Modeling Stock Order Flows and Learning Market-Making from Data

Abstract

Stock markets employ specialized traders, market-makers, designed to provide liquidity and volume to the market by constantly supplying both supply and demand. In this paper, we demonstrate a novel method for modeling the market as a dynamic system and a reinforcement learning algorithm that learns profitable market-making strategies when run on this model. The sequence of buys and sells for a particular stock, the order flow, we model as an Input-Output Hidden Markov Model fit to historical data. When combined with the dynamics of the order book, this creates a highly non-linear and difficult dynamic system. Our reinforcement learning algorithm, based on likelihood ratios, is run on this partially-observable environment. We demonstrate learning results for two separate real stocks.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2002
Accession Number
ADA459806

Entities

People

  • Adlar J. Kim
  • Christian R. Shelton

Organizations

  • Massachusetts Institute of Technology

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • Contracts
  • Hidden Markov Models
  • Information Operations
  • Instructions
  • Learning
  • Machine Learning
  • Markov Models
  • Massachusetts
  • Models
  • Reinforcement Learning
  • Standards

Readers

  • Computational Modeling and Simulation
  • Logistics and Supply Chain Management.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks