Publication Date

Fall 12-16-2023

Abstract

Forecasting in financial markets with various technologies is critical nowadays. The Bitcoin cryptocurrency has grown in popularity in recent years, and as a result, many people all around the world have attempted to forecast its price. To improve forecast accuracy, we must employ multiple types of data and diverse approaches. This thesis combines textual and financial data and employs statistical methods and machine learning to forecast Bitcoin's price as precisely as possible. We show the performance of each strategy using Bitcoin data at the end of each chapter.

Degree Name

Statistics

Level of Degree

Masters

Department Name

Mathematics & Statistics

First Committee Member (Chair)

James Degnan

Second Committee Member

Mohammad Motamed

Third Committee Member

Ronald Christensen

Document Type

Thesis

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