Christian R. Shelton, Assistant Professor
Modeling Stock Order Flows and Learning Market-Making from Data (2002)
by Adlar J. Kim, Christian R. Shelton, and Tomaso Poggio
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 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.
Download Information
Adlar J. Kim, Christian R. Shelton, and Tomaso Poggio (2002). "Modeling Stock Order Flows and Learning Market-Making from Data." Technical report. MIT AI Lab, AI Memo 2002-009.
Bibtex citation
@techreport{KimShePog02,
author = "Adlar J. Kim and Christian R. Shelton and Tomaso Poggio",
title = "Modeling Stock Order Flows and Learning Market-Making from Data",
institution = "{MIT} {AI} Lab",
type = "AI Memo",
year = 2002,
number = "2002-009",
month = June,
}
Modeling Stock Order Flows and Learning Market-Making from Data (2002)
by Adlar J. Kim, Christian R. Shelton, and Tomaso Poggio
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 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.
Download Information
| Adlar J. Kim, Christian R. Shelton, and Tomaso Poggio (2002). "Modeling Stock Order Flows and Learning Market-Making from Data." Technical report. MIT AI Lab, AI Memo 2002-009. | |
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Bibtex citation
@techreport{KimShePog02,
author = "Adlar J. Kim and Christian R. Shelton and Tomaso Poggio",
title = "Modeling Stock Order Flows and Learning Market-Making from Data",
institution = "{MIT} {AI} Lab",
type = "AI Memo",
year = 2002,
number = "2002-009",
month = June,
}