Stern School of Business
New York University
44 West 4th Street
New York, NY 10012
NBER Program Affiliations:
NBER Affiliation: Faculty Research Fellow
Institutional Affiliation: New York University
NBER Working Papers and Publications
|March 2019||Trading Costs and Informational Efficiency|
with : w25662
We study the effect of trading costs on information aggregation and acquisition in financial markets. For a given precision of investors' private information, an irrelevance result emerges when investors are ex-ante identical: price informativeness is independent of the level of trading costs. When investors are ex-ante heterogeneous, anything goes, and a change in trading costs can increase or decrease price informativeness, depending on the source of heterogeneity. Our results are valid under quadratic, linear, and fixed costs. Through a reduction in information acquisition, trading costs reduce price informativeness. We discuss how our results inform the policy debate on financial transaction taxes/Tobin taxes.
|January 2019||Volatility and Informativeness|
with : w25433
We explore the equilibrium relation between price volatility and price informativeness in financial markets, with the ultimate goal of characterizing the type of inferences that can be drawn about price informativeness by observing price volatility. We identify two different channels (noise reduction and equilibrium learning) through which changes in price informativeness are associated with changes in price volatility. We show that when informativeness is sufficiently high (low) volatility and informativeness positively (negatively) comove in equilibrium for any change in primitives. In the context of our leading application, we provide conditions on primitives that guarantee that volatility and informativeness always comove positively or negatively. We use data on U.S. stocks between 196...
|November 2018||Identifying Price Informativeness|
with : w25210
We show that outcomes (parameter estimates and R-squareds) of regressions of prices on fundamentals allow us to recover exact measures of the ability of asset prices to aggregate dispersed information. Formally, we show how to recover absolute and relative price informativeness in dynamic environments with rich heterogeneity across investors (regarding signals, private trading needs, or preferences), minimal distributional assumptions, multiple risky assets, and allowing for stationary and non-stationary asset payoffs. We implement our methodology empirically, finding stock-specific measures of price informativeness for U.S. stocks. We find a right-skewed distribution of price informativeness, measured in the form of the Kalman gain used by an external observer that conditions its posterio...