Sentiment-Aware Mean-Variance Portfolio Optimization for Cryptocurrencies

2025-08-22

AI Summary

This study addresses the insufficient integration of technical indicators and market sentiment in cryptocurrency portfolio optimization. Methodologically, it proposes a dynamic multi-source signal fusion framework that jointly leverages the 14-day RSI and SMA to capture price momentum, employs VADER for initial news sentiment screening, and—novelty introduced—utilizes Google Gemini, a large language model (LLM), to perform dual validation of sentiment polarity and contextual plausibility. Calibrated sentiment scores are then embedded into return forecasting to drive a rolling-window mean-variance optimization with weight constraints. The key contribution lies in establishing a synergistic modeling mechanism integrating technical indicators with hierarchical sentiment signals (lexicon-based + LLM-enhanced). Backtesting results show the strategy achieves a cumulative return of 38.72%, substantially outperforming Bitcoin (8.85%) and an equally weighted portfolio (21.65%), with a Sharpe ratio of 1.1093—demonstrating the efficacy of LLM-augmented sentiment analysis in asset allocation.

Technology Category

• Search and Optimization: Algorithm Configuration
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining

Application Category

• Web Mining and Content Analysis: Sentiment analysis and opinion mining
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

This paper presents a dynamic cryptocurrency portfolio optimization strategy that integrates technical indicators and sentiment analysis to enhance investment decision-making. The proposed method employs the 14-day Relative Strength Index (RSI) and 14-day Simple Moving Average (SMA) to capture market momentum, while sentiment scores are extracted from news articles using the VADER (Valence Aware Dictionary and sEntiment Reasoner) model, with compound scores quantifying overall market tone. The large language model Google Gemini is used to further verify the sentiment scores predicted by VADER and give investment decisions. These technical indicator and sentiment signals are incorporated into the expected return estimates before applying mean-variance optimization with constraints on asset weights. The strategy is evaluated through a rolling-window backtest over cryptocurrency market data, with Bitcoin (BTC) and an equal-weighted portfolio of selected cryptocurrencies serving as benchmarks. Experimental results show that the proposed approach achieves a cumulative return of 38.72, substantially exceeding Bitcoin's 8.85 and the equal-weighted portfolio's 21.65 over the same period, and delivers a higher Sharpe ratio (1.1093 vs. 0.8853 and 1.0194, respectively). However, the strategy exhibits a larger maximum drawdown (-18.52%) compared to Bitcoin (-4.48%) and the equal-weighted portfolio (-11.02%), indicating higher short-term downside risk. These results highlight the potential of combining sentiment and technical signals to improve cryptocurrency portfolio performance, while also emphasizing the need to address risk exposure in volatile markets.

Problem

• Integrating sentiment analysis and technical indicators for cryptocurrency portfolio optimization
• Enhancing investment returns by combining market momentum and news sentiment signals
• Addressing risk exposure in volatile cryptocurrency markets through constrained optimization

Innovation

• Integrates technical indicators with sentiment analysis for optimization
• Uses VADER and Google Gemini for sentiment verification
• Applies constrained mean-variance optimization to cryptocurrency portfolios