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Reinforcement Learning for Market Making in a Multi-agent Dealer Market
Hua Zheng / Manuela Veloso / Mengda Xu / Nelson Vadori / Prashant Reddy / Sumitra Ganesh
liquidity / market makingResource overview

Reinforcement Learning for Market Making in a Multi-agent Dealer Market

Paper2019

by Hua Zheng, Manuela Veloso, Mengda Xu, Nelson Vadori, Prashant Reddy, Sumitra Ganesh

Tags
liquiditymarket makingreinforcement learningrisk management

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Suggested foundations for market microstructure and execution and risk measurement and stress. Specialist prerequisites vary by paper.

Before you read

  1. Order Books

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

  2. Order Flow and Adverse Selection

    Understand why fills and order flow can carry information.

  3. Random Variables and Distributions

    Represent losses as a distribution rather than one forecast.

  4. Expected Shortfall and Other Tail Risk Measures

    Distinguish a loss quantile from the average loss in the tail.

Go further

  1. Market Impact

    Connect trading speed and size to execution cost.

  2. Implementation Shortfall

    Measure execution relative to the decision price.

  3. VaR Estimation in Practice: Variance-Covariance, Historical Simulation, Monte Carlo

    Compare risk estimators and their assumptions.

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