Figure 6: a,b US dealer positioning and b expected returns. Figure adapted from Adrian et al. Figure 7: US dealer VaR and funding costs. Figure 8: Shadow credit intermediation: a global nonbank credit intermediation and b US nonbank credit intermediation. Figure 9: Securitization activity. Figure Corporate bond market liquidity measured by price impact. Financial crises are runs on short-term debt. Whatever its form, short-term debt is an inherent feature of a market economy. A run is an information event in which holders of short-term debt no longer want to lend to banks because they receive information Figure 1: The frequency of financial crises.
Data from Bordo et al. Email Share.
Larry G. Download Citation Citation Alerts. Abstract The Ellsberg paradox suggests that people's behavior is different in risky situations—when they are given objective probabilities—from their behavior in ambiguous situations—when they are not told the odds as is typical in financial markets. Related Articles Journal Most Downloaded. Abstract - Figures Preview. Abstract This paper surveys the recent literature on CEO compensation. Behavioral Finance David Hirshleifer Vol.
Abstract Preview. Abstract Behavioral finance studies the application of psychology to finance, with a focus on individual-level cognitive biases.
Abstract Ten years after the financial crisis of , there is widespread agreement that the boom in mortgage lending and its subsequent reversal were at the core of the Great Recession. A above. It also yields further implications for the correlation between public information announcements such as managers' forecasts or financial reports of sales, cash flows, or earnings and future price changes. We retain the basic structure considered in earlier sections.
As before, an informed investor forms expectations about value rationally using Bayesian updating except for his perceptions of his private information precision. At every subsequent release of public information the investor updates his estimate of the noise variance. If the new public signal disconfirms his private signal, the investor revises the estimated precision downward, but not by as much. Rationally he should allow for the fact that v C , t is an estimate. We expect that the essential results are not sensitive to this simplification.
We also make the investor's initial estimate of his precision equal to the true precision of his private signal.
Average price path following private information shock. This figure shows the average price path calculated using the simulation in Section III. The price initially jumps from 0 up to 0. On average, the price continues moving up, reaching a maximum of 0. The average price then declines, and eventually asymptotes to zero. Thus, there is an initial overreaction phase in which the price moves away from the true value as the investor's attribution bias causes him to place more weight, on average, on his private information. Eventually the public information become precise enough that the investor revises his valuation of the security downward.
This is the correction phase.
This changing confidence is the source of the overreacting average price trend. Average price change autocorrelations. This figure presents the unconditional average autocorrelations at lags between 1 period and periods , calculated using the simulation described in Section III. In unreported simulations, these coefficients exhibit behavior similar to that of the autocorrelations. The conclusions of this simulation are summarized as follows. Result 1. This contrasts with a steadily declining price path if there is no attribution bias.
Result 2. Result 3. Recent research indicates strong and consistent evidence of momentum in the United States and in European countries, but weak and insignificant evidence of momentum in Japan see, e. Our model provides one possible psychological foundation for a stochastic tendency for trades to be correlated with past price movements, which can create an appearance of positive feedback trading.
Finally, we consider the implications of this model for the correlation between accounting performance and future price changes. Accounting information sales, earnings, etc. If this is positive, the first private signal was probably also positive. Eventually prices will decline as the cumulative public signal becomes more precise and informed investors put less weight on their signal. Correlation between information changes and future price changes.
These are calculated using the simulated dynamic model of Section III. To evaluate the above conjecture, we again calculate average correlations using our simulation as follows. These correlations are then averaged over the Monte Carlo draws. The average correlations are plotted in Figure 4.
This simulation yields the following result. Result 4. To summarize, the analysis suggests that the conclusion from the basic model that investors overreact to private signals holds in the dynamic model. Though investors underreact on average to public signals, public signals initially tend to stimulate additional overreaction to a previous private signal. Thus, underreaction is mixed with continuing overreaction. Further, the literature cited in Section III.
Empirical securities markets research in the last three decades has presented a body of evidence with systematic patterns that are not easy to explain with rational asset pricing models. Some studies conclude that the market underreacts to information, others find evidence of overreaction. The theory implies that investors overreact to private information signals and underreact to public information signals. In contrast with the common correspondence of positive negative return autocorrelations with underreaction overreaction to new information, we show that positive return autocorrelations can be a result of continuing overreaction.
This pattern has sometimes been interpreted as market underreaction to the event. Such predictability can arise from underreaction only if the event is chosen in response to market mispricing.
Alternatively, predictability can arise when the public event triggers a continuing overreaction. The basic noise trading approach to securities markets e. Our approach is based on the premise that an important class of mistakes by investors involves the misinterpretation of genuine new private information. Thus, our model endogenously generates trading mistakes that are correlated with fundamentals. This structure provides predictions about the dynamic behavior of asset prices which depend on the particular cognitive error that is assumed. Of course, one could arbitrarily specify whatever pattern of correlated noise is needed to match empirically observed ex post price patterns.
Such an exercise would merely be a relabeling of the puzzle, not a theory. Some models of exogenous noise trades e.
As noted in the introduction, a possible objection to models with imperfectly rational traders is that wealth may shift from foolish to rational traders until price setting is dominated by rational traders. For example, in our model the overconfident informed traders lose money on average. Another distinct benefit of overconfidence is that it can act like a commitment to trade aggressively. Because this may intimidate competing informed traders, those known to be overconfident may earn higher returns see Kyle and Wang and Benos Moving beyond the confines of the formal model, we expect the effects of overconfidence to be more severe in less liquid securities and assets.
Suppose that all investors are risk averse and that prices are not fully revealing perhaps because of noisy liquidity trading.
If rational arbitrageurs face fixed setup costs of learning about a stock, then large liquid stocks will tend to be better arbitraged more rationally priced than small stocks, because it is easier to cover the fixind investigation cost in large, liquid stocks. This suggests greater inefficiencies for small stocks than for large stocks, and for less liquid securities and assets such as real estate than for stocks. Furthermore, because the model is based on overconfidence about private information, the model predicts that return predictability will be strongest in firms with the greatest information asymmetries.
This also implies greater inefficiencies in the stock prices of small companies. It is an open question whether the overconfident traders in the model can be identified with a specific category of investor, such as institutions, other investment professionals, small individual investors, or all three. Even small individual investors, who presumably have less information, may still be overconfident. Some smart contrarian investors could be viewed as rational and informed, and including such traders would not change the qualitative nature of the model predictions.
An identification of the confidence characteristics of different observable investor categories may generate further empirical implications, and is an avenue for further research. This appendix cites the relevant literature for the anomalies mentioned in the first paragraph of the introduction. Events for which this has been found include: 1. Tender offer and open market repurchases Lakonishok and Vermaelen , Ikenberry, Lakonishok, and Vermaelen 3.
Analyst recommendations Groth et al. Dividend initiations and omissions Michaely, Womack, and Thaler 5. Earnings surprises at least for a period after the event Bernard and Thomas , , Brown and Pope 7. Public announcement of previous insider trades Seyhun ; see also Seyhun , and Rozeff and Zaman 8.