MARTIN SMID, Institute of Information Theory and Automation, Prague
Email: smid@utia.cas.cz

We formulate a general model of the continuous double auction, covering the models of Maslov [2000], Luckock [2003], Smith at al. [2003] and (partially) the the model of Mike et. al. [2008]. We analytically describe the distribution of the best quotes (bid and ask) and the (conditional) distribution of the order books.

Based on these results, we propose a simple test of the validity of our model, a technique of a statistical inference of the order books and a procedure allowing a Monte Carlo simulation of the order books, much more efficient then a crude simulation.

THOMAS ROURKE, Duquesne University - Department of Finance
Email: rourket@duq.edu

This paper examines the role of limit orders in the price discovery process in related security markets and also the level of integration between stock and option markets. I estimate both the initial price impacts and the permanent price revisions in response to market and limit orders in and across stock and option markets. I show that limit order submissions, in any market, forecast significant permanent price revisions within that market and across other markets for a majority of firms in the sample. However, only market orders in the stock market forecast significant permanent price revisions across markets. Additionally, the paper documents that net buying pressure is serially correlated in each market but not serially correlated across markets for the many of the firms in the sample. The primary implication of these results is that the common theoretical assumption that informed traders only submit market orders to exploit private information is not valid. As a result, empirical estimations of asymmetric information in financial markets that omit limit orders are likely to understate the true level of adverse selection. The secondary implication is that stock and option markets are best viewed as segmented markets, with each market having its own unique clientele of traders.

DIEGO GARCIA, UNC at Chapel Hill
Email: diego_garcia@unc.edu
FRANCESCO SANGIORGI, Collegio Carlo Alberto
Email: francesco.sangiorgi@carloalberto.org

We study information sales in financial markets with strategic risk-averse traders. Our main result establishes that the optimal selling mechanism is one of the following two: (i) sell to as many agents as possible very imprecise information; (ii) sell to a small number of agents a signal as precise as possible. As noise trading per unit of risk-tolerance becomes large, the newsletters or rumors associated with (i) dominate the exclusivity contract in (ii). The optimal information sales contracts share similar properties in market-orders and limit-orders markets, while models in which competitive behavior is assumed yield qualitatively different equilibria. The endogeneity of the information allocation implies a ranking reversal of the informational content of prices: (a) equilibrium prices become more informative in market-orders than in limit-orders markets, and (b) the model with imperfect competition yields more informative prices than its competitive counterpart. These results are driven by the financial intermediary selling more information when the externality in its valuation is relatively less intense.

JACOB PAROUSH, Bar Ilan University - Department of Economics
Email: parousj@mail.biu.ac.il
ROBERT A. SCHWARTZ, Baruch College - CUNY
Email: ROBERT_SCHWARTZ@BARUCH.CUNY.EDU
AVNER WOLF, Baruch College
Email: avner_wolf@baruch.cuny.edu

We demonstrate that the process of discovering efficient values in equity trading introduces noise in prices. Noise plays an important role in theoretical microstructure literature, and empirical studies have documented high, U-shaped intra-day volatility that is a manifestation of noise. While implicit transaction costs and the tactical trading of informed participants in an asymmetric information environment are contributing factors, they do not provide a sufficient explanation. Accordingly, we focus on an additional factor - price discovery. Our formulation, which allows investors with divergent expectations to respond rationally to each others‘ valuations, implies elevated volatility even when information is common knowledge.