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This work was completed while the author was visiting the IMF Institute. This material was prepared as background for courses on the topic. I would like to thank Jorge Roldós, Ayhan Kose, and Marco Terrones for their helpful comments and suggestions.
England banned short selling for much of the 18th and 19th centuries, Napoleon declared short sellers to be enemies of the state, and many countries today either ban or severely restrict short selling. Short sellers make money precisely when other investors are losing it. Lamont (2004) describes a variety of tactics that firms employ against short sellers. On average, the firms in his sample that started various actions against short sellers ended up losing 42 percent of their market capitalization over the next three years, suggesting that they had indeed been overvalued, just as the short sellers suspected.
In addition to feedback traders, institutional restrictions may serve to amplify past price movements. For example, when it comes to downward prices movements, many institutions are forced to sell their shares of a stock once the firm’s market capitalization falls below the institution’s investible universe. This selling pressure, now unrelated to past news, causes a further price decline. In addition, a lower level of institutional ownership is likely to reduce the stock’s liquidity, making it even less attractive to investors and forcing the price to drop even further.
We will use the terms “discount rate” and “required rate of return” interchangeably.
Some studies dispute that there was a stock market bubble in the United States in the 1920s (e.g., Siegel (2003)).
Fiat money is an example of an infinitely-lived asset with a bubble, since the intrinsic value of fiat money is zero.
Conlon (2004) is able to achieve an equilibrium in which bubbles can exist with a simpler setting and without assuming a lack of common knowledge.
Extending the dataset to December 31, 2010 and using aggregate dividends in place of cash flows, we estimate the bubble to be 67.84 percent of the S&P 500 index price.
The herding incentive is relaxed if, in addition to caring about reputation, managers also care about their investment return.
In a popular account of the recent U.S. real estate bubble, Lewis (2010) describes a few of the very small number of hedge fund managers who realized that mortgage-backed securities contained a bubble; these managers were all outsiders with respect to Wall Street’s investment community, which provided them with sufficient separation to be able to think independently.
Regulation Fair Disclosure, adopted in 2000 in response to analyst scandals in the United States, is designed to prevent selective information disclosure but has not been entirely effective.
In contrast to the model of Hong and Stein (1999), in this model, feedback traders always lose money. The other difference is that due to the short horizon of the model, prices do not overshoot their fundamentals in the absence of speculators, but they would if the number of periods were larger because feedback traders would continue to trade on past price movements, pushing prices past their fair values.
To simply the calculations, the set of investors who suffer from biased self-attribution are assumed to be risk-neutral; therefore, they set the equilibrium price.
In contrast, investors operating with a particular model in mind, typically underreact to the information contained in the most recent earnings realization.
When the mean-reversion model is used, investors instead underreact to the information in the current earnings realization, mistakenly thinking that the recent earnings innovation will be reversed in the future.
This formula ignores real estate taxes, depreciation, maintenance costs, and the tax benefit of ownership, which could be easily added in.
Consistently, Hayunga and Lung (2011) show that high house price-to-rent ratios are associated with high turnover, measured as the number of house sales to total housing inventory.
Underwriters generally require that existing stockholders do not sell their shares for a certain time period after the IPO (with 180 days being standard). The stated purpose of this restriction is to prevent flooding the market with additional shares before the shares issued during the IPO are absorbed.
An angel investor is a wealthy individual who provides capital in the early stages of a start-up.
See also the model of Hong, Scheinkman, and Xiong (2008) relating the size of equity bubbles to the supply of tradeable shares.
For example, during the dot-com bubble, the price run-up of internet stocks was accompanied by heavy trading. Hong and Stein (2007) document that monthly turnover of internet stocks exceeded 50 percent in 12 out of 24 months preceding the internet index peak in February 2000, while the average turnover for non-internet stocks was in the range of only 10-15 percent. After the internet index decline, the turnover of internet stocks dropped to the average market level.
For example, Hoyt (1933) writes about how, in the later stages of the 1920s real estate bubble in Chicago, an illusion of rising prices was created by arms-length transactions in which properties were exchanged at inflated prices between related parties.
Baker and Wurgler (2007) combine a number of market indicators to construct a measure of investor sentiment.
Of course, a completely different reason for why bubbles are not arbitraged away is that they are not bubbles to begin with; several studies argue that the observed patterns of rapid price increases followed by crashes do not have to be attributed to bubbles. For example, Zeira (1999) models a setting in which such price patterns are frequently observed, when a market expands to a new capacity, which is unknown until it is reached. In the transition phase, before the new capacity is reached, prices increase rapidly. However, right after the new capacity is reached, prices crash. The reason is that the last price before the crash was based on the growth rate forecast extrapolated from the recently high growth rates. Therefore, even though just before the crash the price was too high, it was the correct price given the information known at that time and the crash could not have been anticipated with ex-ante-known information. Similarly, Pástor and Veronesi (2003) argue that patterns of rapidly rising and then falling prices need not reflect mispricing; they also attribute such price trajectories to technological revolutions, during which the productivity of the new technology is subject to learning. During the adoption period of the new technology, positive cash flow news push prices up, but as the technology becomes a larger part of the economy, its risk gradually changes from idiosyncratic to systematic, leading to a higher discount rate. The higher discount rate effect eventually starts to dominate the positive cash flow effect and pushes prices down. In both models, even though the price experiences a fast increase followed by a decrease, prices do not drop all the way down to the starting level. Finally, Pástor and Veronesi (2006) argue that there was no dotcom bubble. Rather, the high valuations at the peak could have been justified by the uncertainty about the future growth rate, increasing expected firm values through Jensen’s inequality, and the subsequent price drop by the downward revisions in investors’ expectations, as described in the model of Zeira (1999).
Large inflows of foreign capital into U.S. Treasury and agency bonds also contributed to low mortgage rates as these rates are linked to U.S. government bonds.
Some mortgage originators were required to keep a portion of the loans on their books in order to reduce the moral hazard problem; however, the high demand for new loans at the time overshadowed concerns about the increase in risk exposure. As a result, many lending banks suffered large losses and went bankrupt after the housing market collapse (see International Monetary Fund (2009)).
For example, the average down payment made by Alt A borrowers (a category between prime and subprime) fell from 14 percent in 2000 to only 2.7 percent in 2006.
Credit ratings are based on an estimate of the underlying debt security’s expected payoff. Coval, Jurek, and Stafford (2009) show that expected payoffs of CDOs (and even more so, the so-called CDO2’s, which are made up of lower tranches of straight CDOs) are highly sensitive to the correlations between payoffs of the underlying debt securities, as well as to the underlying securities’ default probabilities and their default recovery rates. Credit rating agencies made overly optimistic assumptions about all these inputs in their rating models. Correlations between underlying mortgage-backed securities turned out to be higher than expected as these mortgages were originated at similar times and in similar geographic areas. Default probabilities exceeded the expectations and recovery rates fell below expectations due to lower than expected borrower quality. The authors further argue that investors failed to take into account that senior tranches of CDO securities had a much higher exposure to systematic risk than similarly rated corporate bonds and, as a result, did not demand sufficient compensation for their exposure to systematic risk.
In that year, the Case-Shiller 20-city index fell by 18.61 percent. In 2007, it fell by 9.03 percent.