Pricing of Cryptocurrency - Use of Deep Learning and Recurrent Neural Networks technology- Application to Bitcoin, Ethereum and Litecoin - Empirical Evidence

The cryptocurrency market has become increasingly accessible and significant to the financial markets. This is understood by not only major financial firms, governments, and investors, but also the individual market participants globally. We delve into the history of cryptocurrency to begin our examination of the Bitcoin, Ethereum and Litecoin. Understanding the circumstances of their humble beginning, the purpose it served, and the path of their evolution, helps us to create a fuller understanding of its functions, its limitations, and the drivers of its value. This enables us to identify key market factors and variables for deployment within a robust approach for pricing and product offerings associated with Bitcoin, Ethereum and Litecoin. In order to fully capture the volume, variety, and velocity of data associated with these cryptocurrencies, the use of machine learning can provide an advantageous approach to model development for cryptocurrency pricing. This paper provides the development of a promising initial prototype pricing model for Bitcoin, Ethereum and Litecoin. Our proposed pricing models resulted in an average 7% difference between actual and predicted price for Bitcoin and Ethereum, and a 4% difference for Litecoin along a timeline, through the use of machine learning and deep learning, artificial neural networks using the contributing factors of key variables and how they influence and capture pricing and investor behaviour. We also identify theinclusion of additional datasets, such as sentiment market data into the model, along with larger exploration of Blockchain and raw transaction mining to increase the accuracy and forecasting ability of the model.