Up to this point, we have concentrated almost exclusively on the left-hand side of the balance sheet—the firm’s capital investment decision. Now we move to the right-hand side and to the problems involved in financing the capital investments. To put it crudely, you’ve learned how to spend money, now learn how to raise it.
Of course, we haven’t totally ignored financing in earlier chapters. We introduced the weighted-average cost of capital, for example. But in most places, we have looked past financing issues and used estimates of the opportunity cost of capital to discount future cash flows. We didn’t ask how the cost of capital might be affected by financing.
Now we are turning the problem around. We take the firm’s present portfolio of real assets and its future investment strategy as given, and then we determine the best financing strategy. For example,
· Should the firm reinvest most of its earnings in the business, or distribute the cash to shareholders?
· Is it better to distribute cash to stockholders by paying out dividends or by repurchasing stock?
· If the firm needs more money, should it issue more stock or should it borrow?
· Should it borrow short term or long term?
· Should it borrow by issuing a normal long-term bond or a convertible bond (a bond that can be exchanged for stock by the bondholders)?
There are countless other financing trade-offs, as you will see.
The purpose of holding the firm’s capital investment decision constant is to separate that decision from the financing decision. Strictly speaking, this assumes that investment and financing decisions are independent. In many circumstances, this is a reasonable assumption. The firm is generally free to change its capital structure by repurchasing one security and issuing another. In that case, there is no need to associate a particular investment project with a particular source of cash. The firm can think, first, about which projects to accept and, second, about how they should be financed.
Sometimes decisions about capital structure depend on project choice or vice versa, and in those cases, the investment and financing decisions have to be considered jointly. However, we defer discussion of such interactions of financing and investment decisions until Chapter 19.
We start this chapter by contrasting investment and financing decisions. The objective in each case is the same—to maximize NPV. However, it may be harder to find positive-NPV financing opportunities. The reason it is difficult to add value by clever financing decisions is that capital markets are usually efficient. By this we mean that fierce competition between investors eliminates profit opportunities and causes debt and equity issues to be fairly priced. If you think that sounds like a sweeping statement, you are right. That is why we have devoted this chapter to explaining and evaluating the efficient-market hypothesis.
We define the efficient-market hypothesis more carefully in Section 13-2. The hypothesis comes in three flavors—weak, semistrong, and strong—depending on the types of information available to investors. We review the evidence for and against each of the flavors. The evidence for efficient markets is mostly convincing, but puzzling anomalies keep cropping up.
Advocates for rational and efficient markets also have a hard time explaining bubbles. Every decade seems to find its own bubble: the 1980s real estate and stock market bubble in Japan, the 1990s technology stock bubble, and the real estate bubble that triggered the subprime crisis. Part of the blame for bubbles goes to the incentive and agency problems that can plague even the most rational people, particularly when they are investing other people’s money. But bubbles may also reflect patterns of irrational behavior that have been well documented by behavioral psychologists. We describe the main features of behavioral finance and the challenge that it poses to the efficient-market hypothesis.
The chapter closes with the five lessons of market efficiency and the implications for the financial manager if markets are not efficient.
13-1Differences between Investment and Financing Decisions
In some ways, financing decisions are more complex than investment decisions. The number of different securities and financing strategies is well into the hundreds (we have stopped counting). You will have to learn the major families, genera, and species. You will also need to become familiar with the vocabulary of financing. You will learn about red herrings, greenshoes, and bookrunners; behind each of these terms lies an interesting story.
There are also ways in which financing decisions are much easier than investment decisions. First, financing decisions do not have the same degree of finality as investment decisions. They are easier to reverse. Second, it’s harder to make money by smart financing strategies. The reason is that financial markets are more competitive than product markets. This means it is more difficult to find positive-NPV financing strategies than positive-NPV investment strategies.
When the firm looks at capital investment decisions, it does not assume that it is facing perfect, competitive markets. It may have only a few competitors that specialize in the same line of business in the same geographical area. And it may own some unique assets that give it an edge over its competitors. Often, these assets are intangible, such as patents, expertise, or reputation. All this opens up the opportunity to make superior profits and find projects with positive NPVs.
In financial markets, your competition is all other corporations seeking funds, to say nothing of the state, local, and federal governments that go to New York, London, Hong Kong, and other financial centers to raise money. The investors who supply financing are comparably numerous, and they are smart. Money attracts brains.
Competition is intense. Competition drives out easy profits for traders and investors who seek mispriced securities. If mispricing is rare, then it is reasonable to assume, at least as a starting point, that prices are right, or as right as human beings can get them.
When we suggest that stock or bond “prices are right,” we do not mean that they are stable. A price that is right today will change tomorrow when new information arrives. We only assume that prices incorporate all relevant information available to traders and investors at the time the prices are set. In other words, we assume that prices are set in efficient financial markets.
We Always Come Back to NPV
Financial managers separate investment and financing decisions. But the decisions to build a factory or issue a bond both involve valuation of an asset. The fact that you are buying a real asset (the factory) and selling a financial asset (the bond) doesn’t matter. In both cases you are concerned with the value of what is bought or sold.
For example, consider a new 10-year bond issue by GENX Corporation. The issue will raise $100 million for a new factory. The interest rate is 7%. GENX tried to negotiate a lower rate, but potential investors pointed out that 7% is the prevailing market interest rate on 10-year bonds issued by other companies with the same financial strength and bond rating as GENX. If GENX wants to sell the new bonds for $1,000 each, it will have to pay 7% interest on that $1,000.
Would you purchase the bond at this price? Before doing so, you decide to do a NPV calculation. You write out investment and interest payments.
NPV =−$1,000 + PV of interest payments at 7% of $1,000 + PV of principal ($1,000 repaid in year 10)
What’s the discount rate—that is, what’s the opportunity cost of capital? It must be 7%. That’s the rate of return you can get on other bonds with the same maturity and risk. Therefore:
NPV = 0 because buying a GENX bond gives you the prevailing market rate of return.
GENX’s CFO now decides to calculate the NPV of the bond issue for GENX. Her calculation is similar to yours, with signs reversed of course. Recall that the bond issue will raise $100 million. NPV of each bond that GENX sells is:
Again NPV = 0. The CFO is puzzled at first because 7% is less than the 15% rate of return she expects from the new factory. But she realizes that if the bond is fairly priced for investors, it must likewise be fairly priced for her company. She realizes that shareholder value comes from the factory (assuming that 15% exceeds the factory’s opportunity cost of capital), not from issuing ordinary bonds at the prevailing market interest rate. (Notice how the CFO has separated the investment and financing decisions. She has assigned a separate value to each.)
The bond issue would be positive NPV for GENX only if it could get an interest rate less than the prevailing market rate.1 That opportunity comes occasionally, but it almost always requires some kind of subsidy. Suppose New York State offers to lend at 3% if GENX locates the new factory in New York instead of New Jersey. That offer is positive-NPV:
Each bond that GENX can sell at the subsidized rate of interest has an NPV of $281. Of course, you don’t need arithmetic to conclude that borrowing at 3% is a good deal when the market rate is 7%. But the NPV calculation tells you just how much that opportunity is worth.
If, on the other hand, GENX receives no subsidy but issues the bond at the prevailing market interest rate, then the “price is right,” and the transaction is zero-NPV for both GENX and the bond investors. In that case, positive NPVs must be sought on the asset side of the balance sheet.
Now we should consider the assumptions embedded in our example. We have ignored transaction costs, which we cover in Chapter 15. We have ignored taxes. We will see in Chapter 18 that interest payments create valuable tax deductions. But our most important assumption was to trust the bond market. We accepted the prevailing interest rate. We did not pause to ask whether that rate was too high or too low. We did not pause to forecast future interest rates. We assumed that the prevailing rate was completely up to date and incorporated all relevant information about all things—past, present and possible future events—that can determine interest rates and bond prices. In other words, we accepted the efficient market hypothesis for bonds.
In the next section we begin a review of the evidence for and against this hypothesis. We will focus on the stock market, but the hypothesis also applies to markets for bonds and other securities.
13-2The Efficient Market Hypothesis
Economists define three levels of market efficiency, depending on the kinds of information incorporated in security prices. In the first level, prices incorporate all information contained in the record of past prices. This is called weak efficiency. If weak-form efficiency holds, prices follow random walks. We will explain “random walk” in a moment.
The second level of efficiency requires that prices incorporate all public information, including information from the Internet, the financial press, and other public sources. This is called semistrong efficiency. If markets are semistrong efficient, the prices will react immediately to new public information, for example to announcements of earnings per share, a new issue of stock or a merger proposal.
With strong efficiency, prices reflect all the information that can be acquired by painstaking analysis of companies and the economy. In such a market we would observe lucky and unlucky investors, but no superior investors who can consistently beat the market.
We will discuss each level of efficiency in its turn.
A Startling Discovery: Price Changes Are Random
As is so often the case with important ideas, the concept of efficient capital markets stemmed from a chance discovery. In 1953, Maurice Kendall, a British statistician, presented a controversial paper to the Royal Statistical Society on the behavior of stock and commodity prices.2 Kendall had expected to find regular price cycles, but to his surprise they did not seem to exist. Each series appeared to be “a ‘wandering’ one, almost as if once a week the Demon of Chance drew a random number . . . and added it to the current price to determine the next week’s price.” In other words, the prices of stocks and commodities seemed to follow a random walk.
