Joel Hasbrouck

Common Factors in Prices, Order Flows and Liquidity

How important are cross-stock common factors in the price discovery/liquidity provision process in equity markets? We investigate two aspects of this question for the thirty Dow stocks. First, using principal components and canonical correlation analyses we find that both returns and order flows are characterized by common factors. Commonality in the order flows explains roughly half of the commonality in returns. Second, we examine variation and common covariation in various liquidity proxies and market depth (trade impact) coefficients. Liquidity proxies such as the bid-ask spread and bid-ask quote sizes exhibit time variation which helps explain time variation in trade impacts. The common factors in these liquidity proxies are relatively small, however.

Trades, quotes, inventories, and information

Abstract This empirical examination of the relation between trades and quote revisions for New York Stock Exchange-listed stocks is designed to ascertain asymmetric-information and inventory-control effects. This study finds that negative autocorrelation in trades consistent with inventory-control behavior characterizes low-volume stocks, but not high-volume stocks. The evidence of inventory control in the impact of trades on quote revisions is inconclusive. The information content of trades, on the other hand, is found to be substantial. There is also strong evidence that large trades convey more information than small trades.

Market vs. Limit Orders: The SuperDOT Evidence on Order Submission Strategy

This paper discusses performance measures for market and limit orders. We suggest two measures: one for precommitted traders (who must trade) and another for passive traders (who are indifferent to trading). We compute these measures for a sample of NYSE SuperDOT orders. The results suggest that the limit order placement strategies most commonly used by NYSE SuperDOT traders do in fact perform best. Limit orders placed at or better than the prevailing quote perform better than do market orders, even after imputing a penalty for unexecuted orders, and after taking into account market order price improvement. Unconditional order submission strategies that use SuperDOT to offer liquidity in competition with the specialist do not appear to be profitable.

The characteristics of takeover targets and other measures

Abstract This study attempts to assess differences in the financial characteristics of target and non-target firms using logit analysis and a case-control methodology in which control groups are matched by size or industry. The results indicate that unregulated non-financial target firms are characterized by low q ratios (market/replacement values) and to a lesser extent high current financial liquidity. Measures of financial leverage were not found to be significant.

Stalking the "Efficient Price" in Market Microstructure Specifications: An Overview

Abstract In virtually all microstructure models, expectations of security payoffs are important determinants of prices and trading strategies. The principle that revisions to this expectation should be unpredictable implies a martingale property. This motivates empirical specifications in which the security price follows a random-walk plus transient disturbances that arise from the trading mechanism. This note is an overview of econometric approaches to characterizing the random-walk component in single- and multiple-price settings. Established techniques of random-walk decomposition provide useful characterizations of random-walk properties and (in the case of multiple markets) informational attributions. More general approaches based on the broader class of permanent/transitory decompositions suffer from problematic identification, a potential for misleading inference and lack of economic relevance.

Liquidity in the Futures Pits: Inferring Market Dynamics from Incomplete Data

Motivated by economic models of sequential trade, empirical analyses of market dynamics frequently estimate liquidity as the coefficient of signed order flow in a price change regression. This paper implements such an analysis for futures transaction data from pit trading. To deal with the absence of timely bid and ask quotes (which are used to sign trades in most equity market studies), this paper proposes new techniques based on Markov chain Monte Carlo estimation. The model is estimated for four representative Chicago Mercantile Exchange contracts. The highest liquidity (lowest order flow coefficient) is found for the S&P 500 index. Liquidity for the Euro and U.K. £ contracts is somewhat lower. The pork belly contract exhibits the least liquidity.

Modeling Market Microstructure Time Series

Microstructure data typically consist of trades and bid and offer quotes for financial securities that are collected at fine sampling intervals (often within the day). This paper reviews approaches taken to modeling these data. The emphasis is on the techniques of stationary multivariate time series analysis: autoregressive and moving average representations o f standard microstructure models, vector autoregressive estimation, random-walk decompositions and cointegration. The paper also discusses the challenges posed by irregular observation frequencies, discreteness and nonlinearity.

Forecasting Volatilities and Correlations with EGARCH Models

Volatility varies randomly over time, making forecasting it d@cult. Formal models for systems with timevarying volatility have been developed in recent years, and widely applied in economics and finance. Models in the Autoregressive Conditional Heteroscedasticity (ARCH) family have been particularly popular. Prior studies of ARCH-type models of securities return variances have looked at a single asset and focused on in-sample explanation of volatility movements, rather than forecasting. This article considers time variation for both volatilities and correlations among returns on broad asset classes in the US. and Japan, specijcally, equities, long-term government bonds, a n d the do l l a r lyen exchange rate. We are most concerned with out-

Security Bid/Ask Dynamics with Discreteness and Clustering: Simple Strategies for Modeling and Estimation

--sample forecasting performance. We fi t Exponen t i a l Genera l ized ARCH (EGARCH) models for the returns variances of weekly data from 1977 to 1990. In-sample parameter estimates are statistically signijcant and of the expected signs and magnitudes. In both regressions and directional tests of outof-sample forecasting ability, the EGARCH models seem to contain more information than historical volatility. But overall explanatory power is not great. Forecasting correlations is less successful. Only six of ten pairwise correlations show any significant ARCH efects. Although the model forecasts were less biased than the historical correlation, explanatory power in all cases is very low.

Order characteristics and stock price evolution An application to program trading

This paper proposes a dynamic model of bid and ask quotes that incorporates a stochastic cost of market-making, discreteness (restriction of quotes to a fixed grid) and clustering (the tendency ofquotes to lie on â¬Snaturalâ¬? multiples of the tick size). The Gibbs sampler provides a convenient vehicle for estimation. The model is estimated for daily and intradaily US Dollar/Deutschemark Reuters quotes.