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A Hybrid Data Processing, Computatio...

Omole, Oluwadamilare Akinpelu,

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A Hybrid Data Processing, Computational Intelligence, and Complex Systems Modeling Approach for Describing and Predicting the Bitcoin Market /

Record Type:	Electronic resources : Monograph/item
Title/Author:	A Hybrid Data Processing, Computational Intelligence, and Complex Systems Modeling Approach for Describing and Predicting the Bitcoin Market // Oluwadamilare Akinpelu Omole.
Author:	Omole, Oluwadamilare Akinpelu,
Description:	1 electronic resource (185 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-08, Section: B.
Contained By:	Dissertations Abstracts International86-08B.
Subject:	Systems science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31638745
ISBN:	9798302883872

A Hybrid Data Processing, Computational Intelligence, and Complex Systems Modeling Approach for Describing and Predicting the Bitcoin Market /
Omole, Oluwadamilare Akinpelu,

A Hybrid Data Processing, Computational Intelligence, and Complex Systems Modeling Approach for Describing and Predicting the Bitcoin Market /Oluwadamilare Akinpelu Omole. - 1 electronic resource (185 pages)

Source: Dissertations Abstracts International, Volume: 86-08, Section: B.

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.

English

ISBN: 9798302883872Subjects--Topical Terms:

3168411
Systems science.
Subjects--Index Terms:

Bitcoin

A Hybrid Data Processing, Computational Intelligence, and Complex Systems Modeling Approach for Describing and Predicting the Bitcoin Market /
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245 1 2 $a A Hybrid Data Processing, Computational Intelligence, and Complex Systems Modeling Approach for Describing and Predicting the Bitcoin Market / $c Oluwadamilare Akinpelu Omole.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (185 pages)
336 $a text $b txt $2 rdacontent
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500 $a Source: Dissertations Abstracts International, Volume: 86-08, Section: B.
500 $a Advisors: Enke, David Committee members: Dagli, Cihan; Corns, Steven; Liu, Jinling; Zhang, Hongxian.
502 $b Ph.D. $c Missouri University of Science and Technology $d 2024.
520 $a 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.
546 $a English
590 $a School code: 0587
650 4 $a Systems science. $3 3168411
650 4 $a Finance. $3 542899
650 4 $a Engineering. $3 586835
653 $a Bitcoin
653 $a Deep learning
653 $a Financial market
653 $a Machine learning
653 $a On-chain data
653 $a Price prediction
690 $a 0790
690 $a 0508
690 $a 0800
690 $a 0537
710 2 $a Missouri University of Science and Technology. $b Systems Engineering. $e degree granting institution. $3 3823582
720 1 $a Enke, David $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-08B.
790 $a 0587
791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31638745