Optimizing portfolios in digital finance: an integration of cryptocurrencies with conventional assets

This research aims to underscore the application of machine learning in achieving an optimal portfolio that incorporates both cryptocurrencies and traditional assets by mapping and analyzing the academic literature through bibliometric analysis. This study encompasses the Preferred Reporting and Items for Systematic Reviews and Meta-Analysis (PRISMA) Framework to exclude duplicate, missing information, and irrelevant studies. After a rigorous analysis based on inclusion/exclusion criteria, the bibliometric analysis, facilitated by VOSviewer, encompasses Performance Analysis and Science Mapping. The former involves the publication and citation-related metrics, while the latter incorporates co-authorship and co-occurrence analyses. The study offers the probability to improve the efficiency of a portfolio with the help of machine learning with a focus on cryptocurrencies. The value of this study lies in emphasizing publication growth, collaborative efforts, and influential keywords in the connection between machine learning, portfolio optimization, cryptocurrencies, and traditional assets. An emerging concern in this field is evident, yet very few studies of bibliometric analysis could be found. This study provides an evaluation of recent findings and emerging trends. Being an efficient method used to summarize literature; it also has some limitations as well. Thus, the study highlights the areas for scholars and professionals in further research based on the identified research gaps.