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Contribution Details
| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | A Machine Learning Approach for a Blockchain-Crypto Portfolio Construction |
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| Institution | ETH Zürich |
| Faculty | Department of Mathematics |
| Date | 2020 |
| Abstract Text | This master’s thesis integrates concepts from the fields of machine learning, quantitative finance, and digital investments in cryptocurrencies and their underlying technology blockchain. This research aims to create and manage actively a blockchain-crypto portfolio based on machine learning prediction models. In this research project, we developed several supervised machine learning models to predict the behavior of three cryptocurrencies with the highest market capitalization (Bitcoin, Ethereum, and Ripple), one blockchain exchange-traded product, and gold. We use different input data types, such as technical indicators (momentum, volume, volatility and trend indicators), economic data, currency exchange rates, commodity prices, and Google Trends, to construct and calibrate predictive models to forecast future crypto-asset returns. We use daily data from 2015 until 2019 to build and train the machine learning models. To test the machine learning models’ out-of-sample accuracy, we use the time frame from January until May 2020. We found out that ensemble techniques such as random forest and gradient boosted trees work particularly well to classify cryptocurrencies’ price direction and regress their bi-weekly returns. Mostly the prediction results for Bitcoin and Ethereum are satisfactory and promising for future use. The machine learning models’ output serves as the investor’s views for the Black-Litterman model to construct and manage an active blockchain-crypto portfolio rebalancing every two weeks. We set portfolio constraints for the rebalancing, such as no short-selling, maximal asset positions, and maximal turnovers. We set a live portfolio from May 2020 until August 2020 with bi-weekly rebalancing to test the constructed active portfolio results. As benchmark portfolios, we take equity exchange-traded funds, cryptocurrency exchange-traded products, an equally weighted and a passive portfolio. The active portfolio results outperformed the returns from traditional investments and reduced the volatility of pure cryptocurrency investments substantially, achieving the highest Sharpe ratio and the lowest drawdown among all portfolios. This research’s output can be carried on in academia, exploring more profound the estimation of the investor’s views in the Black-Litterman model based on machine learning predictions. Additionally, the growing interest in cryptocurrencies offers the industry the opportunity to create this active blockchain-crypto portfolio and launch it in the market. |
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