Doctoral Dissertations
Keywords and Phrases
Bitcoin; Deep learning; Financial market; Machine learning; On-chain data; Price prediction
Abstract
"The Bitcoin market, like traditional financial markets, is a complex system with intricate interdependencies and nonlinear interactions among various market elements. This, coupled with the inherent uncertainty and high volatility present, makes predicting Bitcoin price movements difficult. The lack of understanding of the underlying market dynamics often results in significant losses for investors and traders. Existing studies have focused on the use of predictive models, which have not sufficiently captured the complexities of the market and cannot forecast extreme market events. This research endeavors to bridge this gap by combining data processing techniques, computational intelligence, and complex systems theory to describe and predict the Bitcoin market.The first contribution of this research is developing of a novel multimodal machine learning model that combines time series data (on-chain data) and visual data (chart images), while also exploring feature engineering and output types that have not been sufficiently explored. The second contribution of this research is using System Dynamics modeling to holistically investigate the intricate relationships among various factors influencing the dynamics of the Bitcoin market and to test market behavior under different scenarios. Finally, the last contribution of this research is using Agent-Based Modeling to examine the impact of the widespread adoption of predictive models in the Bitcoin market. The findings from this study could be used by traders, investors, and portfolio managers to improve their investment decisions, ultimately maximizing returns while minimizing financial risk"-- Abstract, p. iv
Advisor(s)
Enke, David Lee, 1965-
Committee Member(s)
Dagli, Cihan H., 1949-
Zhang, Hongxian
Liu, Jinling
Corns, Steven
Department(s)
Engineering Management and Systems Engineering
Degree Name
Ph. D. in Systems Engineering
Publisher
Missouri University of Science and Technology
Publication Date
Spring 2025
Pagination
xv, 167 pages
Note about bibliography
Includes_bibliographical_references_(pages 59, 89, 120, 142, 156 and 162-166)
Rights
©2024 Oluwadamilare Akinpelu Omole , All Rights Reserved
Document Type
Dissertation - Open Access
File Type
text
Language
English
Thesis Number
T 12467
Recommended Citation
Omole, Oluwadamilare Akinpelu, "A Hybrid Data Processing, Computational Intelligence, And Complex Systems Modeling Approach For Describing And Predicting The Bitcoin Market" (2025). Doctoral Dissertations. 3368.
https://scholarsmine.mst.edu/doctoral_dissertations/3368
