The Quarterly Journal of Finance

This study develops a structural model of the initial public offering (IPO) pricing process that enables the estimation of adjustment rates for public and private pricing information gathered during bookbuilding. The estimated upward adjustment rate of public information is only 21%, significantly less than the 28% rate of private information. Adjustment rates decline towards the IPO date, especially for upward adjustments. The findings contradict information acquisition theories that predict a complete adjustment to public information and highlight the inefficiency of the IPO bookbuilding mechanism in handling new information even when information is publicly available and especially when it is favorable.

We propose and estimate a new class of equity return models that incorporate scale mixtures of the skew-normal distribution for the error distribution into the standard stochastic volatility framework. The main advantage of our models is that they can simultaneously accommodate the skewness, heavy-tailedness, and leverage effect of equity index returns observed in the data. The proposed models are flexible and parsimonious, and include many asymmetrically heavy-tailed error distributions — such as skew-t and skew-slash distributions — as special cases. We estimate a variety of specifications of our models using the Bayesian Markov Chain Monte Carlo method, with data on daily returns of the S&P 500 index over 1987–2009. We find that the proposed models outperform existing ones of index returns.

This paper studies the "overpriced puts puzzle" — the finding that historical prices of the S&P 500 put options have been too high and incompatible with the canonical asset-pricing models. To investigate whether put returns could be rationalized by another, possibly non-standard equilibrium model, we implement the model-free methodology of Bondarenko [2003a, Statistical Arbitrage and Securities Prices, Review of Financial Studies 16, 875–919]. The methodology requires no parametric assumptions on investors' preferences. Furthermore, it can be applied even when the sample is affected by certain selection biases (such as the Peso problem) and when investors' beliefs are incorrect. The main finding of the paper is that no model within a studied class of models can possibly explain the put anomaly.

In some countries, it is common that initial public offerings (IPOs) are preceded by forward ("when-issued") trading of the shares; in the US, such trading is prohibited. We analyze the effect of when-issued trading on the pricing and allocation of IPO shares. We demonstrate that the optimal selling mechanism in the presence of when-issued trading differs qualitatively from the optimal mechanism if such trading is prohibited. Furthermore we show that trading rules in the when-issued market can be designed so that allowing when-issued trading results in an increase in expected issue proceeds.

This paper develops and applies a new approach for disentangling the influence of analysts on each other's earnings forecasts from the effects of correlated information shocks. We estimate that, on average, each cent a new forecast by an analyst is above (below) another analyst's most recent forecast causes the other analyst to revise her forecast upwards (downwards) by between 0.21 and 0.36 cents. More reputable analysts are more influential, while those that tend to be optimistic are less influential and are influenced more by the forecasts of other analysts. We do not find support for career concerns-driven herding or anti-herding. Finally, we find that more influential analysts are more likely to subsequently be ranked as All-Stars and to move from a less to a more prestigious brokerage house, and less likely to leave the analyst profession, suggesting that influence is a desirable characteristic.