Giudici Paolo, Pagnottoni Paolo, Polinesi Gloria
Department of Economics and Management, University of Pavia, Pavia, Italy.
Department of Economics and Social Sciences, Universitá Politecnica delle Marche, Ancona, Italy.
Front Artif Intell. 2020 Apr 24;3:22. doi: 10.3389/frai.2020.00022. eCollection 2020.
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.
近年来，加密货币的使用以及金融自动化咨询服务的使用正在广泛传播。然而，自动化咨询服务尚未挖掘这个新兴市场的潜力，该市场代表了一类可由机器人顾问提出的创新金融产品。因此，我们提出了一种新颖的方法来构建涉及波动性金融工具（如加密货币）的高效投资组合分配策略。换句话说，我们开发了传统马科维茨模型的扩展，该扩展结合了随机矩阵理论和网络度量，以实现增强投资组合风险回报特征的投资组合权重。结果表明，总体而言我们的模型优于几种竞争方案，同时保持相对较低的风险水平。