If you are not sure what we mean by “random walk,” you might like to think of the following example: You are given $100 to play a game. At the end of each week a coin is tossed. If it comes up heads, you win 3% of your investment; if it is tails, you lose 2.5%. Therefore, your capital at the end of the first week is either $103.00 or $97.50. At the end of the second week, the coin is tossed again. Now the possible outcomes are:
This process is a random walk with a positive drift of .25% per week.3 It is a random walk because successive changes in value are independent. That is, the odds each week are the same, regardless of the value at the start of the week or of the pattern of heads and tails in the previous weeks.
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Stock prices can appear to have patterns
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When Maurice Kendall suggested that stock prices follow a random walk, he was implying that the price changes are independent of one another just as the gains and losses in our coin-tossing game were independent. Figure 13.1 illustrates this for four stocks from different markets—Microsoft, Marks & Spencer, Philips, and Rio Tinto. Each panel shows the change in price of the stock on successive days. The circled dot in the southeast quadrant of the Micro-soft panel refers to a pair of days in which a 2.9% increase was followed by a 2.9% decrease. If there were a systematic tendency for increases to be followed by decreases, there would be many dots in the southeast quadrant and few in the northeast quadrant. It is obvious from a glance that there is very little pattern in these price movements, but we can test this more precisely by calculating the coefficient of correlation between each day’s price change and the next. If price movements persisted, the correlation would be positive; if there were no relationship, it would be 0. In our example, the correlation between successive price changes in Microsoft stock was −.037; there was a negligible tendency for price rises to be followed by price falls.4 For Marks & Spencer, this correlation was also negative at −.020. For Rio Tinto, it was positive at +.010, and for Philips, the correlation was also just positive at +.004.
FIGURE 13.1 Each dot shows a pair of returns for a stock on two successive days between September 1997 and September 2017. The circled dot for Microsoft records a daily return of +2.9% and then −2.9% on the next day. The scatter diagram shows no significant relationship between returns on successive days.
Figure 13.1 suggests that successive price changes of all four stocks were effectively uncorrelated. Today’s price change gave investors almost no clue as to the likely change tomorrow. Does that surprise you? If so, imagine that it were not the case and that changes in Microsoft’s stock price were expected to persist for several months. Figure 13.2 provides an example of such a predictable cycle. You can see that an upswing in Microsoft’s stock price started last month, when the price was $40, and it is expected to carry the price to $80 next month. What will happen when investors perceive this bonanza? It will self-destruct. Since Microsoft stock is a bargain at $60, investors will rush to buy. They will stop buying only when the stock offers a normal risk-adjusted rate of return. Therefore, as soon as a cycle becomes apparent to investors, they immediately eliminate it by their trading.
FIGURE 13.2 Cycles self-destruct as soon as they are recognized by investors. The stock price instantaneously jumps to the present value of the expected future price.
You should see now why prices in competitive markets must follow a random walk. If past price changes could be used to predict future price changes, investors could make easy profits. But in competitive markets, there are no such free lunches. As investors try to take advantage of any information in past prices, prices adjust immediately until the superior profits from studying price movements disappear. As a result, all the information in past prices will be reflected in today’s stock price, not tomorrow’s. Patterns in prices will no longer exist, and price changes in one period will be independent of changes in the next. In other words, the share price will follow a random walk.
Random Walks: The Evidence
Since Maurice Kendall’s early discovery, statisticians have undertaken a myriad of tests of the weak form of the efficient market hypothesis. These have confirmed that stock prices throughout the world follow something close to a random walk. We say “close to a random walk” because every economic theory has its exceptions and there appear to be some patterns in stock returns.
For example, there is statistical evidence for momentum: Stocks that have delivered superior returns over the last few weeks or months tend to deliver superior returns in the future. There is also momentum on the downside: Poorly performing stocks tend to continue to disappoint.5
Momentum does not generate easy money for investors. It is a statistical tendency, not a sure thing. Also, pursuit of momentum profits sacrifices diversification and increases risk. Nevertheless, some investment funds specialize in momentum strategies, and a few have done well.
There are also some profit opportunities as prices bounce around in the very short run. But to have any chance of making money from such bounces, you need to be a high-frequency trader with one eye on the computer screen and the other on your annual bonus. You will need super-fast computers with algorithms that trade in high volumes with the aim of capturing a few cents per trade.6
Semistrong Market Efficiency: The Evidence
To test for semistrong efficiency, researchers have examined how stock prices respond to public releases of information—for example, news about earnings or dividends, announcements of mergers or takeovers, or macroeconomic developments. Semistrong efficiency means that stock prices respond to relevant news quickly and completely.
There’s no doubt that stock prices respond quickly to breaking news. Take Volkswagen’s (VW’s) diesel emissions scandal as an example. On Friday, September 18, 2015, the U.S. Environmental Protection Agency (EPA) announced that VW had installed “defeat devices” in several models of diesel cars that reduced emissions only when emissions tests were under way. VW’s stock price dropped immediately from about $160 to about $130 per share. There was another drop to about $110 per share on Monday, when VW admitted that it had sold 11 million cars worldwide with the defeat device. VW lost nearly one third of its stock-market value in two days’ trading.
Another example: CNBC broadcasts a daily Morning and Midday Call that summarizes security-analyst reports and other information about individual stocks. A study of 322 stocks that were discussed in these calls found that positive reports triggered a price increase seconds after the positive news was first broadcast. Investors could make a small profit after expenses only if they were able to buy in the first 15 seconds.7
Abnormal Returns The quick response of a stock price to new public information does not prove that the new price is right and completely incorporates the new information. More thorough tests of semistrong efficiency rely on event studies, which examine abnormal returns on samples of stocks that encountered the same type of news release.
Suppose you decide to investigate how stock prices of takeover targets respond when the takeovers are first announced. As a first stab, you could simply calculate the average return on target-company stocks in the days leading up to the announcement and immediately after it. With daily returns on a large sample of targets, the average announcement effect should be clear. There won’t be too much contamination from movements in the overall market around the announcement dates because daily market returns average out to a very small number.8 The potential contamination increases for weekly or monthly returns, however. In these cases you will usually want to adjust for market movements. For example, you can simply subtract out the return on the market:
Adjusted stock return = return on stock − return on market index
Chapter 8 suggests a refined adjustment based on betas. (Just subtracting the market return assumes that target-firm betas equal 1.0.) This adjustment is called the market model:
Expected stock return = α + β × return on market index
Alpha (α) states how much on average the stock price changed when the market index was unchanged. Beta (β) tells us how much extra the stock price moved for each 1% change in the market index.9 Suppose that subsequently the stock price return is 
in a month when the market return is m . In that case, we would conclude that the abnormal return for that month is
This abnormal return should reflect firm-specific news only.10
Figure 13.3 illustrates how the release of news affects abnormal returns. It shows the cumulative abnormal return on a sample of U.S. firms that were targets of takeover attempts. Acquiring firms usually have to pay a substantial takeover premium to get the deal done, so the target firm’s stock price increases as soon as the takeover bid is announced. Figure 13.3 shows the average pattern of the target’s stock returns before and after the announcement of a takeover (day 0 in the figure). Stock prices drift up before date zero, as investors gradually realize that a takeover may be coming. On the day of the announcement and the following day, prices jump up by 17.3%.11 The adjustment to the stock price is immediate and complete. There is no significant further drift in the price, either upward or downward. Thus, within the day, the new stock prices reflect (at least on average) the magnitude of the takeover premium.
FIGURE 13.3 The abnormal performance of the stocks of takeover targets around the announcement date. The prices of target stocks jump up on the day following the announcement, but from then on, there are no unusual price movements. The figure shows the cumulative abnormal returns for 8,668 U.S. targets between 1975 and 2016.
Source: WRDS.
Studies of price reactions to takeovers provide good news for semistrong efficiency. But there is also bad news for the hypothesis. One example is the strange case of the “Siamese twins,” two securities with claims on the same cash flows, which nevertheless trade separately. Before the two companies merged in July 2005, the Dutch company Royal Dutch Petroleum and the British company Shell Transport & Trading (T&T) were Siamese twins, each with a fixed share in the profits and dividends of the oil giant. Since both companies participated in the same underlying cash flows, you would expect the stock prices to have moved in exact lockstep. But, as you can see from Figure 13.4, the prices of the two shares sometimes diverged substantially.12
FIGURE 13.4 Log deviations from Royal Dutch Petroleum/Shell T&T parity
Source: Mathijs van Dijk, www.mathijsavandijk.com/dual-listed-companies.
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Long-run IPO returns
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Strong Market Efficiency: The Evidence
Tests of the strong form of the efficient markets hypothesis have examined whether professional portfolio managers can consistently “beat the market.” Some researchers have found a slight persistent outperformance, but just as many have concluded that professionally managed funds fail to recoup the costs of management. Look, for example, at Figure 13.4, which compares the returns on diversified equity funds to the Wilshire 5000 Index. You can see that in some years, the mutual funds beat the market, but roughly 60% of the time, it was the other way around.
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Mutual fund cumulative returns
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Figure 13.5 provides a fairly crude comparison because mutual funds have tended to specialize in particular sectors of the market, such as low-beta stocks or large-firm stocks, that may have given below-average returns. To control for such differences, each fund needs to be compared with a benchmark portfolio of similar securities. A number of studies have done this. Most have found that the message was unchanged: The funds earned a lower return than the benchmark portfolios after expenses and roughly matched the benchmarks before expenses.13
FIGURE 13.5 Diversified equity funds versus the Wilshire 5000 Index, 1971–2017. Notice that mutual funds underperform the market in approximately 60% of the years.
