Financial prediction and trading via reinforcement learning and soft computing
"The overall goal of this research is to develop a practical intelligent system that can be used in investment decision-making, especially for high-frequency security trading, to help investors realize more desired and reliable returns. The study is specific to two key areas which have direct bearing on high-frequency financial investment issues, i.e. financial time series short-term prediction and discrete technical security trading. The research addresses quantitative data modeling and analysis in financial stock markets using artificial intelligent methodologies. Assisted by traditional soft computing approaches, the focus of the work is to provide a systematic treatment of reinforcement learning design for intelligent high-frequency financial trading systems. The results presented in this dissertation represent an effort toward generic and robust implementations of on-line reinforcement learning / neuro-dynamic programming designs under a very large scale financial market system"--Introduction, page 8.
Engineering Management and Systems Engineering
Ph. D. in Engineering Management
University of Missouri--Rolla
x, 142 pages
Note about bibliography
Includes bibliographical references (pages 129-141).
© 2005 Hailin Li, All rights reserved.
Dissertation - Citation
Neural networks (Computer science)
Reinforcement learning (Machine learning)
Print OCLC #
Link to Catalog Record
Full-text not available: Request this publication directly from Missouri S&T Library or contact your local library.http://merlin.lib.umsystem.edu/record=b5746513~S5
Li, Hailin, "Financial prediction and trading via reinforcement learning and soft computing" (2005). Doctoral Dissertations. 1650.
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