Valuation risk of a security—uncertainty about its fair value—is a subject of considerable concern in the mutual fund industry. If funds report different values for identical securities, investors cannot easily compare their performance. Yet it is not unusual to see identical illiquid stocks, small-cap stocks, stocks with high analyst dispersion, stocks with less analyst coverage, and newly listed stocks valued differently across mutual funds. An equity fund that has positive price dispersion in its portfolio holdings, that performs poorly, that belongs to a fund family with an inclination for aggressive reporting, that holds more stocks subject to stale prices, that holds more pre-IPO firms, or that experiences net outflows will tend to show positive price dispersion again in the next quarter. This behavior is significant in a volatile market. Aggressive reporting helps funds gain in the mutual fund tournament.

A simple general equilibrium production economy matches moments of the value premium and equity premium. Value firms have low productivity, but will eventually produce high cash flows. The present value of these temporally distant cash flows is especially sensitive to equity premium movements. The value premium is the reward for bearing this sensitivity. Capital adjustment costs are important. Without these costs, value firms would disinvest heavily, leading to high cash flows today, low cash-flow growth going forward, and little exposure to discount rate shocks. Empirical evidence verifies that value firms have higher cash-flow growth and supports other predictions.

We develop an estimator for publication bias-adjusted returns and apply it to 156 published long-short portfolios. Our adjustment uses only in-sample data and provides sharper inferences than out-of-sample tests. Bias-adjusted returns are only 12.3% smaller than in-sample returns with a standard error of 1.7 percentage points. The small bias comes from the dispersion of returns across predictors, which is too large to be explained by data-mined noise. The bias is much smaller than post-publication decay (p-value ¡.0001), suggesting mispricing is important. Our results offer a different perspective about recent papers that find most published predictors are likely false.

We examine a simple measure of operating leverage: the ratio of fixed costs (measured by depreciation and amortization plus selling, general, and administrative expenses) to the market (or book) value of assets. We find that this measure of operating leverage positively predicts returns. This operating leverage measure is not explained by common factors and performs better than the traditional measures of operating leverage. Furthermore, an exploratory two-factor model with the operating leverage factor works at least as well as, but does not subsume, the Fama and French five-factor model.

We construct a traded funding liquidity measure from stock returns. Guided by a model, we extract the measure as the return spread between two beta-neutral portfolios constructed using stocks with high and low margins, to control for their sensitivity to the aggregate funding shocks. Our measure of funding liquidity is correlated with other funding liquidity proxies. It delivers a positive risk premium that cannot be explained by existing risk factors. A model augmented by our funding liquidity measure has superior pricing performance for various portfolios. Despite evident comovement, this measure contains additional information that is not subsumed by market liquidity.

This paper documents a new channel for rating-based bond market segmentation, which, in contrast to prior research, is based on nonregulatory investment management practices. A 2005 Lehman Brothers index redefinition provides a quasinatural experiment in which a number of previously high-yield split-rated bonds were mechanically relabeled as investment grade. Although their regulatory standing was unaffected, these bonds had abnormal yield declines of 21 basis points. These valuation changes can be traced to buying by asset-class-sensitive institutional investors for whom these bonds became investable. Reputation, regulation, indexation, and liquidity cannot explain the observed price and trading patterns.

Using a dynamic selection model, we obtain consistent and unbiased estimates of risk and returns for infrequently traded bonds and conduct the first comprehensive asset pricing test of municipal bonds using the multifactor approach. Correction for sample selection and infrequent trading problems results in substantially higher beta estimates. Besides conventional risk factors, illiquidity and taxes are important for the pricing of municipal bonds. Importantly, bond returns contain a significant liquidity risk premium. Failing to account for sample selection bias leads to erroneous inference on the magnitude of systematic risk and substantial underestimation of risk premiums.

This paper proposes a novel approach to estimating the probability weighting function (PWF) of investors in the option market. We match observed option prices to the option pricing bounds under stochastic dominance rules. Using 1-month S&P 500 index option data, we find that investors could subjectively employ an inverse S-shaped probability weighting function, which increases the weights on extreme returns and asymmetrically assigns greater weights to extremely low returns than to extremely high returns. Our findings suggest that the inverse S-shaped nature of the PWFs is robust across various estimation specifications, such as adopting an alternative methodology to construct the return distribution, and employing option data with different times to maturity.

We propose a naïve model averaging (NMA) method that averages the OLS out-of-sample forecasts and the historical means and produces mostly positive out-of-sample R2s for the variables significant in sample in forecasting market returns. Surprisingly, more sophisticated weighting schemes that combine the predictive variable and historical mean do not consistently perform better. With unstable economic relations and a limited sample size, sophisticated methods may lead to overfitting or be subject to more estimation errors. In such situations, our simple methods may work better. Model misspecification, rather than declining return predictability, likely explains the predictive performance of the NMA method.

We evaluate the performance of various methods for estimating factor returns in an approximate factor model. Differences across estimators are most pronounced when there is cross-sectional heteroscedasticity or when cross-sectional sample sizes, n, have fewer than 4,000 assets. Estimators incorporating either cross-sectional or time-series heteroscedasticity outperform the other estimators when those types of heteroscedasticity are present. The differences are most pronounced when the cross-sectional sample is small. Received December 2, 2015; editorial decision May 16, 2017 by Editor Jeffrey Pontiff.