Cryptofinance with Quantitative Investment Management

  • Weihao Han

Student thesis: Doctoral ThesisPhD


Cryptocurrencies, a product of financial technology (FinTech) innovations, are a recent phenomenon drawing extensive attention. This essay dissects cryptocurrencies in two distinct dimensions: asset pricing and portfolio management. Cryptocurrency returns are highly non-normal, casting doubt on the standard performance metrics. Therefore, we apply almost stochastic dominance (ASD), which does not require any assumption about the return distribution or degree of risk aversion. From 29 long-short cryptocurrency factor portfolios, we find eight that dominate our four benchmarks. Their returns cannot be fully explained by the existing three-factor coin model, so we develop a new three-factor model where momentum is replaced by a mispricing factor, based on size and risk-adjusted momentum, which significantly improves pricing performance. The new three-factor model is robust to various out-of-sample tests. Additionally, we investigate diversification benefits on a cryptocurrency-factor level within a portfolio management framework. The uncertainties of input parameters cause the traditional mean-variance framework to be misleading. To enhance the out-of-sample performance of optimised portfolios, we combine machine learning and various asset-allocation strategies to tackle the estimation errors of these input parameters. Through estimating out-of-sample performance metrics, we find that cryptocurrency factors formed on size and momentum groups can add substantial diversification benefits (e.g., statistically significant risk-adjusted returns) to a traditional stock-bond portfolio across both conventional and novel asset-allocation strategies. Furthermore, our results are robust to transaction costs, an alternative benchmark, and a rolling-window estimation. Therefore, this essay suggests that investors could i) estimate the expected cryptocurrencies by our new three-factor model, and ii) include cryptocurrencies in a stock-bond portfolio to gain considerable diversification benefits.
Date of Award18 Jan 2023
Original languageEnglish
Awarding Institution
  • University of Bath
SponsorsUniversity of Bath
SupervisorDavid Newton (Supervisor) & Emmanouil Platanakis (Supervisor)


  • Cryptocurrencies
  • Asset Pricing
  • Portfolio Management
  • Machine Learning
  • FinTech

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