"Machine Learning Techniques for Quantitative Finance in Cryptocurrency" by Giancarlo Sanchez
 

Document Type

Dissertation

Degree

Doctor of Philosophy (PhD)

Major/Program

Applied Mathematical Sciences

First Advisor's Name

Zhongming Wang

First Advisor's Committee Title

Co-Committee Chair

Second Advisor's Name

Svetlana Roudenko

Second Advisor's Committee Title

Co-Committee Chair

Third Advisor's Name

Hakima Bessaih

Third Advisor's Committee Title

Committee Member

Fourth Advisor's Name

Enrique Villamor

Fourth Advisor's Committee Title

Committee Member

Fifth Advisor's Name

Arun Upadhyay

Fifth Advisor's Committee Title

Committee Member

Keywords

Machine Learning, Bitcoin, Financial Markets

Date of Defense

6-19-2023

Abstract

This research delves deep into the application of machine learning techniques in the realm of cryptocurrency markets, with a specific focus on Bitcoin. The work explores forecasting Bitcoin prices, identifying patterns in Bitcoin transactions, and the innovative use of reinforcement learning for algorithmic trading.

In the initial segment, we develop a comprehensive framework for forecasting Bitcoin prices. Through meticulous experimentation and optimization, we demonstrate the effectiveness of various machine learning models in predicting the notoriously volatile Bitcoin market. The validity of the chosen methods is established through statistical significance and model performance metrics.

In the following section, the research identifies unique patterns and behaviors in Bitcoin transactions by applying advanced machine learning algorithms to cluster transactional data. The findings derived from this analysis can enhance our understanding of Bitcoin transaction dynamics, providing valuable insights for investors, regulators, and other market participants.

In the culminating phase of the research, we introduce a novel approach using reinforcement learning, a dynamic subset of machine learning, to improve algorithmic trading strategies and decision-making processes in the high-frequency world of cryptocurrency trading.

Collectively, this research provides valuable insights into the growing field of cryptocurrency trading and investment by applying machine learning techniques. The potential implications of the research findings could affect future market analysis, algorithmic trading strategies, and even broader financial technologies.

Identifier

FIDC011188

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