Empirical Market Microstructure Models: A Review of Trading Behavior, Liquidity, and Price Formation

This paper reviews empirical market microstructure models and their role in explaining trading behavior, liquidity, price formation, and transaction costs in financial markets. Market microstructure research examines how financial securities are traded and how trading mechanisms, order flow, and information asymmetry influence market outcomes. Unlike traditional financial theories that assume perfect and frictionless markets, market microstructure focuses on the actual trading process, including how prices are determined, how liquidity is provided, and how information is reflected in market prices. The study mainly relied on a literature review approach using secondary sources from academic journals, books, reports, and reputable databases. The review examined classical empirical market microstructure frameworks, focusing on adverse selection models, inventory models, and hybrid models. Classical theories such as the Kyle model, Glosten–Milgrom model, Stoll model, and Ho–Stoll model were reviewed together with more recent hybrid and algorithmic trading frameworks such as the Madhavan–Richardson–Roomans model and the Avellaneda–Stoikov model. The findings show that empirical market microstructure models have evolved from traditional dealer-based frameworks to more advanced models using high-frequency trading data, electronic order books, and algorithmic trading systems. The review further shows that liquidity, bid-ask spreads, and price discovery are influenced by information asymmetry, inventory risk, order processing costs, and trading technology. The study concludes that hybrid empirical models provide a broader explanation of modern market behavior because they combine information effects and inventory management within a single framework. However, many traditional models remain limited by assumptions of rational behavior and perfect information processing. The study recommends further empirical research focusing on emerging markets and the integration of behavioral finance and machine learning approaches into market microstructure analysis.