Article
Version 1
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Analyzing Financial Market Trends in Cryptocurrency and Stock Prices Using CNN-LSTM Models
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Version 1
: Received: 14 July 2024 / Approved: 15 July 2024 / Online: 15 July 2024 (04:53:59 CEST)
How to cite: Zhang, X. Analyzing Financial Market Trends in Cryptocurrency and Stock Prices Using CNN-LSTM Models. Preprints 2024, 2024071119. https://doi.org/10.20944/preprints202407.1119.v1 Zhang, X. Analyzing Financial Market Trends in Cryptocurrency and Stock Prices Using CNN-LSTM Models. Preprints 2024, 2024071119. https://doi.org/10.20944/preprints202407.1119.v1
Abstract
This article comprehensively explores multiple aspects of cryptocurrencies and their price forecasting. Firstly, the article introduces the definition of cryptocurrency and its development process on a global scale, especially focusing on the launch of Facebook Libra and China's central bank digital currency, highlighting the importance and influence of digital currency in the global financial market. Subsequently, the article analyzes the advantages of digital currencies over traditional currencies, including improving economic transaction efficiency, reducing transaction costs and enhancing transaction transparency. Meanwhile, the article also explores the challenges and risks potentially brought by the development of digital currencies, such as regulatory uncertainty and market volatility. In this context, the article raises the importance of cryptocurrency price forecasting and introduces the forecasting models and techniques commonly used today. Finally, through specific experimental analysis, the effectiveness of using the deep learning model CNN-LSTM to predict the price of Bitcoin is demonstrated, and the directions of future research and optimization strategy are proposed. In summary, this paper comprehensively presents the research status and prospects of cryptocurrency and its price prediction field through systematic introduction and analysis.
Keywords
Cryptocurrencies; price forecasting; artificial intelligence; deep learning; financial technology
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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