OK, maybe mutual funds in general don’t earn superior returns, but surely some managers are smarter than others and can be relied on to beat their less competent brethren. Unfortunately, it seems difficult to spot the smart ones. For example, a top-quartile fund in one year has no more than an average chance of being in the top quartile the following year.14 It seems that the top-performing managers in one period have about an average chance of falling on their faces in the next period.
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Measuring mutual fund performance
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The evidence on efficient markets has convinced many professional and individual investors to give up pursuit of superior performance. They simply “buy the index,” which maximizes diversification and cuts costs to the bone. Individual investors can buy index funds, which are mutual funds that track stock market indexes. There is no active management, so costs are very low. For example, in mid-2018 management fees for the Vanguard 500 Index Fund, which tracks the S&P 500 Index, were .04% per year for investments over $10,000. The size of this fund was $418 billion.
How far could indexing go? Not to 100%: If all investors hold index funds, then nobody will be collecting information, and prices will not respond to new information when it arrives. An efficient market needs some smart investors who gather information and attempt to profit from it. To provide incentives to gather costly information, prices cannot reflect all information.15 There must be some profits available to allow the costs of information to be recouped. But if the costs are small, relative to the total market value of traded securities, then the financial market can still be close to perfectly efficient.
In some ways, the evidence for strong efficiency is stronger than the evidence for weak or semistrong efficiency. Researchers’ statistical tools find patterns, tendencies and anomalies in stock prices. They find examples of lagged responses to public information. Yet it is exceptionally difficult to generate consistent, superior investment performance. Researchers can beat the market in hindsight. Investors with real money have a much harder time of it.
Strong efficiency has important implications for corporate financial managers, who must often decide how to manage investment portfolios. Two examples: Exelon Corporation, which operates the largest fleet of nuclear power plants in the United States, manages a decommissioning trust earmarked to cover future costs of shutting down and decommissioning its nuclear plants. The value of the trust portfolio was $13.3 billion in 2017. Cummins Inc. has a defined-benefit pension plan and sets aside money to invest to cover future pension payments to retired employees in the U.S. and U.K. Its pension assets in 2017 were $5.1 billion.
How should Exelon and Cummins manage these investments? Should they search for portfolio managers that can consistently deliver superior risk-adjusted returns? The evidence for strong-form efficiency indicates that they would be better off minimizing costs by passive indexing of pension fund or other investment portfolios. More and more corporations are doing just that—at least for investments in U.S. stocks and bonds. But they do hire active managers where inefficiencies are more likely—for example, in developing-country stock markets.16
Strong-form efficiency has implications for you also. Most readers of this book will be investors. Will you be an active stock-picker? Or will you diversify and minimize management fees by indexing? If you are an active stock-picker, we hope you have fun doing it.
13-3Bubbles and Market Efficiency
So far, we have asked whether individual stocks are “priced right,” given the information that investors can see or acquire. But what about the market as a whole? Are there cases where the overall level of prices cannot be justified by fundamentals? We will look at the evidence in a moment, but first we should note how difficult it is to value common stocks and to determine whether their prices are irrational.
For example, imagine that in mid-2017 you wanted to check whether the stocks forming Standard & Poor’s Composite Index were fairly valued. As a first stab, you might use the constant-growth formula that we introduced in Chapter 4. In 2017, the annual dividends paid by the companies in the index were roughly $420 billion. Suppose that these dividends were expected to grow at a steady rate of 4.0% and that investors required a return of 6.0%. Then the constant-growth formula gives a value for the common stocks of
which was roughly their value in August 2017. But how confident could you be about these figures? Perhaps the likely dividend growth was only 3.5% per year. In that case, your estimate of the value of the common stocks would decline to
In other words, a reduction of just half a percentage point in the expected rate of dividend growth would reduce the value of common stocks by 20%.
The extreme difficulty of valuing common stocks from scratch has two important consequences. First, investors find it easier to price a common stock relative to yesterday’s price or relative to today’s price of similar securities. In other words, they generally take yesterday’s price as correct, adjusting upward or downward on the basis of today’s information. If information arrives smoothly, then, as time passes, investors become increasingly confident that today’s price level is correct. But when investors lose confidence in the benchmark of yesterday’s price, there may be a period of confused trading and volatile prices before a new benchmark is established.
Second, most of the tests of market efficiency are concerned with relative prices and focus on whether there are easy profits to be made. It is almost impossible to test whether stocks are correctly valued because no one can measure true value with any precision. Take, for example, Pepsi stock, which sold for $116 July 2018. Could we prove that this was its true value? Of course not, but we could be more confident that the price of Pepsi should be somewhat more than double that of Coca-Cola ($45) because Pepsi’s earnings and dividends per share were 2.5 times those of Coke and the two companies had similar growth prospects.
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Two mysterious crashes
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It may be impossible to prove that market levels are, or are not, consistent with fundamentals. However, every now and again investors seem to be caught up in a speculative frenzy, and asset prices then reach levels that (at least with hindsight) cannot easily be justified by the outlook for profits and dividends. Investors refer to such occasions as bubbles. Bubbles can result when prices rise rapidly, and more and more investors join the game on the assumption that prices will continue to rise. These bubbles can be self-sustaining for a while. It can be rational to jump on the bandwagon as long as you are sure that there will be greater fools that you can sell out to. But remember that lots of money will be lost, perhaps by you, when the bubble bursts.17
The Japanese bubble is a good example. The Nikkei 225 Index increased by about 240% from the start of 1985 to its peak of about 39,000 in January 1990. But stock prices fell sharply after an increase in interest rates. The Nikkei fell to about 23,000 by year-end 1990 and continued an irregular slide downward to about 10,000 in 2010. The index has since recovered, but only to 23,000 in early 2018.
The boom in Japanese stock prices was matched by an even greater explosion in land prices. For example, Ziemba and Schwartz document that the few hundred acres of land under the Emperor’s Palace in Tokyo, evaluated at neighborhood land prices, was worth as much as all the land in Canada or California.18 But then the real estate bubble also burst. By 2005, land prices in the six major Japanese cities had slumped to just 13% of their peak.
Such bubbles are not confined to Japan. Toward the end of the twentieth century, investors in technology stocks saw a remarkable run-up in the value of their holdings. The Nasdaq Composite Index, which has a heavy weighting in high-tech stocks, rose 580% from the start of 1995 to its high in 2000. Then, as rapidly as it began, the boom ended, and by October 2002 the Nasdaq index had fallen 78% from its peak.
Some of the largest gains and losses were experienced by dot-com stocks. For example, Yahoo! shares, which began trading in April 1996, appreciated by 1,400% in four years. In these heady days, some companies found that they could boost their stock price simply by adding “.com” to the company name.19
Looking back at the Japanese and dot-com bubbles, it seems difficult to believe that future cash flows could ever have been sufficient to provide investors with a reasonable return.20 If that is the case, then we must conclude that “bubbles happen.” When they do happen, markets cannot be efficient.
13-4Behavioral Finance
Why might prices depart from fundamental values? Some believe that the answer lies in behavioral psychology. People are not 100% rational 100% of the time. This shows up in investors’ attitudes to risk and the way they assess probabilities.
1. Attitudes toward risk. Psychologists have observed that, when making risky decisions, people are particularly loath to incur losses. It seems that investors do not focus solely on the current value of their holdings, but look back at whether their investments are showing a profit or a loss. For example, if I sell my holding of IBM stock for $10,000, I may feel on top of the world if the stock only cost me $5,000, but I will be much less happy if it had cost $11,000. This observation is the basis for prospect theory.21 Prospect theory states that (a) the value investors place on a particular outcome is determined by the gains or losses that they have made since the asset was acquired or the holding last reviewed, and (b) investors are particularly averse to the possibility of even a very small loss and need a high return to compensate for it.
The pain of loss seems also to depend on whether it comes on the heels of earlier losses. Once investors have suffered a loss, they may be even more concerned not to risk a further loss. Conversely, just as gamblers are known to be more willing to make large bets when they are ahead, so investors may be more prepared to run the risk of a stock market dip after they have enjoyed a run of unexpectedly high returns.22 If they do then suffer a small loss, they at least have the consolation of still being ahead for the year.
When we discussed portfolio theory in Chapters 7 and 8, we pictured investors as forward-looking only. Past gains or losses were not mentioned. All that mattered was the investor’s current wealth and the expectation and risk of future wealth. We did not allow for the possibility that Nicholas would be elated because his investment is in the black, while Nicola with an equal amount of wealth would be despondent because hers is in the red.
2. Beliefs about probabilities. Most investors do not have a PhD in probability theory and may make systematic errors in assessing the probability of uncertain events. Psychologists have found that, when judging possible future outcomes, individuals tend to look back at what happened in a few similar situations. As a result, they are led to place too much weight on a small number of recent events. For example, an investor might judge that an investment manager is particularly skilled because he has “beaten the market” for three years in a row or that three years of rapidly rising prices are a good indication of future profits from investing in the stock market. The investor may not stop to reflect on how little one can learn about expected returns from three years’ experience.
Most individuals are also too conservative—that is, too slow to update their beliefs in the face of new evidence. People tend to update their beliefs in the correct direction, but the magnitude of the change is less than rationality would require.
