I document a delisting bias in the stock return data base maintained by the Center for Research in Security Prices (CRSP). I find that delists for bankruptcy and other negative reasons are generally surprises and that correct delisting returns are not available for most of the stocks that have been delisted for negative reasons since 1962. Using over-the-counter price data, I show that the omitted delisting returns are large. Implications of the bias are discussed.

The author documents a delisting bias in the stock return data base maintained by the Center for Research in Security Prices. He finds that delists for bankruptcy and other negative reasons are generally surprises and that correct delisting returns are not available for most of the stocks that have been delisted for negative reasons since 1962. Using over-the-counter price data, the author shows that the omitted delisting returns are large. Implications of the bias are discussed.

I document a delisting bias in the stock return data base maintained by the Center for Research in Security Prices (CRSP). I find that delists for bankruptcy and other negative reasons are generally surprises and that correct delisting returns are not available for most of the stocks that have been delisted for negative reasons since 1962. Using over‐the‐counter price data, I show that the omitted delisting returns are large. Implications of the bias are discussed.

We examine the accuracy and contribution of the Merton distance to default (DD) model, which is based on Merton's (1974) bond pricing model. We compare the model to a “naïve” alternative, which uses the functional form suggested by the Merton model but does not solve the model for an implied probability of default. We find that the naïve predictor performs slightly better in hazard models and in out-of-sample forecasts than both the Merton DD model and a reduced-form model that uses the same inputs. Several other forecasting variables are also important predictors, and fitted values from an expanded hazard model outperform Merton DD default probabilities out of sample. Implied default probabilities from credit default swaps and corporate bond yield spreads are only weakly correlated with Merton DD probabilities after adjusting for agency ratings and bond characteristics. We conclude that while the Merton DD model does not produce a sufficient statistic for the probability of default, its functional form is useful for forecasting defaults.

A large literature finds evidence that pricing kernels nonparametrically estimated from option prices and historical returns are not monotonically decreasing in market index returns. We argue that existing estimation methods are inconsistent and propose a new nonparametric estimator of the pricing kernel that reflects the information available to investors who set asset prices. In simulations, the estimator outperforms existing techniques. Our empirical estimates using S&P 500 index option data from 1996 to 2014 and FTSE 100 index option data from 2002 to 2014 suggest that the “pricing kernel puzzle” is due to flaws in existing estimators rather than a behavioral or economic phenomenon.

[Using a large sample of individual investor records over a nine-year period, we analyze survival rates, the disposition effect, and trading performance at the individual level to determine whether and how investors learn from their trading experience. We find evidence of two types of learning: some investors become better at trading with experience, while others stop trading after realizing that their ability is poor. A substantial part of overall learning by trading is explained by the second type. By ignoring investor attrition, the existing literature significantly overestimates how quickly investors become better at trading.]

This paper documents strong evidence for behavioral biases among Chicago Board of Trade proprietary traders and investigates the effect these biases have on prices. Our traders appear highly loss‐averse, regularly assuming above‐average afternoon risk to recover from morning losses. This behavior has important short‐term consequences for afternoon prices, as losing traders actively purchase contracts at higher prices and sell contracts at lower prices than those that prevailed previously. However, the market appears to distinguish these risk‐seeking trades from informed trading. Prices set by loss‐averse traders are reversed significantly more quickly than those set by unbiased traders.

This paper examines expected option returns in the context of mainstream asset‐pricing theory. Under mild assumptions, expected call returns exceed those of the underlying security and increase with the strike price. Likewise, expected put returns are below the risk‐free rate and increase with the strike price. S&P index option returns consistently exhibit these characteristics. Under stronger assumptions, expected option returns vary linearly with option betas. However, zero‐beta, at‐the‐money straddle positions produce average losses of approximately three percent per week. This suggests that some additional factor, such as systematic stochastic volatility, is priced in option returns.

We analyze the information content of the ambient noise level in the Chicago Board of Trade's 30‐year Treasury Bond futures trading pit. Controlling for a variety of other variables, including lagged price changes, trading volumes, and news announcements, we find that the sound level conveys information which is highly economically and statistically significant. Specifically, changes in the sound level forecast changes in the cost of transacting. Following a rise in the sound level, prices become more volatile, depth declines, and information asymmetry increases. Our results offer important implications for the future of open outcry and floor‐based trading mechanisms.

We investigate the bias in CRSP's Nasdaq data due to missing returns for delisted stocks. We find that the missing returns are large and negative on average, and that delisted stocks experience a substantial decrease in liquidity. We estimate that using a corrected return of −55 percent for missing performance‐related delisting returns corrects the bias. We revisit previous work which finds a size effect among Nasdaq stocks. After correcting for the delisting bias, there is no evidence that there ever was a size effect on Nasdaq. Our results are inconsistent with most risk‐based explanations of the size effect.