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.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/7271 |
| Date | 01 June 2002 |
| Creators | Kim, Adlar J., Shelton, Christian R. |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | en_US |
| Detected Language | English |
| Format | 7 p., 2119856 bytes, 1370177 bytes, application/postscript, application/pdf |
| Relation | AIM-2002-009, CBCL-217 |
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