Ranking for Riches: AI-Powered Crypto Portfolio Management

Author: Denis Avetisyan

A new machine learning approach predicts cryptocurrency performance by ranking assets, offering a potential edge over traditional investment strategies.

The multi-layer perceptron trading algorithm demonstrably outperforms the UCRP benchmark, as evidenced by its cumulative wealth exceeding that of the benchmark and generating significant excess return, or alpha.

This review details a neural network-based algorithm for long-only cryptocurrency portfolio optimization using rank prediction and cross-sectional analysis.

Effective cryptocurrency portfolio management remains challenging due to the inherent volatility and complex interrelationships within the asset class. This paper, ‘Long-only cryptocurrency portfolio management by ranking the assets: a neural network approach’, introduces a novel machine learning algorithm that leverages cross-sectional analysis to predict the relative future performance of cryptocurrencies. Backtesting on over three years of daily market data demonstrates that ranking-based weight allocation consistently outperforms existing methods, achieving a Sharpe ratio of 1.01 and an annualized return of 64.26%. Can this approach provide a robust and scalable solution for navigating the evolving landscape of digital asset investment?

The Essence of Efficient Diversification

Portfolio construction, as formalized by the Markowitz Model and Modern Portfolio Theory, fundamentally centers on the principle of efficient diversification. This approach doesn’t simply advocate for spreading investments across different assets; it rigorously seeks to assemble a combination that achieves the highest possible expected return for a defined level of risk, or conversely, the lowest risk for a target return. The core of this process involves quantifying the relationships between assets – not just individual expected returns and volatilities, but also the correlations between their movements. By strategically allocating capital based on these interdependencies, investors can construct portfolios that lie on the “efficient frontier,” representing the optimal risk-return trade-off. This isn’t about eliminating risk entirely, but about ensuring that every unit of risk taken is justified by an appropriate level of potential reward, thereby maximizing the probability of achieving long-term financial goals.

Accurate return prediction stands as a fundamental, yet remarkably difficult, cornerstone of modern portfolio construction. While the theoretical framework of maximizing returns for a given risk level is well-established, its practical implementation hinges on forecasting future asset performance – a task complicated by inherent market volatility and the influence of countless economic factors. Sophisticated methodologies, ranging from statistical time-series analysis and econometric modeling to machine learning algorithms, are therefore employed in an attempt to discern patterns and predict future returns. However, even these advanced techniques are frequently challenged by unpredictable events and the non-stationary nature of financial markets, meaning that consistently accurate prediction remains an elusive goal. The pursuit of improved predictive power continues to drive research and innovation in financial modeling, with practitioners constantly seeking more robust and reliable methods to inform investment decisions and optimize portfolio performance.

The Sharpe Ratio stands as a cornerstone for assessing investment performance, quantifying risk-adjusted returns – a measure of excess return per unit of volatility. This metric is fundamentally dependent on accurately gauging asset volatility, as higher volatility necessitates a greater return to justify the increased risk. A commonly cited benchmark, the Buy-and-Hold (BAH) strategy, historically demonstrates a Sharpe Ratio around 0.83, serving as a useful, though not definitive, point of comparison for evaluating the efficiency of more complex investment strategies. Consequently, a higher Sharpe Ratio generally indicates a more favorable balance between risk and reward, enabling investors to confidently compare different portfolio options and optimize their investment decisions based on this critical performance indicator.

Harnessing Machine Learning for Portfolio Optimization

Machine learning techniques are gaining prominence in portfolio construction due to their capacity to model non-linear relationships and high-dimensional data, improving return prediction accuracy compared to traditional statistical methods. Algorithms such as regression trees, support vector machines, and particularly neural networks are utilized to forecast asset returns based on historical price data, macroeconomic indicators, and alternative datasets. The application of these techniques aims to identify mispriced assets and optimize portfolio weights, leading to enhanced Sharpe ratios and overall portfolio performance as measured by total return and risk-adjusted metrics. Backtesting and rigorous validation procedures are crucial to mitigate overfitting and ensure the robustness of these machine learning-driven strategies.

Several machine learning methods are utilized to model complex financial data, each with distinct characteristics. Neural Networks, composed of interconnected nodes, excel at identifying non-linear relationships but require substantial data for training. Random Forests, an ensemble method constructing multiple decision trees, mitigate overfitting and provide robust predictions. XGBoost, a gradient boosting algorithm, further optimizes prediction accuracy and computational efficiency, often demonstrating superior performance in structured datasets. Finally, k-Nearest Neighbor employs a non-parametric approach, classifying data points based on proximity to their $k$ nearest neighbors, proving effective for pattern recognition without extensive training requirements; however, its performance is sensitive to feature scaling and the choice of $k$.

Temporal Graph Convolution (TGC) is a machine learning technique used in financial modeling that moves beyond traditional time-series analysis by explicitly incorporating relationships between assets. Unlike methods treating stocks in isolation, TGC constructs a graph where nodes represent stocks and edges define the relationships between them – often based on correlation or co-movement. This graph structure is then combined with temporal data, allowing the model to learn how these relationships evolve over time. By leveraging the network structure, TGC can capture dependencies and transmission effects between assets, resulting in more accurate predictions of future price movements and improved portfolio construction compared to methods that ignore these interdependencies. The convolution operation is performed on the graph, effectively aggregating information from neighboring nodes at each time step, and capturing the dynamic interactions between assets in a portfolio.

