S&P 500, corporate bond funds were compared to the Lehman Brothers Corporate Bond index, international funds were compared to the Morgan Stanley EAFE index, and small company equity funds were compared to the Wilshire 2000. This study found that, on average, the benchmark indices significantly outperformed the mutual funds for all fund categories but one. The one category that the funds outperformed the index was small company equity funds. Apparently the fund managers are able to exploit enough market inefficiencies in the small firm equity market to allow excess returns to accrue.

  1. Anomalies

In this section, several well-known market anomalies are discussed: the Day-of-the-week effect; the amazing January effect (and two of its extensions), and; Bubbles and Crashes (including the Market Crashes of 1929, 1987, the Asian Crash, and the “Dot-Com Bubble and Crash).

  1. The Day-of-the-Week Effect

Day-of-the-week effect: This is the term for the tendency for Monday to have a negative average return.

Table 7.2 in the textbook shows the day-of-the-week effect, which indicates that Monday is the only day with a negative average return. Notice that Friday has a high positive return. This effect is statistically significant, but it is difficult to exploit it to earn a positive excess. About all we can do is use this in our trading decisions; purchase a stock late on Monday and sell our stocks late on Friday.

  1. The Amazing January Effect

January effect: This is the term for the tendency for small stocks to have large returns in January.

Figures 7.9a and 7.9b show the results of the January effect. Small stocks tend to have much higher returns in January, whereas larger stocks (S&P 500) do not show this result. The bulk of the return occurs in the first few days of January. The effect is more pronounced for stocks that have significant declines. This effect exists in most major markets around the world. Two factors are important in explaining the January effect: tax-loss selling and institutional investors rebalancing their portfolios.

  1. Turn-of-the-Year Effect

Researchers have delved deeply into the January effect to see whether the effect is due to returns during the whole month of January or to returns bracketing the end of the year. Table 7.4 shows our calculations concerning this effect. The returns in the “Turn-of-the-Year Days” category are higher than returns in the “Rest-of-the-Days” category. Further, the difference is apparent in the 1984-2009 period. However, the difference was more than twice as large in the 1962-1983 period.

  1. Turn-of-the-Month Effect

Financial market researchers have also investigated whether a turn-of-the-month effect exists. Table 7.5 shows the results of our calculations. It appears that this effect was stronger in the 1984-2009 period than in the 1962-1983 period.

  1. The Earnings Announcement Puzzle

Researchers have found that it takes days (or even longer) for a market price to adjust fully to information about earnings surprises. In addition, some researchers have found that buying stocks after positive earnings surprises is a profitable investment strategy.

  1. The Price-Earnings (P/E) Puzzle

The P/E ratio is widely followed by investors and is used in stock valuation. Researchers have found that, on average, stocks with relatively low P/E ratios outperform stocks with relatively high P/E ratios, even after adjusting for other factors, like risk. Because a P/E ratio is publicly available information, it should already be reflected by stock prices. However, purchasing stocks with relatively low P/E ratios appears to be a potentially profitable investment strategy.

  1. Bubbles and Crashes

A bubble occurs when market prices soar far in excess of what normal and rational analysis would suggest. Investment bubbles eventually pop because they are not based on fundamental values. When a bubble does pop, investors find themselves holding assets with plummeting values.

A crash is a significant and sudden drop in market wide values. Crashes are generally associated with a bubble. Typically, a bubble lasts much longer than a crash. A bubble can form over weeks, months, or even years. Crashes, on the other hand, are sudden, generally lasting less than a week. However, the disastrous financial aftermath of a crash can last for years.

  1. The Crash of 1929

    Although the Crash of 1929 was a large decline, it pales with respect to the ensuing bear market. As shown in Figure 7.11, the DJIA rebounded about 20 percent following the October 1929 crash. However, the DJIA then began a protracted fall, reaching the bottom at 40.56 on July 8, 1932. This level represents about a 90 percent decline from the record high level of 386.10 on September 3, 1929. By the way, the DJIA did not surpass its previous high level until November 24, 1954, more than 25 years later.
  2. The Crash of October 1987

NYSE circuit breakers: This is the name for rules that kick in to slow trading when the DJIA declines by more than a preset amount in a trading session. In fact, if the DJIA declines far enough, trading will halt.

