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Automated Market Makers: A Stochastic Optimization Approach for Profitable Liquidity Concentration
Ali Sunyaev / Daniel Kirste / Niclas Kannengießer / Paul-Niklas Ken Kandora / Simon Caspar Zeller / Steffen Rebennack
liquidity / optimizationResource overview

Automated Market Makers: A Stochastic Optimization Approach for Profitable Liquidity Concentration

Paper2025

by Ali Sunyaev, Daniel Kirste, Niclas Kannengießer, Paul-Niklas Ken Kandora, Simon Caspar Zeller, Steffen Rebennack

Tags
liquidityoptimizationstochastic processes

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Understand this paper

Suggested foundations for stochastic processes and pricing and market microstructure and execution. Specialist prerequisites vary by paper.

Before you read

  1. Conditional Expectation

    Understand what it means to condition on current information.

  2. Stochastic Processes, Brownian Motion and Itô's Lemma

    Follow Brownian dynamics and the second-order term in Itô calculus.

  3. Order Books

    Read bid, ask, depth and queue-based market state.

  4. Order Flow and Adverse Selection

    Understand why fills and order flow can carry information.

Go further

  1. Market Model and Risk-Neutral Valuation

    Connect stochastic dynamics to no-arbitrage prices.

  2. Simulating SDEs

    Translate continuous dynamics into a numerical experiment.

  3. Market Impact

    Connect trading speed and size to execution cost.

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