Association Meetings (Maastricht) and a referee and the editor, Stephen Brown, for helpful comments. We are grateful to Anchada Charoenrook and Jennifer Conrad for providing us with Amihud factor. We thank Morgan Stanley for research support. The authors are solely responsible for the contents of this paper. Factoring Information into Returns We examine the potential profits of trading on a measure of private information (pin) in a stock. A zero-investment portfolio which is size neutral, but long in high pin stocks and short in low pin stocks earns a significant abnormal return. The Fama-French, momentum and liquidity factors do not explain this return. However, significant covariation in returns exists among high pin stocks and among low pin stocks, suggesting that pin might proxy for an underlying factor. We create a pin factor as the monthly return on the zero-investment portfolio above and show that it is successful in explaining returns to independent pin-size portfolios. We also show that it is robust to inclusion of the Pastor-Stambaugh liquidity factor and the Amihud illiquidity factor. We argue that information remains an important determinant of asset returns even in the presence of these additional factors. Factoring Information into Returns I.

We extend the fractionally integrated exponential GARCH (FIEGARCH) model for daily stock return data with long memory in return volatility of Bollerslev and Mikkelsen (1996) by introducing a possible volatility-in-mean effect. To avoid that the long memory property of volatility carries over to returns, we consider a filtered FIEGARCH-in-mean (FIEGARCH-M) effect in the return equation. The …ltering of the volatility-in-mean component thus allows the co-existence of long memory in volatility and short memory in returns. We present an application to the S&P 500 index which documents the empirical relevance of our model.

We use a sample of option prices, and the method of Bakshi, Kapadia and Madan (2003), to estimate the ex ante higher moments of the underlying individual securities ’ risk-neutral returns distribution. We find that individual securities ’ volatility, skewness and kurtosis are strongly related to subsequent returns. Specifically, we find a negative relation between volatility and returns in the cross-section. We also find a significant relation between skewness and returns, with more negatively (positively) skewed returns associated with subsequent higher (lower) returns, while kurtosis is positively related to subsequent returns. To analyze the extent to which these returns relations represent compensation for risk, we use data on index options and the underlying index to estimate the stochastic discount factor over the 1996-2005 sample period, and allow the stochastic discount factor to include higher moments. We find evidence that, even after controlling for differences in co-moments, individual securities ’ skewness matters. However, when we combine information in the risk-neutral distribution and the stochastic discount factor to estimate the implied physical distribution of industry returns, we find little evidence that the distribution of technology stocks was positively skewed during the bubble period–in fact, these stocks have the lowest skew, and the highest estimated Sharpe ratio, of all stocks in our sample. All errors are the responsibility of the authors. We thank Robert Battalio, Patrick Dennis, and Stewart Mayhew for providing data and computational code. We thank Andrew Ang, Leonce Bargeron, and Paul Pfleiderer

A new semiparametric estimator for an empirical asset pricing model with general nonparametric risk-return tradeoff and a GARCH process for the underlying volatility is introduced. The estimator does not rely on any initial parametric estimator of the conditional mean function, and this feature facilitates the derivation of asymptotic theory under possible nonlinearity of unspecified form of the risk-return tradeoff. Besides the nonlinear GARCH-in-mean effect, our specification accommodates exogenous regressors that are typically used as conditioning variables entering linearly in the mean equation, such as the dividend yield. Using the profile likelihood approach, we show that our estimator under stated conditions is consistent, asymptotically normal, and efficient, i.e. it achieves the semiparametric lower bound. A sampling experiment provides evidence on finite sample properties as well as comparisons with the fully

We study the possibility that, aside from standard sources of utility, investors also derive utility from realizing gains and losses on assets that they own. We propose a tractable model of this “realization utility, ” derive its predictions, and show that it can shed light on a number of puzzling facts. These include the poor trading performance of individual investors, the disposition effect, the greater turnover in rising markets, the effect of historical highs on the propensity to sell, the negative premium to volatility in the cross-section, and the heavy trading of highly valued assets. Underlying some of these applications is one of our model’s more novel predictions: that, even if the form of realization utility is linear or concave, investors can be risk-seeking.

We propose a broad measure of liquidity for the overall financial market by exploiting its connection with the amount of arbitrage capital in the market and the potential impact on price deviations in US Treasurys. When arbitrage capital is abundant, we expect the arbitrage forces to smooth out the Treasury yield curve and keep the dispersion low. During market crises, the shortage of arbitrage capital leaves the yields to move more freely relative to the curve, resulting in more “noise. ” As such, noise in the Treasury market can be informative and we expect this information about liquidity to reflect the broad market conditions because of the central importance of the Treasury market and its low intrinsic noise — high liquidity and low credit risk. Indeed, we find that our “noise ” measure captures episodes of liquidity crises of different origins and magnitudes and is also related to other known liquidity proxies. Moreover, using it as a priced risk factor helps explain cross-sectional returns on hedge funds and currency carry trades, both known to be sensitive to the general liquidity conditions of the market.

* I am deeply indebted to my dissertation committee members Yongheng Deng

I examine the impact of status-seeking considerations on investors ’ portfolio choices and asset prices in a general equilibrium setting. The economy I study consists of traditional (“Markowitz”) investors as well as status-seekers who are concerned about relative wealth. The model highlights the strategic and interdependent nature of portfolio selection in such a setting: low-status investors look for portfolio choices that maximize their chances of moving up the ladder while high-status investors look to maintain the status quo and hedge against these choices of the low-status investors. In equilibrium, asset returns obey a novel two-factor model in which one factor is the traditional market factor and the other is a particular “high volatility factor ” that does not appear to have been identified so far in the theoretical or empirical literature. I test this two-factor model using stock market data and find significant support for it. Of particular interest, the model and the empirical results attribute the low returns on idiosyncratic volatility stocks documented by Ang, Hodrick, Xing and Zhang (2006) to their covariance with the portfolio of highly volatile stocks held by investors with relatively low status.

helpful comments. Any errors are my own. Do Investors Understand Loss Persistence?

Individual investors often invest actively and lose thereby. Social interaction seems to exacerbate the bias toward active trading. In the model here, conversational biases in the social transmission of performance information favor active over passive investment strategies. Senders ’ propensity to communicate their returns is increasing in returns. Receivers’ propensity to attend to and be converted by senders is increasing and convex in sender return. Active strategies (high variance, skewness, and personal involvement) dominate the population unless the mean return penalty to active investing is too large. Thus, the model can explain overvaluation of assets with these characteristics even if investors have no inherent preference over them.