Financial prediction and trading via reinforcement learning and soft computing

Author

Hailin Li

Abstract

"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.

Department(s)

Engineering Management and Systems Engineering

Degree Name

Ph. D. in Engineering Management

Publisher

University of Missouri--Rolla

Publication Date

Fall 2005

Pagination

x, 142 pages

Note about bibliography

Includes bibliographical references (pages 129-141).

Rights

© 2005 Hailin Li, All rights reserved.

Document Type

Dissertation - Citation

File Type

text

Language

English

Subject Headings

Financial engineeringNeural networks (Computer science)Dynamic programmingMathematical optimizationReinforcement learning (Machine learning)

Thesis Number

T 8854

Print OCLC #

77494793

Recommended Citation

Li, Hailin, "Financial prediction and trading via reinforcement learning and soft computing" (2005). Doctoral Dissertations. 1650.
https://scholarsmine.mst.edu/doctoral_dissertations/1650