Bid-Ask Spread: Theory and Empirical Evidence

The most important friction studied in the microstructure literature is the adverse selection borne by liquidity providers when facing traders who are better informed, and the bid-ask spread quoted by market makers is one of these frictions in securities markets that has been extensively studied. In the early 1980s, the transparency of U.S. stock markets was limited to post-trade end-of-day transactions prices, and there were no easily available market quotes for researchers and market participants to study the effects of bid-ask spread on the liquidity and quality of markets. This led to models that used the auto-covariance of daily transactions prices to estimate the bid-ask spread. In the early 1990s, the U.S. stock markets (NYSE/AMEX/NASDAQ) provided pre-trade quotes and transaction sizes for researchers and market participants. The increased transparency and access to quotes and trades led to the development of theoretical models and empirical methods to decompose the bid-ask spread into its components: adverse selection, inventory, and order processing. These models and methods can be broadly classified into those that use the serial covariance properties of quotes and transaction prices, and others that use a trade direction indicator and a regression approach to decompose the bid-ask spread. Covariance and trade indicator models are equivalent in structural form, but they differ in parameters’ estimation (reduced form). The basic microstructure model is composed of two equations; the first defines the law of motion of the “true” price, while the second defines the process generating transaction price. From these two equations, an appropriate relation for transaction price changes is derived in terms of observed variables. A crucial point that differentiates the two approaches is the assumption made for estimation purposes relative to the behavior of order arrival, which is the probability of order reversal or continuation. Thus, the specification of the most general models allows for including an additional parameter that accounts for order behavior. The article provides a unified framework to compare the different models with respect to the restrictions that are imposed, and how this affects the relative proportions of the different components of the spread.