Further Reading: Liquidity Provision and Market Making

Foundational Papers

1. Glosten, L. R. and 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.

The seminal model of adverse selection in market making. Derives the bid and ask prices as conditional expectations of asset value given the direction of order flow. Essential reading for understanding why spreads exist and how they respond to informed trading. Every subsequent model of market microstructure builds on this foundation.

2. Kyle, A. S. (1985). "Continuous Auctions and Insider Trading." Econometrica, 53(6), 1315–1335.

Introduces the Kyle model where a single informed trader strategically chooses order size to maximize profits against a market maker. The parameter $\lambda$ (Kyle's lambda) measures price impact per unit of order flow and has become a standard metric for adverse selection. More tractable than Glosten-Milgrom for many applications.

3. Avellaneda, M. and Stoikov, S. (2008). "High-Frequency Trading in a Limit Order Book." Quantitative Finance, 8(3), 217–224.

Provides the optimal quoting framework for a risk-averse market maker in a limit order book. The key result—shifting quotes based on inventory, risk aversion, volatility, and time horizon—has become the industry standard for inventory management. The paper's simplicity and practical applicability make it the go-to reference for building market-making systems.

4. Hanson, R. (2003). "Combinatorial Information Market Design." Information Systems Frontiers, 5(1), 107–119.

Introduces the Logarithmic Market Scoring Rule (LMSR), which can be understood as both a scoring rule and a subsidized automated market maker. The bounded-loss property ($b \ln n$) makes it ideal for prediction markets where a platform is willing to subsidize liquidity in exchange for information aggregation.

5. Hanson, R. (2007). "Logarithmic Market Scoring Rules for Modular Combinatorial Information Aggregation." Journal of Prediction Markets, 1(1), 3–15.

Extends the LMSR to combinatorial markets where traders can express beliefs about joint distributions of multiple events. Important for understanding how market making scales to complex prediction market architectures.

Market Microstructure Theory

6. O'Hara, M. (1995). Market Microstructure Theory. Blackwell Publishers.

The classic textbook on market microstructure. Covers the Glosten-Milgrom model, the Kyle model, inventory models, and the information economics of trading in depth. Dense but comprehensive. Best used as a reference alongside the original papers.

7. Foucault, T., Pagano, M., and Roell, A. (2013). Market Liquidity: Theory, Evidence, and Policy. Oxford University Press.

A more modern treatment of liquidity theory, covering both theoretical models and empirical evidence. Includes excellent chapters on the determinants of bid-ask spreads, the role of market makers, and the design of trading mechanisms. More accessible than O'Hara for readers coming from a prediction markets background.

8. Easley, D. and O'Hara, M. (1987). "Price, Trade Size, and Information in Securities Markets." Journal of Financial Economics, 19(1), 69–90.

Extends the Glosten-Milgrom framework to allow trade size to carry information. Shows that large trades are more likely to be informed, which has implications for how market makers should size their quotes at different price levels.

Prediction Market Design

9. Othman, A., Pennock, D. M., Reeves, D. M., and Sandholm, T. (2013). "A Practical Liquidity-Sensitive Automated Market Maker." ACM Transactions on Economics and Computation, 1(3), 1–25.

Proposes a liquidity-sensitive variant of the LMSR that adjusts the liquidity parameter $b$ based on trading volume. When volume is high, the market maker offers tighter spreads; when volume is low, spreads widen. A practical improvement over the fixed-$b$ LMSR for real prediction markets.

10. Abernethy, J., Chen, Y., and Vaughan, J. W. (2013). "Efficient Market Making via Convex Optimization, and a Connection to Online Learning." ACM Transactions on Economics and Computation, 1(2), 1–39.

Connects automated market making to online convex optimization, showing that market scoring rules are equivalent to online learning algorithms. This theoretical bridge is valuable for designing new market-making mechanisms with desirable properties (bounded loss, adaptivity, composability).

11. Chen, Y. and Pennock, D. M. (2007). "A Utility Framework for Bounded-Loss Market Makers." Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence (UAI).

Develops a framework for understanding the bounded-loss property of market scoring rules from a utility-theoretic perspective. Clarifies when and why platforms should prefer bounded-loss market makers over traditional order books.

Adverse Selection and Informed Trading

12. Easley, D., Lopez de Prado, M. M., and O'Hara, M. (2012). "Flow Toxicity and Liquidity in a High-Frequency World." Review of Financial Studies, 25(5), 1457–1493.

Introduces the VPIN (Volume-Synchronized Probability of Informed Trading) metric, which estimates the fraction of toxic (informed) order flow in real time. Originally developed for equity markets but directly applicable to prediction markets. The paper also demonstrates VPIN's ability to predict liquidity crises.

13. Glosten, L. R. (1994). "Is the Electronic Open Limit Order Book Inevitable?" Journal of Finance, 49(4), 1127–1161.

Examines competition between market makers and limit order books. Argues that under certain conditions, an open limit order book dominates a specialist system. Relevant for understanding why some prediction markets use order books while others use AMMs.

14. Budish, E., Cramton, P., and 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.

Analyzes how speed advantages create adverse selection in modern markets and proposes frequent batch auctions as an alternative to continuous-time limit order books. The insights about latency-driven adverse selection apply to prediction markets with API-based trading.

Automated Market Makers and DeFi

15. Adams, H., Zinsmeister, N., et al. (2021). "Uniswap v3 Core." Uniswap Labs White Paper.

Describes the concentrated liquidity mechanism used in Uniswap v3, which allows liquidity providers to focus capital within specific price ranges. This innovation is directly relevant to prediction market AMMs, where LPs want to provide liquidity only near the current probability estimate.

16. Angeris, G. and Chitra, T. (2020). "Improved Price Oracles: Constant Function Market Makers." Proceedings of the 2nd ACM Conference on Advances in Financial Technologies.

Provides a rigorous analysis of constant-function market makers (CFMMs), including conditions under which they accurately track reference prices. Important for understanding the theoretical properties of AMM-based prediction markets.

17. Milionis, J., Moallemi, C. C., Roughgarden, T., and Zhang, A. L. (2022). "Automated Market Making and Loss-Versus-Rebalancing." Working Paper.

Introduces the concept of "loss-versus-rebalancing" (LVR) as a more precise characterization of LP losses in AMMs. LVR measures the cost of providing liquidity against arbitrageurs and is the AMM analog of adverse selection in traditional market making.

Practical Market Making

18. Cartea, A., Jaimungal, S., and Penalva, J. (2015). Algorithmic and High-Frequency Trading. Cambridge University Press.

A comprehensive textbook on algorithmic trading with strong coverage of optimal market making. Chapters 10–11 develop the Avellaneda-Stoikov model in detail and extend it to various practical settings. The mathematical treatment is rigorous but accessible to readers with a quantitative background.

19. Guant, O., Lehalle, C.-A., and Fernandez-Tapia, J. (2012). "Optimal Portfolio Liquidation with Limit Orders." SIAM Journal on Financial Mathematics, 3(1), 740–764.

Addresses the problem of optimally liquidating a position using limit orders—a problem faced by market makers when they need to unwind accumulated inventory. The solution involves posting increasingly aggressive orders as the deadline approaches, a strategy directly applicable to prediction markets approaching resolution.

20. Spooner, T., Fearnley, J., Savani, R., and Koukorinis, A. (2018). "Market Making via Reinforcement Learning." Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems (AAMAS).

Applies deep reinforcement learning to the market-making problem, showing that learned policies can outperform analytical solutions in realistic environments. An interesting direction for prediction market makers who want to adapt to complex, non-stationary conditions without explicitly modeling every aspect of the environment.