Cryptocurrency Assets Valuation Based on LSTM: Evidence from Bitcoin, Ethereum, and Dogecoin

Abstract

In recent decades, data analytics has become increasingly involved in people's daily lives. Machine learning, an important part of data analysis, has also been used in the financial sector. Contemporarily, the high volatility feature of cryptocurrencies has attracted lots of investors, which also brings lots of difficulty to predict and analyze. In fact, the price of cryptocurrencies can also be forecasted based on machine learning. This paper uses historical data of Bitcoin, Ethereum and Dogecoin as inputs to predict the future value based on the LSTM. LSTM model can learn the long-term dependencies in data. According to the analysis, mean absolute error calculate the average size of the error in a set of predictions, regardless of its direction. The results produced can roughly predict the future trends of these three cryptocurrencies. This paper combines the fields of machine learning and finance to predict the future value of cryptocurrencies. These results shed light on guiding further exploration of predicting cryptocurrency assets valuation based on LSTM model.