Another systematic bias is overconfidence. For example, an American small business has just a 35% chance of surviving for five years. Yet the great majority of entrepreneurs think that they have a better than 70% chance of success.23 Similarly, most investors think they are better-than-average stock pickers. Two speculators who trade with each other cannot both make money, but nevertheless, they may be prepared to continue trading because each is confident that the other is the patsy.24 Overconfidence also shows up in the certainty that people express about their judgments. They consistently overestimate the odds that the future will turn out as they say and underestimate the chances of unlikely events.
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Overconfident CFOs
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You can see how these behavioral characteristics may help to explain the Japanese and dot-com bubbles. As prices rose, they generated increased optimism about the future and stimulated additional demand. The more that investors racked up profits, the more confident they became in their views and the more willing they became to bear the risk that next month might not be so good.
Sentiment
Behavioral economists stress the importance of investor sentiment in determining stock prices, and they point to evidence of major swings in sentiment. For example, every week the American Association of Individual Investors surveys its members and asks them whether they are bullish, bearish, or neutral on the stock market over the next six months. Anyone who believed that all the good or bad news was already reflected in stock prices would tick the neutral box. But you can see from Figure 13.6 that private investors swing quite strongly between being bullish or bearish. In January 2000, at the height of the dot-com boom, a massive 75% of investors said they were bullish, 62% more than claimed to be bearish. Perhaps these periods of bullishness and bearishness may explain the short-term momentum effect that we commented on earlier.25
FIGURE 13.6 The spread between the percentage of investors claiming to be bullish and those claiming to be bearish in the weekly sentiment survey of the American Association of Individual Investors
Source: http://www.aaii.com/SentimentSurvey
Limits to Arbitrage
It is not difficult to believe that amateur investors may sometimes be caught up in a scatty whirl of irrational exuberance.26 But there are plenty of hard-headed professional investors managing huge sums of money. Why don’t these investors bail out of overpriced stocks and force their prices down to fair value? One reason is that there are limits to arbitrage—that is, limits on the ability of the rational investors to exploit market inefficiencies.
Strictly speaking, arbitrage means an investment strategy that guarantees superior returns without any risk. In practice, arbitrage is defined more casually as a strategy that exploits market inefficiency and generates superior returns if and when prices return to fundamental values. Such strategies can be very rewarding, but they are rarely risk-free.
In an efficient market, if prices get out of line, then arbitrage forces them back. The arbitrageur buys the underpriced securities (pushing up their prices) and sells the overpriced securities (pushing down their prices). The arbitrageur earns a profit by buying low and selling high and waiting for prices to converge to fundamentals. Thus, arbitrage trading is often called convergence trading.
But arbitrage is harder than it looks. Trading costs can be significant, and some trades are difficult to execute. For example, suppose that you identify an overpriced security that is not in your existing portfolio. You want to “sell high,” but how do you sell a stock that you don’t own? It can be done, but you have to sell short.
BEYOND THE PAGE
Short sales
mhhe.com/brealey13e
To sell a stock short, you borrow shares from another investor’s portfolio, sell them, and then wait hopefully until the price falls and you can buy the stock back for less than you sold it for. If you’re wrong and the stock price increases, then sooner or later you will be forced to repurchase the stock at a higher price (therefore at a loss) to return the borrowed shares to the lender. But if you’re right and the price does fall, you repurchase, pocket the difference between the sale and repurchase prices, and return the borrowed shares. Sounds easy, once you see how short selling works, but there are costs and fees to be paid, and in some cases, you will not be able to find shares to borrow.27
The perils of selling short were dramatically illustrated in 2008. Given the gloomy outlook for the automobile industry, several hedge funds decided to sell Volkswagen (VW) shares short in the expectation of buying them back at a lower price. Then in a surprise announcement, Porsche revealed that it had effectively gained control of 74% of VW’s shares. Since a further 20% was held by the state of Lower Saxony, there was not enough stock available for the short sellers to buy back. As they scrambled to cover their positions, the price of VW stock rose in just two days from €209 to a high of €1,005, making VW the most highly valued company in the world. Although the stock price drifted rapidly down, those short-sellers who were caught in the short squeeze suffered large losses.
The VW example illustrates that the most important limit to arbitrage is the risk that prices will diverge even further before they converge. Thus, an arbitrageur has to have the guts and resources to hold on to a position that may get much worse before it gets better. Take another look at the relative prices of Royal Dutch and Shell T&T in Figure 13.4. Suppose that you were a professional money manager in 1980, when Royal Dutch was about 12% below parity. You decided to buy Royal Dutch, sell Shell T&T short, and wait confidently for prices to converge to parity. It was a long wait. The first time you would have seen any profit on your position was in 1983. In the meantime, the mispricing got worse, not better. Royal Dutch fell to more than 30% below parity in mid-1981. Therefore, you had to report a substantial loss on your “arbitrage” strategy in that year. You were fired and took up a new career as a used-car salesman.
The demise in 1998 of Long Term Capital Management (LTCM) provides another example of the problems with convergence trades. LTCM, one of the largest and most profitable hedge funds of the 1990s, believed that interest rates in the different eurozone countries would converge when the euro replaced the countries’ previous currencies. LTCM had taken massive positions to profit from this convergence, as well as massive positions designed to exploit other pricing discrepancies. After the Russian government announced a moratorium on some of its debt payments in August 1998, there was great turbulence in the financial markets, and many of the discrepancies that LTCM was betting on suddenly got much larger.28 LTCM was losing hundreds of millions of dollars daily. The fund’s capital was nearly gone when the Federal Reserve Bank of New York arranged for a group of LTCM’s creditor banks to take over LTCM’s remaining assets and shut down what was left in an orderly fashion.
LTCM’s sudden meltdown has not prevented rapid growth in the hedge fund industry in the 2000s. If hedge funds can push back the limits to arbitrage and avoid the kinds of problems that LTCM ran into, markets will be more efficient going forward. But asking for complete efficiency is probably asking too much. Prices can get out of line and stay out if the risks of an arbitrage strategy outweigh the expected returns.
Incentive Problems and the Financial Crisis of 2008–2009
The limits to arbitrage open the door to individual investors with built-in biases and misconceptions that can push prices away from fundamental values. But there can also be incentive problems that get in the way of a rational focus on fundamentals. We illustrate with a brief look at the financial crisis in 2008 and 2009.
Although U.S. house prices had risen nearly threefold in the decade to 2006, few homeowners foresaw a collapse in the price of their home. After all, the average house price in the U.S. had not fallen since the Great Depression of the 1930s. But in 2006, the bubble burst. By March 2009, U.S. house prices had fallen by nearly a third from their peak.29
How could such a boom and crash arise? In part because banks, credit rating agencies, and other financial institutions all had distorted incentives. Purchases of real estate are generally financed with mortgage loans from banks. In most parts of the United States, borrowers can default on their mortgages with relatively small penalties. If property prices fall, they can simply walk away. But if prices rise, they make money. Thus, borrowers may be willing to take large risks, especially if the fraction of the purchase price financed with their own money is small.
Why, then, are banks willing to lend money to people who are bound to default if property prices fall significantly? Since the borrowers benefited most of the time, they were willing to pay attractive up-front fees to banks to get mortgage loans. But the banks could pass on the default risk to somebody else by packaging and reselling the mortgages as mortgage-backed securities (MBSs). Many MBS buyers assumed that they were safe investments because the credit rating agencies said so. As it turned out, the credit ratings were a big mistake. (The rating agencies introduced another agency problem because issuers paid the agencies to rate the MBS issues, and the agencies consulted with issuers over how MBS issues should be structured.)
The “somebody else” was also the government. Many subprime mortgages were sold to FNMA and FHLMC (“Fannie Mae” and “Freddie Mac”). These were private corporations with a special advantage: government credit backup. (The backup was implicit but quickly became explicit when Fannie and Freddie got into trouble in 2008. The U.S. Treasury had to take them over.) Thus, these companies were able to borrow at artificially low rates, channeling money into the mortgage market.
The government was also on the hook because large banks that held subprime MBSs were “too big to fail” in a financial crisis. So the original incentive problem—the temptation of home buyers to take out a large mortgage and hope for higher real estate prices—was never corrected. The government could have cut its exposure by reining in Fannie and Freddie before the crisis but did not do so, perhaps because the government was happy to see more people able to buy their own homes.
Agency and incentive problems are widespread in the financial services industry. In the United States and many other countries, people engage financial institutions such as pension funds and mutual funds to invest their money. These institutions are the investors’ agents, but the agents’ incentives do not always match the investors’ interests. Just as with real estate, these agency relationships can lead to mispricing, and potentially bubbles.30
13-5The Five Lessons of Market Efficiency
The efficient-market hypothesis emphasizes that arbitrage will rapidly eliminate any profit opportunities and drive market prices back to fair value. Behavioral-finance specialists may concede that there are no easy profits, but argue that arbitrage is costly and sometimes slow-working, so that deviations from fair value may persist.
Sorting out the puzzles will take time, but we suggest that financial managers should assume, at least as a starting point, that stock and bond prices are “right” and that there are no free lunches to be had on Wall Street.
The “no free lunch” principle gives us the following five lessons of market efficiency. After reviewing these lessons, we consider what market inefficiency can mean for the financial manager.
