Valuating Cryptocurrency Assets using Linear Regression, HRL, and LSTM: Machine Learning Evidence

DOI

10.2991/978-94-6463-298-9_58How to use a DOI?

Keywords

Cryptocurrency; Blockchain; Price Prediction; Machine Learning; Evaluation Metrics

Abstract

Since cryptocurrencies have grown in popularity as a form of investment, many researchers and investors are now interested in making predictions about their future value. This article seeks to explore and analyze three machine learning models for predicting bitcoin prices, i.e., linear regression, hierarchical reinforcement learning (HRL), and long short-term memory (LSTM). According to the findings, the Random Forest model fared better at predicting Bitcoin prices than other conventional machine learning models like Linear Regression and Support Vector Regression. With a low Mean Absolute Percentage Error, the HRL model, which is based on sentiment analysis of social media data, demonstrated encouraging results in predicting bitcoin prices. Last but not least, the deep learning-based LSTM model beat other models at predicting the price of bitcoin. These models have drawbacks because they are based on past data and might not take quick market shifts or unforeseen events into consideration. Future studies could investigate how real-time data and news stories can be used to increase the predictive power of machine learning models for cryptocurrencies. Overall, this work demonstrates the potential of machine learning in forecasting financial markets and adds to the expanding body of literature on cryptocurrency price prediction. The findings of this study may help traders and investors make wise selections in the bitcoin market.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Haixin Shen
PY  - 2023
DA  - 2023/11/30
TI  - Valuating Cryptocurrency Assets using Linear Regression, HRL, and LSTM: Machine Learning Evidence
BT  - Proceedings of the 2023 International Conference on Finance, Trade and Business Management (FTBM 2023)
PB  - Atlantis Press
SP  - 535
EP  - 543
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6463-298-9_58
DO  - 10.2991/978-94-6463-298-9_58
ID  - Shen2023
ER  -