Pricing of cryptocurrency - use of deep learning and recurrent neural networks technology- application to Bitcoin, Ethereum and Litecoin - empirical evidence

Sy, M and Morris, S 2018, 'Pricing of cryptocurrency - use of deep learning and recurrent neural networks technology- application to Bitcoin, Ethereum and Litecoin - empirical evidence', in Proceedings of the 25th Annual Conference of the Multinational Finance Society, Budapest, Hungary, 24-27 June 2018, pp. 1-37.


Document type: Conference Paper
Collection: Conference Papers

Title Pricing of cryptocurrency - use of deep learning and recurrent neural networks technology- application to Bitcoin, Ethereum and Litecoin - empirical evidence
Author(s) Sy, M
Morris, S
Year 2018
Conference name 25th Annual Conference of the Multinational Finance Society
Conference location Budapest, Hungary
Conference dates 24-27 June 2018
Proceedings title Proceedings of the 25th Annual Conference of the Multinational Finance Society
Publisher Global Business Publications
Place of publication United States
Start page 1
End page 37
Total pages 37
Abstract 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.
Subjects Banking, Finance and Investment not elsewhere classified
Keyword(s) Cryptocurrencies
Bitcoin
Ethereum
Litecoin
Pricing
Machine Learning
Deep Machine Learning
Artificial Neural Networks
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