Lesson 1: Markets Have No Memory
The weak form of the efficient-market hypothesis states that the sequence of past price changes contains no information about future changes. Economists express the same idea more concisely when they say that the market has no memory. Sometimes financial managers seem to act as if this were not the case. For example, after an abnormal market rise, managers prefer to issue equity rather than debt.31 The idea is to catch the market while it is high. Similarly, they are often reluctant to issue stock after a fall in price. They are inclined to wait for a rebound. But we know that the market has no memory and the cycles that financial managers seem to rely on do not exist.32
Sometimes a financial manager will have inside information indicating that the firm’s stock is overpriced or underpriced. Suppose, for example, that there is some good news that the market does not know but you do. The stock price will rise sharply when the news is revealed. Therefore, if your company sells shares at the current price, it would offer a bargain to new investors at the expense of present stockholders.
Naturally, managers are reluctant to sell new shares when they have favorable inside information. But such information has nothing to do with the history of the stock price. Your firm’s stock could be selling at half its price of a year ago, and yet you could have special information suggesting that it is still grossly overvalued. Or it may be undervalued at twice last year’s price.
Lesson 2: Trust Market Prices
In an efficient market, you can trust prices because they impound all available information about the value of each security. This means that in an efficient market, there is no way for most investors to achieve consistently superior rates of return. To do so, you not only need to know more than anyone else; you need to know more than everyone else. This message is important for the financial manager who is responsible for the firm’s exchange-rate policy or for its purchases and sales of debt. If you operate on the basis that you are smarter than others at predicting currency changes or interest-rate moves, you will trade a consistent financial policy for an elusive will-o’-the-wisp.
Procter & Gamble (P&G) supplied a costly example of this point in early 1994, when it lost $102 million in short order. It seems that in 1993, P&G’s treasury staff believed that interest rates would be stable and decided to act on this belief to reduce P&G’s borrowing costs. They committed P&G to deals with Bankers Trust designed to do just that. Of course, there was no free lunch. In exchange for a reduced interest rate, P&G agreed to compensate Bankers Trust if interest rates rose sharply. Rates did increase dramatically in early 1994, and P&G was on the hook. Then P&G accused Bankers Trust of misrepresenting the transactions—an embarrassing allegation since P&G was hardly investing as a widow or orphan—and sued Bankers Trust.
We take no stand on the merits of this litigation, which was eventually settled. But think of P&G’s competition when it traded in the fixed-income markets. Its competition included the trading desks of all the major investment banks, hedge funds, and fixed-income portfolio managers. P&G had no special insights or competitive advantages on the fixed-income playing field. Its decision to place a massive bet on interest rates was about as risky (and painful) as playing leapfrog with a unicorn.
Why was it trading at all? P&G would never invest to enter a new consumer market if it had no competitive advantage in that market. In Chapter 11, we argued that a corporation should not invest unless it can identify a competitive advantage and a source of economic rents. Market inefficiencies may offer economic rents from convergence trades, but few corporations have a competitive edge in pursuing these rents. As a general rule, nonfinancial corporations gain nothing, on average, by speculation in financial markets. They should not try to imitate hedge funds.33
The company’s assets may also be directly affected by management’s faith in its investment skills. For example, one company may purchase another simply because its management thinks that the stock is undervalued. On approximately half the occasions, the stock of the acquired firm will, with hindsight, turn out to be undervalued. But on the other half, it will be overvalued. On average, the value will be correct, so the acquiring company is playing a fair game except for the costs of the acquisition.
Lesson 3: Read the Entrails
If the market is efficient, prices impound all available information. Therefore, if we can only learn to read the entrails, security prices can tell us a lot about the future. For example, in Chapter 23, we show how information in a company’s financial statements can help to estimate the probability of bankruptcy. But the market’s assessment of the company’s securities can also provide important information about the firm’s prospects. Thus, if the company’s bonds are trading at low prices, you can deduce that the firm is probably in trouble.
Here is another example: Suppose that investors are confident that interest rates are set to rise over the next year. In that case, they will prefer to wait before they make long-term loans, and any firm that wants to borrow long-term money today will have to offer the inducement of a higher rate of interest. In other words, the long-term rate of interest will have to be higher than the one-year rate. Differences between the long-term interest rate and the short-term rate tell you something about what investors expect to happen to short-term rates in the future.
The nearby box shows how market prices reveal opinions about issues as diverse as a presidential election, the weather, or the demand for a new product.
Lesson 4: The Do-It-Yourself Alternative
In an efficient market, investors will not pay others for what they can do equally well themselves. As we shall see, many of the controversies in corporate financing center on how well individuals can replicate corporate financial decisions. For example, companies often justify mergers on the grounds that they produce a more diversified and hence more stable firm. But if investors can hold the stocks of both companies, why should they thank the companies for diversifying? It is much easier and cheaper for them to diversify than it is for the firm.
The financial manager needs to ask the same question when considering whether it is better to issue debt or common stock. If the firm issues debt, it will create financial leverage. As a result, the stock will be more risky, and it will offer a higher expected return. But stockholders can obtain financial leverage without the firm’s issuing debt; they can borrow on their own accounts. The problem for the financial manager is, therefore, to decide whether there is an advantage to the company issuing debt rather than the individual shareholder.
Lesson 5: Seen One Stock, Seen Them All
The elasticity of demand for any article measures the percentage change in the quantity demanded for each percentage addition to the price. If the article has close substitutes, the elasticity will be strongly negative; if not, it will be near zero. For example, coffee, which is a staple commodity, has a demand elasticity of about −.2. This means that a 5% increase in the price of coffee changes sales by −.2 × .05 = −.01; in other words, it reduces demand by only 1%. Consumers are likely to regard different brands of coffee as much closer substitutes for each other. Therefore, the demand elasticity for a particular brand could be in the region of, say, −2.0. A 5% increase in the price of Maxwell House relative to that of Folgers would in this case reduce demand by 10%.
Investors don’t buy a stock for its unique qualities; they buy it because it offers the prospect of a fair return for its risk. This means that stocks should be like very similar brands of coffee, almost perfect substitutes. Therefore, the demand for a company’s stock should be highly elastic. If its prospective return is too low relative to its risk, nobody will want to hold that stock. If the reverse is true, everybody will scramble to buy.
Suppose that you want to sell a large block of stock. Since demand is elastic, you naturally conclude that you need to cut the offering price only very slightly to sell your stock. Unfortunately, that doesn’t necessarily follow. When you come to sell your stock, other investors may suspect that you want to get rid of it because you know something they don’t. Therefore, they will revise their assessment of the stock’s value downward. Demand is still elastic, but the whole demand curve moves down. Elastic demand does not imply that stock prices never change when a large sale or purchase occurs; it does imply that you can sell large blocks of stock at close to the market price as long as you can convince other investors that you have no private information.
What If Markets Are Not Efficient? Implications for the Financial Manager
Our five lessons depend on efficient markets. What should financial managers do when markets are not efficient? The answer depends on the nature of the inefficiency.
What If Your Company’s Shares Are Mispriced? The financial manager may not have special information about future interest rates, but she definitely has special information about the value of her own company’s shares. Or investors may have the same information as management, but they may be slow in reacting to that information or may be infected with behavioral biases.
FINANCE IN PRACTICE
Prediction Markets
Stock markets allow investors to bet on their favorite stocks. Prediction markets allow them to bet on almost anything else. These markets reveal the collective guess of traders on issues as diverse as New York City snowfall, an avian flu outbreak, and the occurrence of a major earthquake.
Prediction markets are conducted on a number of small online exchanges such as the Iowa Electronic Markets and Predictit. Take presidential elections as an example. Prediction markets allowed you to bet that a particular candidate would win. To do so, you could buy a contract that paid $1 if he or she won and nothing otherwise. If you thought that the probability of victory was 55% (say), you would have been prepared to pay up to $.55 for this contract. Someone who was relatively pessimistic about that candidate’s chances would have been happy to sell you such a contract, for that sale would turn a profit if that candidate were to lose.
With many participants buying and selling, the market price of a contract reveals the collective wisdom of the crowd (or at least of those people who participated in the market). For example, take a look at panel a of the accompanying figure. It shows the contract prices through 2012 for a victory by Obama or Romney. For all of this period, those prices pointed to the likelihood of an Obama victory.
Of course, no set of individuals are perfect forecasters, and prediction markets are not unique in performing poorly when the participants have common biases or are focusing on the same information sources. That is what happened in the 2016 presidential election. Panel b of the figure shows that at no point during the contest did participants rank Donald Trump’s chances as better than evens.
Because prediction markets can pool ideas efficiently, some businesses have also formed internal prediction markets to survey the views of their staff. For example, Google operates an internal market to forecast product launch dates, the number of Gmail users, and other strategic questions.*
*See B. Cowgill and E. Zitzewitz, “Corporate Prediction Markets: Evidence from Google, Ford, and Firm X,” Review of Economic Studies 82 (October 2015), pp. 1309–1341.
Sometimes you hear managers thinking out loud like this:
Great! Our stock is clearly overpriced. This means we can raise capital cheaply and invest in Project X. Our high stock price gives us a big advantage over our competitors who could not possibly justify investing in Project X.
But that doesn’t make sense. If your stock is truly overpriced, you can help your current shareholders by selling additional stock and using the cash to invest in other capital market securities. But you should never issue stock to invest in a project that offers a lower rate of return than you could earn elsewhere in the capital market. Such a project would have a negative NPV. You can always do better than investing in a negative-NPV project: Your company can go out and buy common stocks. In an efficient market, such purchases are always zero NPV.
