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.

ABSTRACT 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.

ABSTRACT 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.

We document persistent superior trading performance among a subset of individual investors. Investors classified in the top performance decile in the first half of our sample subsequently earn risk-adjusted returns of about 6% per year. These returns are not confined to stocks in which the investors are likely to have inside information, nor are they driven by illiquid stocks. Our results suggest that skilled individual investors exploit market inefficiencies (or perhaps conditional risk premiums) to earn abnormal profits, above and beyond any profits available from well-known strategies based on size, value, momentum, or earnings announcements. (JEL G11, G14, G40, G51) Received: October 11, 2020 Editorial decision: January 4, 2021 Editor: Jeffrey Pontiff

We document significant persistence in the market timing performance of active individual investors, suggesting that some investors are skilled at timing. Using data on all trades by active Finnish individual investors over almost 15 years, we also show that the net purchases of skilled versus unskilled investors predict monthly market returns. Our results lend credibility to the view that market returns are predictable, without having to specify which variables active investors use to successfully time the market. (JEL G10, G11, G12, G14, G15).

Psychological evidence and casual intuition predict that sunny weather is associated with upbeat mood. This paper examines the relationship between morning sunshine in the city of a country's leading stock exchange and daily market index returns across 26 countries from 1982 to 1997. Sunshine is strongly significantly correlated with stock returns. After controlling for sunshine, rain and snow are unrelated to returns. Substantial use of weather‐based strategies was optimal for a trader with very low transactions costs. However, because these strategies involve frequent trades, fairly modest costs eliminate the gains. These findings are difficult to reconcile with fully rational price setting.

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. Received August 2, 2015; editorial decision April 15, 2017 by Editor Andrew Karolyi.

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.

Existing evidence shows that risk aversion and trust are largely determined by environmental factors. We test whether one such factor is peer influence. Using random assignment of MBA students to peer groups and predetermined survey responses of economic attitudes, we find causal evidence of positive peer effects in risk aversion and no effects in trust. After the first year of the MBA program, the difference between an individual and her peers' average risk aversion has shrunk by 41%. Finding no peer effects in trust is consistent with recent research showing that distinct cognitive processes govern risk aversion and trust.

This paper develops an overlapping generations model of optimal rebalancing where agents differ in age and risk tolerance. Equilibrium rebalancing is driven by a leverage effect that influences levered and unlevered agents in opposite directions, an aggregate risk tolerance effect that depends on the distribution of wealth, and an intertemporal hedging effect. After a negative macroeconomic shock, relatively risk-tolerant investors sell risky assets, while more risk-averse investors buy them. Owing to interactions of leverage and changing wealth, however, all agents have higher exposure to aggregate risk after a negative macroeconomic shock and lower exposure after a positive shock.