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  • Cryptocurrencies should be used with financial automated consultancy. 
  • Network models are generated to enhance automated cryptocurrency portfolio management. 
  • The methodologies involved in these network models include random matrix theory, minimal spanning trees, and centrality measures. 

Different network models are being generated these days to enhance automated cryptocurrency portfolio management. These are focused on stock and financial networks that are built on correlation matrices. 

Enhancement in Automated Crypto Portfolio Management

The use of cryptocurrencies in financial automated consulting is increasing day by day. However, the services of automated consultants are still in the potentially exploitable state of the nascent market. Hence, a novel approach is proposed to build efficient portfolio allocation strategies involving volatile financial instruments such as cryptocurrencies. 

An extension of the traditional Markowitz model is developed that combines network measures with random matrix theory to achieve portfolio weights. From the point of view of methodologies, the analysis should be based on an important stream of literature that focuses on stock and financial networks built on correlation matrices. The correlation matrices are used to infer the hierarchical structure of the stock market. 

Network Models 

Several network models were developed using different methodologies, such as random matrix theory (RMT), the minimal spanning tree (MST), network centrality measures, and portfolio construction.  

  1. Random Matrix Theory: It is widely employed in several fields such as quantum mechanics, economics, finance, condensed matter physics, and wireless communications. This technique is able to remove the noise component from the pure signal, which is basically embedded in correlation matrices. 
  2. Minimal Spanning Tree: It is applied to simplify the relationships given by the correlation matrix, which is obtained from the random matrix approach. It is the representation of the cryptocurrency return time series. It is very much consistent with the literature on stock similarities. 
  3. Network Centrality Measures: This is employed in order to develop a portfolio allocation that takes into account the centrality of a node (cryptocurrency) in the system. It includes several centrality measures, such as counting how many neighbors a node has, the degree centrality, hub and authority centralities, and central measures based on the spectral properties of graphs. 
  4. Portfolio Construction: Correlation-based graphs are useful tools to build optimal investment strategies. Asset correlations are key items in investment theory and risk measurement, in particular for optimization problems. 

In the end, empirical findings are made that include data description and network topology analysis. The average daily returns are all close to zero, according to the general economic theory regarding asset returns. However, different cryptocurrencies exhibit different standard deviations, which means that the variability in returns differs quite strongly among cryptocurrencies. 

Conclusion: 

The results showed that overall models outperform several competing alternatives and maintain a relatively low level of risk. The effectiveness of models achieving better cumulative portfolio performances while keeping a relatively low level of risk The proposed models, which employ RMT, MST, and centrality measures, rapidly adapt to market conditions and are able to yield satisfactory performances during bull market periods. However, the outcomes suggested that a combination of the proposed models should be employed in order to achieve an efficient cryptocurrency allocation strategy. 

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