What about the reverse? Suppose you know that your stock is underpriced. In that case, it certainly would not help your current shareholders to sell additional “cheap” stock to invest in other fairly priced stocks. If your stock is sufficiently underpriced, it may even pay to forgo an opportunity to invest in a positive-NPV project rather than to allow new investors to buy into your firm at a low price. Financial managers who believe that their firm’s stock is underpriced may be justifiably reluctant to issue more stock, but they may instead be able to finance their investment program by an issue of debt. In this case the market inefficiency would affect the firm’s choice of financing but not its real investment decisions. In Chapter 15, we will have more to say about the financing choice when managers believe their stock is mispriced.
What If Your Firm Is Caught in a Bubble? On occasion, your company’s stock price may be swept up in a bubble like the dot-com boom of the late 1990s. Bubbles can be exhilarating. It’s hard not to join in the enthusiasm of the crowds of investors bidding up your firm’s stock price.34 On the other hand, financial management inside a bubble poses difficult personal and ethical challenges. Managers don’t want to “talk down” a high-flying stock price, especially when bonuses and stock-option payoffs depend on it. The temptation to cover up bad news or manufacture good news can be very strong. But the longer a bubble lasts, the greater the damage when it finally bursts. When it does burst, there will be lawsuits and possibly jail time for managers who have resorted to tricky accounting or misleading public statements in an attempt to sustain the inflated stock price.
When a firm’s stock price is swept upward in a bubble, CEOs and financial managers are tempted to acquire another firm using the stock as currency. One extreme example where this arguably happened is AOL’s acquisition of Time Warner at the height of the dot-com bubble in 2000. AOL was a classic dot-com company. Its stock rose from $2.34 at the end of 1995 to $75.88 at the end of 1999. Time Warner’s stock price also increased during this period, but only from $18.94 to $72.31. AOL’s total market capitalization was a small fraction of Time Warner’s in 1995, but overtook Time Warner’s in 1998. By the end of 1999, AOL’s outstanding shares were worth $173 billion, compared with Time Warner’s $95 billion. AOL managed to complete the acquisition before the Internet bubble burst. AOL-Time Warner’s stock then plummeted, but not by nearly as much as the stocks of dot-com companies that had not managed to find and acquire safer partners.35
SUMMARY
The patron saint of the Bolsa (stock exchange) in Barcelona, Spain, is Nuestra Señora de la Esperanza—Our Lady of Hope. She is the perfect patroness, for we all hope for superior returns when we invest. But competition between investors will tend to produce an efficient market. In such a market, prices will rapidly impound any new information, and it will be difficult to make consistently superior returns. We may indeed hope, but all we can rationally expect in an efficient market is a return just sufficient to compensate us for the time value of money and for the risks we bear.
The efficient-market hypothesis comes in three different flavors. The weak form of the hypothesis states that prices efficiently reflect all the information in the past series of stock prices. In this case, it is impossible to earn superior returns simply by looking for patterns in stock prices; in other words, prices follow a random walk. The semistrong form of the hypothesis states that prices reflect all published information. That means it is impossible to make consistently superior returns just by reading the newspaper, looking at the company’s annual accounts, and so on. The strong form of the hypothesis states that stock prices effectively impound all available information. It tells us that superior information is hard to find because in pursuing it you are in competition with thousands, perhaps millions, of active, intelligent, and greedy investors. The best you can do in this case is to assume that securities are fairly priced and to hope that one day Nuestra Señora will reward your humility.
During the 1960s and 1970s, every article on the topic seemed to provide additional evidence that markets are efficient. But then readers became tired of hearing the same message and wanted to read about possible exceptions. During the 1980s and 1990s, more and more anomalies and puzzles were uncovered. Bubbles, including the dot-com bubble of the 1990s and the real estate bubble of the 2000s, cast doubt on whether markets were always and everywhere efficient.
Limits to arbitrage can explain why asset prices may get out of line with fundamental values. Behavioral finance, which relies on psychological evidence to interpret investor behavior, is also consistent with many of the deviations from market efficiency. Behavioral finance says that investors are averse to even small losses, especially when recent investment returns have been disappointing. Investors may rely too much on a few recent events in predicting the future. They may be overconfident in their predictions and may be sluggish in reacting to new information.
There are plenty of quirks and biases in human behavior, so behavioral finance has plenty of raw material. But if every puzzle or anomaly can be explained by some recipe of quirks, biases, and hindsight, what have we learned? Research in behavioral finance literature is informative and intriguing, but not yet at the stage where a few parsimonious models can account for most of the deviations from market efficiency.
There has been a long-running debate on just how efficient markets are, and there seems no prospect of a universally accepted conclusion any time soon. Perhaps nothing could better illustrate the open nature of this debate than the decision to award the 2013 Nobel Prize in economics jointly to Eugene Fama, who has been dubbed the father of the “efficient market” hypothesis, and to Robert Shiller, whose work has focused on market inefficiencies. (The third recipient of the 2013 prize was Lars Hansen for his development of statistical methods that have been widely used to test theories of asset pricing.)36
For the corporate treasurer who is concerned with issuing or purchasing securities, the efficient-market theory has obvious implications. In one sense, however, it raises more questions than it answers. The existence of efficient markets does not mean that the financial manager can let financing take care of itself. It provides only a starting point for analysis. It is time to get down to details about securities and issue procedures. We start in Chapter 14.
FURTHER READING
Malkiel’s book is an-easy-to-read book on market efficiency. Fama has written two classic review articles on the topic:
B. G. Malkiel, A Random Walk Down Wall Street, 11th ed. (New York: W. W. Norton, 2016).
E. F. Fama, “Efficient Capital Markets: A Review of Theory and Empirical Work,” Journal of Finance 25 (May 1970), pp. 383–417.
E. F. Fama, “Efficient Capital Markets: II,” Journal of Finance 46 (December 1991), pp. 1575–1617.
There are several useful surveys of behavioral finance:
N. Barberis and R. H. Thaler, “A Survey of Behavioral Finance,” in G. M. Constantinides, M. Harris, and R. M. Stulz (eds.), Handbook of the Economics of Finance (Amsterdam: Elsevier Science, 2003).
M. Baker, R. S. Ruback, and J. Wurgler, “Behavioral Corporate Finance: A Survey,” in B. E. Eckbo (ed.), The Handbook of Empirical Corporate Finance (Amsterdam: Elsevier/North-Holland, 2007), Chapter 4.
R. J. Shiller, “Human Behavior and the Efficiency of the Financial System,” in J. B. Taylor and M. Woodford (eds.), Handbook of Macroeconomics (Amsterdam: North-Holland, 1999).
A. Shleifer, Inefficient Markets: An Introduction to Behavioral Finance (Oxford: Oxford University Press, 2000).
R. H. Thaler (ed.), Advances in Behavioral Finance (New York: Russell Sage Foundation, 1993).
D. Hirshleifer, “Behavioral Finance,” Annual Review of Financial Economics 7 (December 2015), pp. 133–159.
Some conflicting views on market efficiency are provided by:
G. W. Schwert, “Anomalies and Market Efficiency,” in G. M. Constantinides, M. Harris, and R. M. Stulz (eds.), Handbook of the Economics of Finance (Amsterdam: Elsevier Science, 2003).
M. Rubinstein, “Rational Markets: Yes or No? The Affirmative Case?” Financial Analysts Journal 57 (May–June 2001), pp. 15–29.
B. G. Malkiel, “The Efficient Market Hypothesis and Its Critics,” Journal of Economic Perspectives 17 (Winter 2003), pp. 59–82.
R. J. Shiller, “From Efficient Markets Theory to Behavioral Finance,” Journal of Economic Perspectives 17 (Winter 2003), pp. 83–104.
E. F. Fama and K. R. French, “Dissecting Anomalies,” Journal of Finance 63 (August 2008), pp. 1653–1678.
Bubbles are discussed in:
M. Brunnermeier, Asset Pricing under Asymmetric Information: Bubbles, Crashes, Technical Analysis, and Herding (Oxford: Oxford University Press, 2001).
A. Scherbina, “Asset Price Bubbles: A Selective Survey,” IMF Working Paper 13/45, 2013.
R. J. Shiller, Irrational Exuberance, 2nd ed. (Princeton, NJ: Princeton University Press, 2005).
For discussions of the rationality of prices in particular bull markets, see
L. Pastor and P. Veronesi, “Was There a Nasdaq Bubble in the Late 1990s?” Journal of Financial Economics 81 (2006), pp. 61–100.
E. Ofek and M. Richardson, “DotCom Mania: The Rise and Fall of Internet Stock Prices,” Journal of Finance 58 (2003), pp. 1113–1138.
K. French and J. M. Poterba, “Were Japanese Stock Prices Too High?” Journal of Financial Economics 29 (October 1991), pp. 337–363.
PROBLEM SETS
Select problems are available in McGraw-Hill’s Connect. Please see the preface for more information.
1. Market efficiency True or false? The efficient-market hypothesis assumes that
a. There are no taxes.
b. There is perfect foresight.
c. Successive price changes are independent.
d. Investors are irrational.
e. There are no transaction costs.
f. Forecasts are unbiased.
2. Market efficiency* True or false?
a. Financing decisions are less easily reversed than investment decisions.
b. Tests have shown that there is almost perfect negative correlation between successive price changes.
c. The semistrong form of the efficient-market hypothesis states that prices reflect all publicly available information.
d. In efficient markets, the expected return on each stock is the same.
