Blockchain Financial and Risk Modeling with Tensor Decompositions and Deep Learning for Institutional Investors

Cryptocurrencies are a new asset class that are emerging in importance and adoption in global markets. Public blockchains such as Bitcoin and Ethereum offer access to an unprecedented amount of financial and technical data. With the increasing amount of blockchain data, it is important that investors and policy makers can access standardized methods of financial and risk analysis to be able to make informed investment and regulatory decisions. Blockchain data are often difficult to model. Tensor decompositions have previously been integrated with deep learning models to successfully predict stock and spot prices. However, there exists a gap in the literature of how to model cryptocurrency spot prices and derivatives. The primary objective of this project is to create and compare two tensor-based deep learning models in each of the two key areas: 1) Cryptocurrency Price Dynamics Modeling, and 2) Derivatives Price Dynamics Modeling.

Faculty Supervisor:

Victoria Lemieux;Elina Robeva

Student:

Partner:

AquaNow

Discipline:

Mathematics

Sector:

Professional, scientific and technical services

University:

The University of British Columbia

Program: