Masters Theses
Keywords and Phrases
Bitcoin; Cryptology; Ethereum; Partial Least Squares Regression; SHA3
Abstract
"With the invention of Bitcoin in 2009, as a seemingly timed response to the ongoing financial crisis, the popularity of the cryptocurrency has since continued to grow. Just this year, the Security Exchange Commission approved Bitcoin for exchange traded funds, allowing major investment firms to begin product trading. With this approval, and during this very moment of writing, Bitcoin has entered a bull market and reached a record value of over 72,000 USD. In addition, the Bitcoin halving event in April of 2024 is expected to increase demand even further. It has been anticipated that Bitcoin and other cryptocurrencies will continue to grow in popularity as an alternate source of investment. The purpose of this thesis is to review two important cryptographic algorithms currently being implemented to validate and trade cryptocurrencies, SHA-3 and ECDSA. Furthermore, we provide a general overview of how Bitcoin, Ethereum, and their networks operate independently of third parties. Finally, a successful analysis using Partial Least Squares regression is conducted to predict the moving average price of Bitcoin with hope the methodology could be implemented in future trading strategies" -- Abstract, p. iii
Advisor(s)
Hu, Wenqing
Committee Member(s)
Adekpedjou, Akim
Olbricht, Gayla R.
Department(s)
Mathematics and Statistics
Degree Name
M.S. in Applied Mathematics
Publisher
Missouri University of Science and Technology
Publication Date
Summer 2024
Pagination
ix, 68 pages
Note about bibliography
Includes_bibliographical_references_(pages 66-67)
Rights
©2024 Paul Kenneth O'Connor , All Rights Reserved
Document Type
Thesis - Open Access
File Type
text
Language
English
Thesis Number
T 12401
Electronic OCLC #
1477930218
Recommended Citation
O'Connor, Paul Kenneth, "Cryptographic Algorithms, Cryptocurrencies, and a Predictive Model of Bitcoin Value by Pls Regression" (2024). Masters Theses. 8198.
https://scholarsmine.mst.edu/masters_theses/8198