The digital asset market is an emerging market of blockchain-based cryptographic assets, characterized by extreme volatility, non-linear dependencies, and a rapidly evolving ecosystem. Studies on optimizing digital-asset-only portfolios are still sparse, fragmented, and undocumented. This survey provides a comprehensive examination of portfolio optimization methods tailored to this market. Drawing on 119 publications discovered via a systematic literature review spanning from 2017 to 2025, we present a detailed bibliometric analysis that illuminates research trends, publication patterns, and thematic gaps. We also provide an overview of this space, focusing on the most commonly used portfolio optimization approaches, evaluation metrics, and digital assets involved. The relevant literature is organized into four primary categories: (a) traditional and statistical methods, (b) evolutionary algorithms and swarm intelligence, (c) machine learning and deep learning, and (d) reinforcement learning. Within each category, we describe the most frequently used portfolio optimization methods, while highlighting representative works that illustrate the strengths and weaknesses of these specific approaches in digital-asset-only portfolios. By consolidating previously fragmented literature, this survey fosters a holistic understanding of digital asset portfolio optimization, providing a roadmap for future investigation, while serving as a point of reference offering guidance to researchers and practitioners navigating this evolving field.
Demosthenous et al. (Thu,) studied this question.
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