Network Models to Enhance Automated Cryptocurrency Portfolio Management

Area of Science:

• Quantitative Finance
• Computational Finance
• Financial Technology (FinTech)

Background:

• The increasing adoption of cryptocurrencies and automated financial consultancy (robo-advisors) presents new opportunities.
• Current robo-advisor services underutilize the potential of cryptocurrencies as innovative financial products.
• There is a need for advanced portfolio allocation strategies for volatile digital assets.

Purpose of the Study:

• To propose a novel approach for efficient portfolio allocation strategies involving cryptocurrencies.
• To extend the traditional Markowitz model by integrating advanced methodologies.
• To enhance the risk-return profiles of investment portfolios containing volatile financial instruments.

Main Methods:

• Extension of the Markowitz portfolio optimization model.
• Integration of Random Matrix Theory (RMT) for analyzing financial instrument relationships.
• Application of network measures to capture complex interdependencies in financial markets.
• Development of portfolio weights to optimize risk and return.

Main Results:

• The proposed model demonstrates superior performance compared to several competing portfolio allocation strategies.
• The model effectively enhances the risk-return profiles of portfolios.
• The strategy maintains a relatively low level of risk, even with volatile assets like cryptocurrencies.

Conclusions:

• The novel approach successfully integrates cryptocurrencies into automated financial consultancy.
• The combined use of Random Matrix Theory and network measures offers a robust framework for portfolio optimization.
• This research provides a valuable tool for robo-advisors seeking to leverage the cryptocurrency market effectively.