Abstract
Cryptocurrency is reshaping the financial landscape, gaining popularity and acceptance among merchants. Despite the increasing investments in cryptocurrency, its dynamic features, uncertainties, and predictability remain largely unknown, posing significant investment risks. This study aims to understand the factors influencing cryptocurrency value formation. Leveraging advanced artificial intelligence frameworks like fully connected Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM) Recurrent Neural Network, we analyze the price dynamics of Bitcoin, Ethereum, and Ripple. Our findings indicate that ANN relies more on long-term history, while LSTM focuses on short-term dynamics, suggesting LSTM's superior efficiency in utilizing historical information. However, with sufficient historical data, ANN can achieve comparable accuracy to LSTM. This study underscores the predictability of cryptocurrency market prices, though the explanation may vary depending on the machine-learning model employed.
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