A Literature Review of Strategic Cryptocurrency Portfolio Optimization Leveraging Deep Learning Models

Abstract

The rise of cryptocurrency markets has presented both opportunities and challenges for investors, particularly due to the volatile nature of digital assets such as Ethereum (ETH), FLOW, and Ripple (XRP). Effective portfolio management in this domain requires sophisticated techniques capable of capturing price trends and predicting future movements with high accuracy. This study proposes a literature review on deep learning-based approach for cryptocurrency portfolio management. Additionally, the study also includes leveraging Long Short-Term Memory (LSTM) networks—a variant of Recurrent Neural Networks (RNN) to forecast the price movements of ETH, FLOW, and XRP. The LSTM model deployed was designed to process time-series data and handle the unique complexities of the cryptocurrency market, such as volatility and non-linear patterns. By optimizing the model using the ADAM optimizer and employing key performance metrics like Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), the study evaluates the model’s predictive accuracy. The results demonstrate the LSTM model's potential in forecasting cryptocurrency price trends and enhancing portfolio decision-making by providing data-driven insights into risk management and asset allocation. This research contributes significantly to the growing literature on applying deep learning models in financial markets and offers practical implications for investors seeking to optimize their cryptocurrency portfolios. The study also highlights the broader applicability of LSTM networks in predicting price movements across different digital assets, emphasizing their utility in managing the inherent risks of cryptocurrency investments.