In general, market microstructure theory might be defined as “the study of the process and outcomes of exchanging assets under explicit rules” (Easley and O’Hara, 1995; p. 357) or “the area of finance that is concerned with the process by which investors’ latent demands are ultimately translated into transactions” (Madhavan, 2000; p. 205-206). Therefore, the market microstructure literature is concerned with the analysis of how specific features of the trading process like the existence of intermediaries (e.g. stock specialist or brokers), or the environment in which trading takes place (e.g. trading at a centralized exchange involving physical presence of traders versus trading on some decentralized electronic trading system) affect the price formation process. In a certain sense, the Null hypothesis of market microstructure theory is that many details of existing trading mechanisms and institutions, which are usually neglected in economic models, may exert a strong impact on the outcomes of the price formation process, namely prices and quantities.
Alternatively, one might argue, that market microstructure theory is just a branch of microeconomics that tries to enlighten the black box of price setting behavior out of equilibrium. Standard microeconomic analysis is based on the assumption, that prices are determined by the intersection of demand and supply curves for a particular good. Analysis the focuses on how the equilibrium outcomes change, as one or both of the curves shift due to exogenous factors but the question of how exactly equilibra are being attained has long been treated as a question of secondary importance. It was only recently, that economists started to analyze more closely the determinants of behavior out of equilibrium.
Many market microstructure models can therefore be interpreted as attempts to examine more closely the relationship between idiosyncratic features of the trading mechanism and the outcomes of trading in terms of prices and quantities. Madhavan (2000) distinguishes in his recent review of the literature between four main categories of research in market microstructure theory:
- Price formation and price discovery: This refers to research on the black box by which latent demands are translated into realized prices and volumes, and includes issues like the determinants of trading costs or the process of gradual incorporation of information into prices.
- Market structure and design issues: This category is concerned with the question, how different trading rules or protocols affect the black box and hence the liquidity and the quality of an asset market.
- Information and disclosure: This category focuses on how the ability of market participants to observe information about the trading process affects the black box (market transparency).
- Information issues arising from the interface of market microstructure with other areas of finance: There are many overlaps between the analysis of the black box to other areas of finance and economics, e.g. corporate finance, asset pricing, international finance, or macroeconomics.
The general market microstructure modeling approaches are used to analyze the effect of information on security prices. In both of the most commonly used approaches, i.e. the sequential trade framework and the batch strategic trading models, new information becomes impounded into prices as a result of the trading behavior of informed and uninformed traders. A characteristic of both approaches is that this price adjustment is not instantaneous. Because prices are conditional expected values, the price at each point reflects all publically available information, but not necessarily all private information. Consequently, until prices adjust to the new-information value, informed traders earn a return to their information and prices are only semi-strong-form efficient.
Here the empirical question is how prices adjust to new information over time. In both microstructure paradigms, prices eventually converge to new information values, but, since this adjustment takes place in the limit, the actual adjustment time can be infinite. To understand how prices become “efficient”, we need to know more about the process by which this adjustment occurs. Moreover, since different market structures can affect this adjustment, understanding how the price process behaves may provide insight into how markets should be structured and regulated.
Examining the process of price adjustment requires focusing on how prices change across time. Since the specialist is responsible for setting market-clearing prices, this requires understanding how the specialist and other uninformed participants learn from observing market information. In microstructure models, as well as in actual security markets, what is actually observable can differ in fundamental ways. Individual traders, for example, are not publicly observable in batch systems, but are observable in continuous auctions. Similarly, the sequence of trades and their timing may be observable in some trading systems but not in others. This suggests that characterizing the price adjustment process requires a careful analysis of how information generated by the trading process is related to information on the underlying asset value.
Initially, we consider the simplest version of this problem by analyzing the information revealed by the price sequence. If prices are not fully revealing, then the sequence of prices may provide information that individual prices do not. Consequently, the adjustment of prices to information may involve drawing inferences from the price process itself to determine what the new full information value should be. This inference process has been largely studied in rational expectations batch-style models, and we first consider the adjustment issue in this framework. Subsequently, the effects of volume and time on the price adjustment process may be considered.
Investigations of price adjustment in rational expectations models are multitudinous, and it is beyond the scope of the present study. What is of interest for us is how in a market setting traders can infer information from market parameters. The discussion can be extended to considering how the market structure itself affects the available information.
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