Market Microstructure - How Trading Actually Works

By EC Assets Research Team, Execution Strategy · Published 19 May 2026 · Updated 16 October 2026

Market Microstructure: Market microstructure is the study of how trades are matched, prices are set, and information flows through markets. For institutional investors, it determines execution costs that often exceed explicit trading commissions.

What Market Microstructure Is

Market microstructure studies how trading rules, venues and participants turn orders into prices. It asks why bid-ask spreads exist and how wide they are, how information gets into prices, how orders are routed and matched across exchanges and other venues, and what all of this costs the people who trade. O'Hara (1995) set out the theory; Harris (2003) describes the institutions from a practitioner's point of view.

For investors, microstructure is the source of most trading costs other than commissions: the spread paid to cross from bid to ask, the price impact of the order, and the information it leaks to other traders.

How It Works

Liquidity providers quote prices at which they will buy and sell, and the spread between them pays for three things: the cost of processing orders, the risk of holding inventory, and the risk of trading against someone who knows more. The third is usually the most important. Glosten and Milgrom (1985) showed that a spread arises even when the other two costs are zero. If some buyers know the asset is worth more, a dealer who sells to a buyer learns something bad about the trade, and sets the ask high enough to break even across informed and uninformed buyers.

Take an asset worth either 98 or 102 with equal probability. Uninformed traders buy or sell with equal chance; informed traders buy if the value is 102 and sell if it is 98:

Single-trade Glosten-Milgrom model; the spread equals the informed share times the range of values.

Kyle (1985) showed the same logic for large orders: when an informed trader splits an order over time, prices move in proportion to order flow, so every trade, informed or not, has price impact. This is why large orders are worked carefully and why venues that hide orders before execution, such as dark pools, exist.

Market structure adds another layer. In the US, the same stock trades on many exchanges and alternative trading systems, linked by rules that require orders not to trade through better displayed prices elsewhere, introduced with Regulation NMS in 2005. Fragmentation lets venues compete on fees and design, but rewards the fastest participants. Brogaard, Hendershott and Riordan (2014) find that high-frequency traders help prices absorb information. Budish, Cramton and Shim (2015) argue that continuous trading makes speed races inevitable, at a cost to liquidity, and propose frequent batch auctions instead.

Worked Example

A stock is quoted at 49.99 bid and 50.01 ask, a midpoint of 50.00. Three buy orders arrive. A minute later the midpoint is checked again:

Spreads measured as twice the distance from the midpoint, in basis points of 50.00; effective spread = realized spread + price impact.

The liquidity provider keeps the realized spread on the first two trades and loses 8.0 basis points on the third, because the price moved by more than the spread it earned. Across many trades, the spread must cover such losses. That explains two features of US equity markets. Small retail orders, which are unlikely to be informed, are often routed to wholesalers who fill them at better than the quoted price. Large institutional orders try to look uninformed, by splitting, timing and routing, to avoid paying for the information they appear to carry.

When It Applies (and Limitations)

Models are stylised. The Glosten-Milgrom table assumes two values and one trade; real spreads also reflect tick sizes, fees, competition between liquidity providers and inventory.

Measurement horizon matters. Realized spreads and price impact depend on how long after the trade the midpoint is measured, and short horizons can miss slower information.

Market structures differ. Futures trade on a single exchange per contract, many bonds and currencies through dealers, and ETFs have both exchange liquidity and the ability to create and redeem units, so the US equity picture does not carry over directly.

Rules change. Tick sizes, fee caps, order-routing and disclosure rules are revised regularly, so venue-specific detail dates quickly.

Why It Matters for Institutional Investors

Cost control. Effective spread, realized spread and price impact show whether an order paid for liquidity, for information leakage or for both, and feed into best-execution reviews.

Order handling. Splitting orders, choosing venues and timing, and using limit or hidden orders reduce the information an order reveals; futures can provide initial exposure cheaply while cash positions are built.

Manager due diligence. Strategies that trade often or in less liquid markets, including many hedge funds, depend on microstructure; asking how they measure costs shows whether paper returns are achievable.

Stress liquidity. Spreads widen and depth thins when volatility rises, so liquidity that looks ample in normal markets can vanish when it is most needed.

References

1. Glosten, L. R., & Milgrom, P. R. (1985). Bid, Ask and Transaction Prices in a Specialist Market with Heterogeneously Informed Traders. Journal of Financial Economics, 14(1), 71-100.
2. Kyle, A. S. (1985). Continuous Auctions and Insider Trading. Econometrica, 53(6), 1315-1335.
3. Brogaard, J., Hendershott, T., & Riordan, R. (2014). High-Frequency Trading and Price Discovery. Review of Financial Studies, 27(8), 2267-2306.
4. Budish, E., Cramton, P., & Shim, J. (2015). The High-Frequency Trading Arms Race: Frequent Batch Auctions as a Market Design Response. Quarterly Journal of Economics, 130(4), 1547-1621.
5. O'Hara, M. (1995). Market Microstructure Theory. Blackwell.
6. Harris, L. (2003). Trading and Exchanges: Market Microstructure for Practitioners. Oxford University Press.