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Updated: Nov 12, 2025

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Network Models to Enhance Automated Cryptocurrency Portfolio Management
Paolo Giudici1, Paolo Pagnottoni1, Gloria Polinesi2
1Department of Economics and Management, University of Pavia, Pavia, Italy.
Abstract:
The usage of cryptocurrencies, together with that of financial automated consultancy, is widely spreading in the last few years. However, automated consultancy services are not yet exploiting the potentiality of this nascent market, which represents a class of innovative financial products that can be proposed by robo-advisors. For this reason, we propose a novel approach to build efficient portfolio allocation strategies involving volatile financial instruments, such as cryptocurrencies. In other words, we develop an extension of the traditional Markowitz model which combines Random Matrix Theory and network measures, in order to achieve portfolio weights enhancing portfolios' risk-return profiles. The results show that overall our model overperforms several competing alternatives, maintaining a relatively low level of risk.
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