An Examination of Changes in Specialists' Posted Price Schedules

New York Stock Exchange specialists disseminate informationto market participants by displaying price schedules consistingof bid prices, ask prices, bid depths, and ask depths. We examinehow specialists update these price schedules in a simultaneousequations model. We find that changes in the best prices anddepths on the limit order book have a significant impact onthe posted price schedule, while the effects of transactionsand order activity are secondary. Furthermore, we show thatspecialists revise prices and depths differently, but find noevidence that they revise the price schedule in response tochanges in inventory.     

A Specialist's Quoted Depth and the Limit Order Book

By partitioning quoted depth into the specialist's contribution and the limit order book's contribution, the paper investigates whether specialists manage quoted depth to reduce adverse selection risk. The results show that both specialists and limit order traders reduce depth around information events, thereby reducing their exposure to adverse selection costs. Moreover, specialists' quotes may reflect only the limit order book on the side (or sides) of the market where they believe there is a chance of informed trading. Changes in quoted depth are consistent with specialists managing their inventory as well as having knowledge of the stock's future value.     

Price discovery in the treasury futures market

The paper conducts a regression analysis utilizing both futures and cash market prices and net orderflow to determine where price discovery takes place as well as the forces at play that influence the location. Specifically, given the strong theoretical linkage between the U.S. Treasury cash and futures markets, they compare how orderflow contributes to price discovery and analyze how and when information flows from one market to the other. How a number of environmental variables (trader type, financing rates, and liquidity) impact the information flows between these two markets is also considered. Their findings provide new evidence on the extent to which price discovery happens away from a primary market. © 2007 Wiley Periodicals, Inc. Jrl Fut Mark 27: 1021–1051, 2007     

A SPECIALIST'S QUOTED DEPTH AS A STRATEGIC CHOICE VARIABLE: AN APPLICATION TO SPREAD DECOMPOSITION MODELS

Although there is a sizable literature demonstrating that liquidity and transaction costs are multidimensional, researchers continue to estimate adverse‐selection costs using only prices. We present a model of a profit‐maximizing specialist who posts prices and depths. The model is simulated to measure changes in the adverse‐selection component of the spread that result under different levels of informed trading. We find that spread decompositions fail to capture the full extent of adverse‐selection risk when specialists choose depth. We recommend that researchers use adverse‐selection measures that account for depth as well as spread to mitigate this problem.     

Technical Analysis and Liquidity Provision

The apparent conflict between the level of resources dedicatedto technical analysis by practitioners and academic theoriesof market efficiency is a long-standing puzzle. We explore apreviously unexamined feature of technical analysis —namely its relation to liquidity provision. We demonstrate thatsupport and resistance levels coincide with peaks in depth onthe limit order book and moving average forecasts reveal informationabout the relative position of depth on the book. Furthermore,we show that these relationships stem from technical rules locatingdepth already in place on the limit order book.     

Volatility and market structure

This study examines volatility within three related intra-day series – transaction returns, quote midpoint returns, and limit order book midpoint returns – for a set of NYSE-listed stocks. We document statistically significant GARCH effects both overall and surrounding earnings announcements in all three series for the majority of stocks in the sample. We then compare the extent of volatility clustering among the series. In addition, the relation between volatility and market structure is examined via a set of cross-sectional regressions, and relations among the series over time are studied in a vector autoregressive framework.