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| Field |
Value |
| Title: | Market Microstructure: A Self-Organizing Map Approach to Investigate Behavior Dynamics under an Evolutionary Environment |
| Authors: | 陳樹衡
Kampouridis, Michael ; Chen, Shu-Heng ; Tsang, Edward |
| Contributors: | 經濟系 |
| Date: | 2012 |
| Issue Date: | 2014-03-20 16:57:55 (UTC+8) |
| Abstract: | This chapter presents a market microstructure model, which investigates the behavior dynamics in financial markets. We are especially interested in examining whether the markets’ behavior is non-stationary, because this implies that strategies from the past cannot be applied to future time periods, unless they have co-evolved with the markets. In order to test this, we employ Genetic Programming, which acts as an inference engine for trading rules, and Self-Organizing Maps, which is used for clustering the above rules into types of trading strategies. The results on four empirical financial markets show that their behavior constantly changes; thus, agents’ trading strategies need to continuously adapt to the changes taking place in the market, in order to remain effective. |
| Relation: | Natural Computing in Computational Finance Studies in Computational Intelligence Volume 380, 2012, pp 181-197 |
| Data Type: | book/chapter |
| DCField |
Value |
Language |
| dc.contributor (Contributor) | 經濟系 | en_US |
| dc.creator (Authors) | 陳樹衡 | zh_TW |
| dc.creator (Authors) | Kampouridis, Michael ; Chen, Shu-Heng ; Tsang, Edward | en_US |
| dc.date (Date) | 2012 | en_US |
| dc.date.accessioned | 2014-03-20 16:57:55 (UTC+8) | - |
| dc.date.available | 2014-03-20 16:57:55 (UTC+8) | - |
| dc.date.issued (Issue Date) | 2014-03-20 16:57:55 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/64754 | - |
| dc.description.abstract (Abstract) | This chapter presents a market microstructure model, which investigates the behavior dynamics in financial markets. We are especially interested in examining whether the markets’ behavior is non-stationary, because this implies that strategies from the past cannot be applied to future time periods, unless they have co-evolved with the markets. In order to test this, we employ Genetic Programming, which acts as an inference engine for trading rules, and Self-Organizing Maps, which is used for clustering the above rules into types of trading strategies. The results on four empirical financial markets show that their behavior constantly changes; thus, agents’ trading strategies need to continuously adapt to the changes taking place in the market, in order to remain effective. | en_US |
| dc.format.extent | 427761 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.language.iso | en_US | - |
| dc.relation (Relation) | Natural Computing in Computational Finance Studies in Computational Intelligence Volume 380, 2012, pp 181-197 | en_US |
| dc.title (Title) | Market Microstructure: A Self-Organizing Map Approach to Investigate Behavior Dynamics under an Evolutionary Environment | en_US |
| dc.type (Data Type) | book/chapter | en |
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