Please use this identifier to cite or link to this item:
http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7271Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.creator | Kim, Adlar J. | - |
| dc.creator | Shelton, Christian R. | - |
| dc.date | 2004-10-20T21:05:02Z | - |
| dc.date | 2004-10-20T21:05:02Z | - |
| dc.date | 2002-06-01 | - |
| dc.date.accessioned | 2013-10-09T02:48:53Z | - |
| dc.date.available | 2013-10-09T02:48:53Z | - |
| dc.date.issued | 2013-10-09 | - |
| dc.identifier | AIM-2002-009 | - |
| dc.identifier | CBCL-217 | - |
| dc.identifier | http://hdl.handle.net/1721.1/7271 | - |
| dc.identifier.uri | http://koha.mediu.edu.my:8181/xmlui/handle/1721 | - |
| dc.description | 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. | - |
| dc.format | 7 p. | - |
| dc.format | 2119856 bytes | - |
| dc.format | 1370177 bytes | - |
| dc.format | application/postscript | - |
| dc.format | application/pdf | - |
| dc.language | en_US | - |
| dc.relation | AIM-2002-009 | - |
| dc.relation | CBCL-217 | - |
| dc.subject | AI | - |
| dc.subject | input/output HMM | - |
| dc.subject | market-making | - |
| dc.subject | reinforcement learning | - |
| dc.subject | stock order flow model | - |
| dc.title | Modeling Stock Order Flows and Learning Market-Making from Data | - |
| Appears in Collections: | MIT Items | |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.