3. Market efficiency Which (if any) of these statements are true? Stock prices appear to behave as though successive values
(a) Are random numbers.
(b) Follow regular cycles.
(c) Differ by a random number.
4. Market efficiency Supply the missing words: “There are three forms of the efficient-market hypothesis. Tests of randomness in stock returns provide evidence for the _____ form of the hypothesis. Tests of stock price reaction to well-publicized news provide evidence for the _____ form, and tests of the performance of professionally managed funds provide evidence for the _____ form. Market efficiency results from competition between investors. Many investors search for new information about the company’s business that would help them to value the stock more accurately. Such research helps to ensure that prices reflect all available information; in other words, it helps to keep the market efficient in the _____ form. Other investors study past stock prices for recurrent patterns that would allow them to make superior profits. Such research helps to ensure that prices reflect all the information contained in past stock prices; in other words, it helps to keep the market efficient in the _____ form.”
5. Market efficiency How would you respond to the following comments?
a. “Efficient market, my eye! I know lots of investors who do crazy things.”
b. “Efficient market? Balderdash! I know at least a dozen people who have made a bundle in the stock market.”
c. “The trouble with the efficient-market theory is that it ignores investors’ psychology.”
d. “Despite all the limitations, the best guide to a company’s value is its written-down book value. It is much more stable than market value, which depends on temporary fashions.”
6. Market efficiency Respond to the following comments:
a. “The random-walk theory, with its implication that investing in stocks is like playing roulette, is a powerful indictment of our capital markets.”
b. “If everyone believes you can make money by charting stock prices, then price changes won’t be random.”
c. “The random-walk theory implies that events are random, but many events are not random. If it rains today, there’s a fair bet that it will rain again tomorrow.”
7. Market efficiency “If the efficient-market hypothesis is true, the pension fund manager might as well select a portfolio with a pin.” Explain why this is not so.
8. Market efficiency evidence* Fama and French show that average stock returns on firms with small market capitalizations have been significantly higher than average returns for “large-cap” firms. What are the possible explanations for this result? Does the result disprove market efficiency? Explain briefly.
9. Market efficiency evidence* Which of the following observations appear to indicate market inefficiency? Explain whether the observation appears to contradict the weak, semistrong, or strong form of the efficient-market hypothesis.
a. Tax-exempt municipal bonds offer lower pretax returns than taxable government bonds.
b. Managers make superior returns on purchases of their company’s stock.
c. There is a positive relationship between the return on the market in one quarter and the change in aggregate profits in the next quarter.
d. There is some evidence that stocks that have appreciated unusually in the recent past continue to do so in the future.
e. The stock of an acquired firm tends to appreciate in the period before the merger announcement.
f. Stocks of companies with unexpectedly high earnings appear to offer high returns for several months after the earnings announcement.
g. Very risky stocks on average give higher returns than safe stocks.
10. Market efficiency evidence Give two or three examples of research results or events that raise doubts about market efficiency. Briefly explain why.
11. Market efficiency implications Here again are the five lessons of market efficiency. For each lesson give an example showing the lesson’s relevance to financial managers.
a. Markets have no memory.
b. Trust market prices.
c. Read the entrails.
d. The do-it-yourself alternative.
e. Seen one stock, seen them all.
12. Market efficiency implications Two financial managers, Alpha and Beta, are contemplating a chart showing the actual performance of the Standard and Poor’s Composite Index over a five-year period. Each manager’s company needs to issue new shares of common stock sometime in the next year.
Alpha: My company’s going to issue right away. The stock market cycle has obviously topped out, and the next move is almost surely down. Better to issue now and get a decent price for the shares.
Beta: You’re too nervous; we’re waiting. It’s true that the market’s been going nowhere for the past year or so, but the figure clearly shows a basic upward trend. The market’s on the way up to a new plateau.
What would you say to Alpha and Beta?
13. Market efficiency implications What does the efficient-market hypothesis have to say about these two statements?
a. “I notice that short-term interest rates are about 1% below long-term rates. We should borrow short-term.”
b. “I notice that interest rates in Japan are lower than rates in the United States. We would do better to borrow Japanese yen rather than U.S. dollars.”
14. Market efficiency implications* True or false?
a. If markets are efficient, shareholders should expect to receive only the risk-free interest rate on their investment.
b. If markets are efficient, investment in the stock market is a mug’s game.
c. If markets are efficient, investors should just invest in firms with good management and an above-average track record.
d. In an efficient market, investors should expect stocks to sell at a fair price.
15. Abnormal returns* Analysis of 60 monthly rates of return on United Futon common stock indicates a beta of 1.45 and an alpha of –.2% per month. A month later, the market is up by 5%, and United Futon is up by 6%. What is Futon’s abnormal rate of return?
16. Abnormal returns The second column in Table 13.1 shows the monthly return on the British FTSE 100 index from January 2015 through July 2017. The remaining columns show returns on the stocks of two firms—Executive Cheese and Paddington Beer. Both firms announced their earnings in July 2017. Calculate the average abnormal return of the two stocks during the month of the earnings announcement. The earnings of one of these stocks slightly disappointed investors and the earnings of the other were slightly better than expected. Which was which?
|
Month |
Market Return |
Executive Cheese Return |
Paddington Beer Return |
|
January 2015 |
2.8 |
3.6 |
1.6 |
|
February |
2.9 |
7.0 |
1.5 |
|
March |
–2.5 |
–2.2 |
–0.7 |
|
April |
2.8 |
3.1 |
3.0 |
|
May |
0.3 |
0.2 |
0.1 |
|
June |
–3.9 |
–6.5 |
1.1 |
|
July |
–0.2 |
0.1 |
0.6 |
|
August |
–6.7 |
–9.8 |
–4.6 |
|
September |
–3.0 |
–7.2 |
–5.3 |
|
October |
4.9 |
5.8 |
6.1 |
|
November |
–0.1 |
0.2 |
0.1 |
|
December |
–1.8 |
–1.0 |
–1.2 |
|
January 2016 |
–2.5 |
–3.1 |
0.6 |
|
February |
0.2 |
0.3 |
1.7 |
|
March |
1.3 |
1.7 |
2.1 |
|
April |
1.1 |
1.1 |
3.0 |
|
May |
–0.2 |
0.1 |
1.6 |
|
June |
4.4 |
7.4 |
2.8 |
|
July |
3.4 |
4.0 |
0.9 |
|
August |
0.8 |
1.2 |
1.0 |
|
September |
1.7 |
5.1 |
1.3 |
|
October |
0.8 |
3.7 |
–1.6 |
|
November |
–2.4 |
–2.7 |
–1.2 |
|
December |
5.3 |
10.7 |
1.8 |
|
January 2017 |
–0.6 |
–0.4 |
–0.7 |
|
February |
2.3 |
2.8 |
2.4 |
|
March |
0.8 |
0.7 |
0.8 |
|
April |
–1.6 |
–1.0 |
–1.2 |
|
May |
4.4 |
6.2 |
–3.7 |
|
June |
–2.8 |
–3.2 |
–1.3 |
|
July |
2.7 |
3.0 |
2.9 |
TABLE 13.1 See Problem 16. Rates of return in percent per month.
17. Abnormal returns Here are alphas and betas for Estée Lauder and Caterpillar Tractor for the 60 months ending June 2017. Alpha is expressed as a percent per month.
|
Alpha |
Beta |
|
|
Estée Lauder |
0.48 |
0.70 |
|
Caterpillar Tractor |
–0.41 |
1.26 |
18. Explain how these estimates would be used to calculate an abnormal return.
19. Behavioral finance Explain how incentive and agency problems might contribute to mispricing of securities or to bubbles. Give examples.
20. Behavioral finance True or false?
a. Most managers tend to be overconfident.
b. Psychologists have found that, once people have suffered a loss, they are more relaxed about the possibility of incurring further losses.
c. Psychologists have observed that people tend to put too much weight on recent events when forecasting.
d. Behavioral biases open up the opportunity for easy arbitrage profits.
21. Behavioral finance Many commentators have blamed the subprime crisis on “irrational exuberance.” What is your view? Explain briefly.
CHALLENGE
21. Market efficiency “The strong form of the efficient-market hypothesis is nonsense. Look at mutual fund X; it has had superior performance for each of the last 10 years.” Does the speaker have a point? Suppose that there is a 50% probability that X will obtain superior performance in any year simply by chance.
a. If X is the only fund, calculate the probability that it will have achieved superior performance for each of the past 10 years.
b. Now recognize that there are nearly 10,000 mutual funds in the United States. What is the probability that by chance there is at least 1 out of 10,000 funds that obtained 10 successive years of superior performance?
22. Bubbles Some extreme bubbles are obvious with hindsight, after they burst. But how would you define a bubble? There are many examples of good news and rising stock prices, followed by bad news and falling stock prices. Can you set out rules and procedures to distinguish bubbles from the normal ups and downs of stock prices?
FINANCE ON THE WEB
Use finance.yahoo.com to download daily prices for five U.S. stocks for a recent five-year period. For each stock, construct a scatter diagram of successive returns as in Figure 13.1. Calculate the correlation between the returns on successive days. Do you find any consistent patterns?
1NPV would be negative only if GENX were foolish enough to pay more than the market rate or were somehow forced to do so.
