Market Microstructure 101: What Every Quant Should Know

Market microstructure is the study of how markets actually work at the mechanical level: how orders are submitted, matched, and reported; how prices form; and how information is impounded into quotes. It is the theoretical foundation beneath every execution algorithm, every TCA framework, and every market-making strategy — yet many quantitative professionals have only a superficial understanding of its key concepts.

The first essential concept is order flow and its information content. Not all orders are created equal. Some orders are submitted by informed traders who possess private information about an asset's fundamental value; others are submitted by liquidity seekers who trade for portfolio rebalancing, hedging, or index tracking reasons. The proportion of informed flow — often estimated using models like the PIN (probability of informed trading) framework — determines how quickly and aggressively market makers adjust their quotes after a trade. High informed-flow environments have wider spreads and greater price impact; low informed-flow environments have tighter spreads and lower costs.

The second concept is adverse selection. When a market maker sells to a buyer, there is always a risk that the buyer knows something the market maker does not. If the buyer is informed, the market maker's position will immediately move against them. Market makers price this risk into the spread: the adverse selection component of the bid-ask spread is the compensation they demand for taking the wrong side of informed trades. Understanding adverse selection is critical for execution algorithms because it explains why aggressive orders (marketable limit orders and market orders) pay a premium: they are more likely to be mistaken for informed flow.

The third concept is price impact — the effect that a trade has on subsequent prices. Price impact is both temporary (caused by short-term supply-demand imbalances) and permanent (caused by information revelation). The Almgren-Chriss framework, which underlies many optimal execution algorithms, models the trade-off between executing quickly (which minimizes timing risk but maximizes impact) and executing slowly (which minimizes impact but maximizes timing risk). The optimal schedule depends on the stock's volatility, its liquidity, and the urgency of the trade — parameters that must be estimated empirically and updated continuously.