On October 19, 1987 (Black Monday) the Dow plummeted 500 points to 1,700 with about $500 billion in losses that day. There are several explanations for what happened:

  • Irrational investors bid up stock prices and the bubble popped.
  • Markets were volatile, the economy was shaky, and Congress was in session considering anti-takeover legislation.
  • Program trading quickly created very large sell orders.

Interestingly, the market recovered very quickly. The market was up in 1987 and the bull market continued for many years after the crash. As a result of the crash, NYSE circuit breakers were introduced. These circuit breakers required trading halts based upon 10, 20, and 30 percent declines in the DJIA. The trading halts vary from 30 minutes, to two hours, to the rest of the trading day.

  1. The Asian Crash

The crash of the Nikkei Index, which began in 1990, lengthened into a particularly long bear market. It is quite like the Crash of 1929 in that respect. In three years from December 1986 to the peak in December 1989, the Nikkei 225 Index rose 115 percent. Over the next three years, the index lost 57 percent of its value. In April 2003, the Nikkei Index stood at a level that was 80 percent

off its peak in December 1989.

  1. The “Dot-Com” Bubble and Crash

By the mid-1990s, the rise in Internet use and its international growth potential fueled widespread excitement over the “new economy.” Investors did not seem to care about solid business plans—only big ideas. Investor euphoria led to a surge in Internet IPOs, which were commonly referred to as “dot-coms” because so many of their names ended in “.com.” Of course, the lack of solid business models doomed many of the newly formed companies. Many of them suffered huge losses and some folded relatively shortly after their IPOs.

The Amex Internet Index soared from a level of 114.60 on October 1, 1998, to its

peak of 688.52 in late March 2000, an increase of about 500 percent. The Amex Internet Index then fell to a level of 58.59 in early October 2002, a drop of about 91 percent. By contrast, the S&P 500 Index rallied about 31 percent in the same 1998–2000 time period and fell 40 percent during the 2000–2002 time period.

  1. The Crash of October 2008

Although still under debate, many agree that one of the underlying causes of the crash of 2008 was excess liquidity, which allowed unworthy borrowers to obtain financing, primarily for mortgages. Moreover, much of this was done at low “teaser rates.” When these rates reset, require payments increased, resulting in bankruptcies for these so-called subprime loans. If house prices had continued to climb, borrowers could have refinanced, avoiding trouble. However, this did not happen.

Chapter 8: Behavioral Finance and the Psychology of Investing

  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?

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.

  1. 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 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? 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.”
  1. 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).

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.

  1. 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.

  1. 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.

  1. 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.

  1. 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.

  1. Snakebite Effect

The opposite of overconfidence is the snakebite effect, which refers to the unwillingness of investors to take a risk following a loss.

  1. 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.

  1. 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.

The clustering illusion is our human belief that random events that occur in clusters are not really random.

    1. 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 next spin will be black, when the true chance remains at about 50 percent (of course, it is exactly 18 in 38).

    1. 8.5 More on Behavioral Finance
  1. 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.

  1. 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.

  1. 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.

    1. 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.

  1. Limits to Arbitrage

The term limits to arbitragerefers 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 observed price on General Motors stock is too low, so you purchase many, many shares. Then, some unanticipated negative news drives the price of General Motors stock even lower.
  • Noise trader risk.A noise traderis 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.
  1. 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 (now it is known as PalmOne). 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.

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.

  1. 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 the daily deviations from the 60-40 ratio of the Royal Dutch price to the Shell price are plotted. 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.

  1. 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 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.

  1. 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.

  1. 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 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.

  1. 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 support 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.

  1. 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 November 16, 2009, to November 20, 2009, 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.

An easy way 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:

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.

  1. 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.

  1. 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.

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 when they actually suggest.

Price Channel

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 level, 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.


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 GM 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.


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 GM 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.


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.

  1. 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


Most market technicians are interested in Φ because:

(Φ-1)/Φ = 0.618/1.618 = 0.382

1/Φ = 1.000/1.618 = 0.618 = 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 × 0.382 = $7.64; $60 − $7.64 = $52.36). A similar calculation that uses the 1 / φ ratio of 0.618 instead of 0.382 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.

  1. Other Technical Indicators

There are many other technical indicators. A few presented in the text include: odd-lot indicator, hemline indicator, and the Super Bowl indicator. The last two indicators illustrate that the world is full of odd coincidences.

Source: Jordan, B., Miller, Jr., T., & Dolvin, S. (2012). Fundamentals of investment: Valuation and managment. (6th ed.). New York, NY: McGraw-Hill.

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