2See M. G. Kendall, “The Analysis of Economic Time Series, Part I. Prices,” Journal of the Royal Statistical Society 96 (1953), pp. 11–25. Kendall’s idea was not wholly new. It had been proposed in an almost forgotten thesis written 53 years earlier by a French doctoral student, Louis Bachelier. Bachelier’s accompanying development of the mathematical theory of random processes anticipated by five years Einstein’s famous work on the random Brownian motion of colliding gas molecules. See L. Bachelier, Théorie de la Speculation (Paris: Gauthiers-Villars, 1900). Reprinted in English (A. J. Boness, trans.) in P. H. Cootner (ed.), The Random Character of Stock Market Prices (Cambridge, MA: MIT Press, 1964), pp. 17–78.
3The drift is equal to the expected outcome: (1/2)(3) + (1/2)(−2.5) = .25%.
4The correlation coefficient between successive observations is known as the autocorrelation coefficient. An autocorrelation of −.037 implies that, if Microsoft’s stock price rose by 1% more than the average yesterday, your best forecast of today’s change would be a mere .037% less than the average.
5See N. Jegadeesh and S. Titman, “Returns to Buying Winners and Selling Losers: Implications for Market Efficiency,” Journal of Finance 48 (March 1993), pp. 65–91. Many practitioners now add a momentum factor to the Fama–French three-factor model, which we discussed in Section 8-3. See M. M. Carhart, “On Persistence in Mutual Fund Performance,” Journal of Finance 52 (March 1997), pp. 57–82.
6High-frequency trading now accounts for roughly half of overall stock-market volume. For a readable and critical book on high-frequency trading, see M. Lewis, Flash Boys: A Wall Street Revolt (New York: W. W. Norton & Co., 2014).
7The price response to negative reports took longer, 15 minutes on average, probably because of the costs and delays of short-selling. See J. A. Busse and T. C. Green, “Market Efficiency in Real Time,” Journal of Financial Economics 65 (2002), pp. 415–437.
8Suppose, for example, that the market return is 12% per year. With 250 trading days in the year, the average daily return is (1.12)1/250 –1 = .00045, or .045%.
9It is important when estimating α and β that you choose a period in which you believe that the stock behaved normally. If its performance was abnormal, then estimates of α and β cannot be used to measure the returns that investors expected. As a precaution, ask yourself whether your estimates of expected returns look sensible. Methods for estimating abnormal returns are analyzed in A. C. MacKinlay, “Event Studies in Economics and Finance,” Journal of Economic Literature 35 (1997), pp. 13–39; and also S. P. Kothari and J. B. Warner, “Econometrics of Event Studies,” in B. E. Eckbo (ed.), The Handbook of Empirical Corporate Finance (Amsterdam: Elsevier/North-Holland, 2007), Chapter 1.
10Abnormal returns are also often calculated using the Fama–French three-factor model that we discussed in Chapter 8. The stock return is adjusted for the market return, the difference between the returns on small- and large-company stocks, and the difference between the returns on high and low book-to-market firms.
11Investors may respond on day 0 if the announcement is made during the hours of trading. Otherwise, they will respond on day 1.
12For evidence on the pricing of Siamese twins see K. A. Froot and E. Dabora, “How Are Stock Prices Affected by the Location of Trade?” Journal of Financial Economics 53 (August 1999), pp. 189–216; and, for more recent data, A. De Jong, L. Rosenthal, and M. A. Van Dijk, “The Risk and Return of Arbitrage in Dual-Listed Companies,” Review of Finance 13 (2009), pp. 495–520.
13See, for example, B. G. Malkiel, “Returns from Investing in Equity Mutual Funds 1971 to 1991,” Journal of Finance 50 (June 1995), pp. 549–572; and M. M. Carhart, “On Persistence in Mutual Fund Performance,” Journal of Finance 52 (March 1997), pp. 57–82. Some evidence of slight persistence in performance is provided in E. F. Fama and K. R. French, “Luck versus Skill in the Cross-Section of Mutual Fund Returns,” Journal of Finance 65 (October 2010), pp. 1915–1947; and in R. Kosowski, A. Timmermann, R. Wermers, and H. White, “Can Mutual Fund ‘Stars’ Really Pick Stocks? New Evidence from a Bootstrap Analysis,” Journal of Finance 61 (December 2006), pp. 2551–2595. See also M. J. Gruber, “Another Puzzle: The Growth in Actively Managed Mutual Funds,” Journal of Finance 51 (July 1996), pp. 783–810; and J. Berk and J. H. Van Binsbergen, “Measuring Skill in the Mutual Fund Industry,” Journal of Financial Economics 118 (October 2015), pp. 1–20.
14See, for example, the Persistence Scorecard, which is published by Standard & Poor’s twice a year.
15See S. J. Grossman and J. E. Stiglitz, “On the Impossibility of Informationally Efficient Markets,” American Economic Review 70 (June 1980), pp. 393–408.
16A. Dyck, K. V. Lins, and L. Pomorski, “Does Active Management Pay? New International Evidence,” Review of Asset Pricing Studies 3 (December 2013), pp. 200–228.
17Bubbles are not necessarily irrational. See M. Brunnermeier, Asset Pricing under Asymmetric Information: Bubbles, Crashes, Technical Analysis and Herding (Oxford: Oxford University Press, 2001).
18See W. T. Ziemba and S. L. Schwartz, Invest Japan (Chicago: Probus Publishing Co., 1992), p. 109.
19M. Cooper, O. Dimitrov, and P. R. Rau, “A Rose.com by Any Other Name,” Journal of Finance 56 (2001), pp. 2371–2388.
20For an analysis of Japanese stock prices, see K. French and J. M. Poterba, “Were Japanese Stock Prices Too High?” Journal of Financial Economics 29 (October 1991), pp. 337–363. For more on dot-com stock prices, see E. Ofek and M. Richardson, “The Valuation and Market Rationality of Internet Stock Prices,” Oxford Review of Economic Policy 18 (Autumn 2002), pp. 265–287.
21Prospect theory was first set out in D. Kahneman and A. Tversky, “Prospect Theory: An Analysis of Decision under Risk,” Econometrica 47 (1979), pp. 263–291.
22The effect is described in R. H. Thaler and E. J. Johnson, “Gambling with the House Money and Trying to Break Even: The Effects of Prior Outcomes on Risky Choice,” Management Science 36 (1990), pp. 643–660. The implications of prospect theory for stock returns are explored in N. Barberis, M. Huang, and T. Santos, “Prospect Theory and Asset Prices,” Quarterly Journal of Economics 116 (February 2001), pp. 1–53.
23See D. Kahneman, Thinking Fast and Slow (New York: Farrar, Straus, and Giroux, 2011).
24For a discussion of the overconfidence bias in financial markets, see K. Daniel and D. Hirshleifer, “Overconfident Investors, Predictable Returns, and Excessive Trading,” Journal of Economic Perspectives 29 (Fall 2015), pp. 61–88.
25For evidence on the link between sentiment measures and stock returns, see M. Baker and J. Wurgler, “Investor Sentiment in the Stock Market,” Journal of Economic Perspectives 21 (2007), pp. 129–151.
26The term “irrational exuberance” was coined by Alan Greenspan, former chairman of the Federal Reserve Board, to describe the dot-com boom. It was also the title of a book by Robert Shiller that examined the boom. See R. Shiller, Irrational Exuberance (New York: Broadway Books, 2001).
27Investment and brokerage firms identify shares eligible for lending and arrange to make them available to short-sellers. The supply of shares that can be borrowed is limited. You are charged a fee for borrowing the stock, and you are required to put up collateral to protect the lender in case the share price rises and the short-seller is unable to repurchase and return the shares. Putting up collateral is costless if the short-seller gets a market interest rate, but sometimes only lower interest rates are offered.
28The Russian debt moratorium was unexpected and unusual because the debt had only recently been issued and was denominated in roubles. The government preferred to default rather than to print roubles to service the debt.
29Investors who did foresee that the fall in house prices would lead to the subprime debacle were able to earn high profits. For example, John Paulson, the hedge fund manager, earned $3.7 billion in 2007 as a result (Financial Times, January 15, 2008, and June 18, 2008).
30See F. Allen, “Do Financial Institutions Matter?” Journal of Finance 56 (2001), pp. 1165–1175.
31See, for example, P. Asquith and D. W. Mullins, Jr., “Equity Issues and Offering Dilution,” Journal of Financial Economics 15 (January–February 1986), pp. 61–89; and (for the U.K.) P. R. Marsh, “The Choice between Equity and Debt: An Empirical Study,” Journal of Finance 37 (March 1982), pp. 121–144.
32If high stock prices signal expanded investment opportunities and the need to finance these new investments, we would expect to see firms raise more money in total when stock prices are historically high. But this does not explain why firms prefer to raise the extra cash at these times by an issue of equity rather than debt.
33There are, of course, some likely exceptions. Hershey and Nestlé are credible traders in cocoa futures markets. The major oil companies probably have special skills and knowledge relevant to energy markets.
34See J. C. Stein, “Rational Capital Budgeting in an Irrational World,” Journal of Business 69 (October 1996), pp. 429–455.
35Pavel Savor and Qi Lu provide evidence that many other firms were able to benefit from stock acquisitions. See “Do Stock Mergers Create Value for Acquirers?” Journal of Finance 64 (June 2009), pp. 1061–1097.
36See http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/2013/ for their Prize Lectures.