Knowledge that Transforms

This article analyzes the effects of information leakage on trading behavior and market efficiency. A trader who receives a noisy signal about a forthcoming public announcement can exploit it twice. First, when he receives it, and second, after the public announcement since he knows best the extent to which his information is already reflected in the pre-announcement price. Given his information he expects the price to overshoot and intends to partially revert his trade. While information leakage makes the price process more informative in the short-run, it reduces its informative-ness in the long-run. The analysis supports Securities and Exchange Commission’s Regulation Fair Disclosure. In a perfect world, all investors would receive information pertinent to the value of the stock immediately and simultaneously. In reality, however, some agents like corporate insiders and their favored analysts can receive signals about this information before it is disclosed to the general public. The focus of our analysis is to determine (i) the optimal trading strategy of an early-informed agent and (ii) the implications of this trading behavior

We study how heterogeneous beliefs affect returns and examine whether they are a priced factor in traditional asset pricing models. To accomplish this task, we suggest new empirical measures based on the disagreement among analysts about expected earnings (short-term and long-term) and show they are good proxies. We first establish that the heterogeneity of beliefs matters for asset pricing and then turn our attention to estimating a structural model in which we use the forecasts of financial analysts to proxy for agents’ beliefs. Finally, we investigate whether the amount of heterogeneity in analysts’ forecasts can help explain asset pricing puzzles.

Responding to a September 2002 regulatory enforcement, the Island electronic communications network stopped displaying its limit order book in the three most active exchange-traded funds (ETFs) where it was the dominant venue. Island’s share of trading activity and price discovery fell, fragmenting the market. ETF prices adjust more slowly when Island goes dark, and there is substantial price discovery movement from ETFs to the futures market. Trading costs increase on Island and decrease off Island, with higher trading costs overall. When Island later redisplays its orders, market quality improves, with transparency and the reduction in fragmentation both playing important roles.

Britten-Jones and Neuberger (2000) derived a model-free implied volatility under the diffusion assumption. In this article, we extend their model-free implied volatility to asset price processes with jumps and develop a simple method for implementing it using observed option prices. In addition, we perform a direct test of the informational efficiency of the option market using the model-free implied volatility. Our results from the Standard & Poor’s 500 index (SPX) options suggest that the model-free implied volatility subsumes all information contained in the Black–Scholes (B–S) implied volatility and past realized volatility and is a more efficient forecast for future realized volatility.

We develop a nonparametric specification test for continuous-time models using the transition density. Using a data transform and correcting for the boundary bias of kernel estimators, our test is robust to serial dependence in data and provides excellent finite sample performance. Besides univariate diffusion models, our test is applicable to a wide variety of continuous-time and discrete-time dynamic models, including time-inhomogeneous diffusion, GARCH, stochastic volatility, regime-switching, jump-diffusion, and multivariate diffusion models. A class of separate inference procedures is also proposed to help gauge possible sources of model misspecification. We strongly reject a variety of univariate diffusion models for daily Eurodollar spot rates and some popular multivariate affine term structure models for monthly U.S. Treasury yields.

We present a simulation-based method for solving discrete-time portfolio choice problems involving non-standard preferences, a large number of assets with arbitrary return distribution, and, most importantly, a large number of state variables with potentially path-dependent or non-stationary dynamics. The method is flexible enough to accommodate intermediate consumption, portfolio constraints, parameter and model uncertainty, and learning. We first establish the properties of the method for the portfolio choice between a stock index and cash when the stock returns are either iid or predictable by the dividend yield. We then explore the problem of an investor who takes into account the predictability of returns but is uncertain about the parameters of the data generating process. The investor chooses the portfolio anticipating that future data realizations will contain useful information to learn about the true parameter values.

We demonstrate that limited participation can arise endogenously in the presence of model uncertainty and heterogeneous uncertainty-averse investors. When uncertainty dispersion among investors is small, full participation prevails in equilibrium. Equity premium is related to the average uncertainty among investors and a conglomerate trades at a price equal to the sum of its single-segment components. When uncertainty dispersion is large, investors with high uncertainty choose not to participate in the stock market, resulting in limited market participation. When limited participation occurs, participation rate and equity premium can decrease in uncertainty dispersion and a conglomerate trades at a discount.

This article studies the asset pricing implication of imprecise knowledge about rare events. Modeling rare events as jumps in the aggregate endowment, we explicitly solve the equilibrium asset prices in a pure-exchange economy with a representative agent who is averse not only to risk but also to model uncertainty with respect to rare events. The equilibrium equity premium has three components: the diffusive- and jump-risk premiums, both driven by risk aversion; and the ‘‘rare-event premium,’’ driven exclusively by uncertainty aversion. To disentangle the rare-event premiums from the standard risk-based premiums, we examine the equilibrium prices of options across moneyness or, equivalently, across varying sensitivities to rare events. We find that uncertainty aversion toward rare events plays an important role in explaining the pricing differentials among options across moneyness, particularly the prevalent ‘‘smirk’ ’ patterns documented in the index options market. Sometimes, the strangest things happen and the least expected occurs. In financial markets, the mere possibility of extreme events, no matter how unlikely, could have a profound impact. One such example is the so-called

We investigate expectation formation in a controlled experimental environment. Subjects are asked to predict the price in a standard asset pricing model. They do not have knowledge of the underlying market equilibrium equations, but they know all past realized prices and their own predictions. Aggregate demand for the risky asset depends upon the forecasts of the participants. The realized price is then obtained from market equilibrium with feedback from six individual expectations. Realized prices differ significantly from fundamental values and typically exhibit oscillations around, or slow convergence to, this fundamental. In all groups participants coordinate on a common prediction strategy.

This paper examines the optimal consumption and portfolio-choice problem of long-horizon investors who have access to a riskless asset with constant return and a risky asset (“stocks”) with constant expected return and time-varying precision—the reciprocal of volatility. Markets are incomplete, and investors have recursive preferences defined over intermediate consumption. The paper obtains a solution to this problem which is exact for investors with unit elasticity of intertemporal substitution of consumption and approximate otherwise. The optimal portfolio demand for stocks includes an intertemporal hedging component that is negative when investors have coefficients of relative risk aversion larger than one, and the instantaneous correlation between volatility and stock returns is negative, as typically estimated from stock return data. Our estimates of the joint process for stock returns and precision (or volatility) using U.S. data confirm this finding. But we also find that stock return volatility does not appear to be variable and persistent enough to generate large intertemporal hedging demands.