Machine learning techniques applied to rank prediction in portfolio construction focus on identifying assets expected to outperform others, rather than predicting absolute returns. This approach utilizes algorithms to assess the relative attractiveness of assets within a defined universe, generating a ranked list for investment selection. Recent studies demonstrate the efficacy of this method, specifically in cryptocurrency markets, where machine learning-based rank prediction strategies have consistently outperformed traditional algorithmic trading systems. Performance gains are typically measured by metrics such as Sharpe ratio and annualized returns, indicating a statistically significant improvement over benchmark algorithms. The method’s success is attributed to its ability to capture complex, non-linear relationships within asset data and adapt to rapidly changing market dynamics.

Strategies and Practical Considerations in Portfolio Management

The Universal Portfolio (UP) framework is a portfolio management approach grounded in the principles of Modern Portfolio Theory (MPT). MPT posits that investors should construct portfolios to maximize expected return for a given level of risk, or minimize risk for a given level of expected return. The UP framework operationalizes MPT by employing a mean-variance optimization process, utilizing historical or forecasted asset returns, volatilities, and correlations. This results in an optimal asset allocation, expressed as portfolio weights, designed to achieve the investor’s desired risk-return profile. The framework’s generality allows for application across various asset classes and investment horizons, providing a foundational structure for more complex portfolio strategies.

Beyond the foundational Universal Portfolio framework, several distinct strategies offer alternative approaches to asset allocation and management. The Buy and Hold strategy represents a passive approach, involving initial asset selection and subsequent retention regardless of market fluctuations. In contrast, the Constant Rebalancing Portfolio (BCRP) periodically adjusts asset weights to maintain a predetermined allocation, requiring transaction activity. More complex algorithmic strategies, such as the Exponential Gradient Algorithm and the Anti-Correlation Algorithm, employ mathematical models to dynamically adjust portfolio weights based on observed market data and predictive analytics, aiming to capitalize on market trends or reduce overall portfolio risk. Each strategy presents a unique trade-off between implementation complexity, transaction costs, and potential returns.

Long-only portfolio strategies, which involve investing in assets expected to appreciate and excluding short selling, are prevalent due to their simplicity and regulatory advantages. However, transaction costs significantly impact the overall profitability of these strategies, particularly when applied to highly volatile assets such as cryptocurrency. Cryptocurrency exchanges often levy higher fees for both order execution and asset transfers compared to traditional markets. Frequent portfolio rebalancing, a common tactic to maintain desired asset allocations, exacerbates these costs, potentially diminishing returns. Therefore, a thorough assessment of exchange fees, slippage, and potential network transaction costs is crucial when implementing long-only strategies with cryptocurrency, and may necessitate lower turnover rates or the selection of exchanges with competitive fee structures.

Empirical validation of the portfolio strategies reveals potential for superior risk-adjusted returns. Specifically, machine learning-driven strategies achieved a Sharpe Ratio exceeding 0.79. This performance surpasses that of ‘follow-the-winner’ strategies, which yielded a Sharpe Ratio of 0.86, and also exceeds the Constant Rebalancing Portfolio (BCRP)’s Information Ratio of 0.61, with our strategies achieving an Information Ratio of 0.61. These metrics indicate a demonstrated ability to generate returns relative to the level of risk assumed, based on historical data analysis.

The pursuit of effective portfolio management, as demonstrated in this research, hinges on discerning patterns within complex datasets. This study’s focus on rank prediction, achieved through a neural network approach, mirrors a core tenet of insightful analysis: reducing multifaceted problems to their essential order. Ada Lovelace observed, “The Analytical Engine has no pretensions whatever to originate anything. It can do whatever we know how to order it to perform.” The engine, like the neural network presented, requires precise instruction – in this case, an algorithm designed to rank assets. The research elegantly translates the ‘how to order’ into a functional system, optimizing performance by focusing on the relative positioning of cryptocurrencies rather than absolute values. This prioritization of order, a simplification of inherent complexity, is precisely where true understanding – and effective portfolio management – begins.

Further Refinements

The pursuit of predictive accuracy, even within a constrained domain like cryptocurrency ranking, reveals a persistent challenge: the signal-to-noise ratio. This work offers a method, not a solution. Future iterations must confront the fundamental limit of data itself – the impossibility of complete information. Simplicity, therefore, remains paramount. Elaborate architectures offer diminishing returns when the underlying reality is stochastic.

A critical extension lies in dynamic ranking. This approach treats assets as static entities, evaluated at discrete intervals. Real markets flow continuously. Algorithms should adapt, not merely react. Consideration of transaction costs, slippage, and exchange limitations-elements currently absent-is not a refinement, but a necessity for practical implementation.

Ultimately, the value lies not in beating a benchmark, but in reducing complexity. The field should prioritize robustness-performance across varied market conditions-over fleeting gains achieved through overfitting to historical data. A parsimonious model, consistently delivering modest returns, serves a purpose. An intricate system, prone to catastrophic failure, does not.

Original article: https://arxiv.org/pdf/2512.08124.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

The Essence of Efficient Diversification

Harnessing Machine Learning for Portfolio Optimization

Strategies and Practical Considerations in Portfolio Management

Further Refinements

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