Sentiment-Aware Mean-Variance Optimization for Crypto Portfolios

Summary

The paper proposes a dynamic cryptocurrency portfolio method that combines technical signals with news sentiment. It uses the 14-day Relative Strength Index and Simple Moving Average to represent momentum, while VADER analyzes news sentiment and Google Gemini is used for validation. The combined signals inform expected-return estimates in a constrained mean-variance optimization framework.

Backtests across multiple cryptocurrencies reportedly outperform a momentum strategy, a Bitcoin long-short strategy, and an equal-weighted portfolio in risk-adjusted returns and cumulative growth. Comparisons of sentiment-only, technical-only, and combined approaches suggest that adding sentiment can improve consistency. The paper also reports substantial drawdowns during periods of market stress and identifies risk management as an unresolved need. The description provides no precise performance figures, transaction-cost assumptions, portfolio constraints, or details about data timing, so the claimed gains and their robustness cannot be independently assessed from this summary.

Key ideas

• The strategy combines RSI and SMA signals with sentiment extracted from cryptocurrency news.
• VADER produces sentiment signals, which are validated with a large language model.
• Signals inform expected returns in a constrained mean-variance portfolio optimizer.
• The reported backtest outperforms several stated benchmarks and finds more consistent gains from combining signal types.
• Substantial drawdowns during market stress point to a need for additional risk controls.

Tags

# Sentiment-Aware Mean-Variance Portfolio Optimization for Cryptocurrencies


# Sentiment-Aware Mean-Variance Portfolio Optimization for Cryptocurrencies









Cryptocurrency markets are highly volatile and influenced by both price trends and market sentiment, making effective portfolio management challenging. This paper proposes a dynamic cryptocurrency portfolio strategy that integrates technical indicators and sentiment analysis to enhance investment decision-making. Market momentum is captured using the 14-day Relative Strength Index (RSI) and Simple Moving Average (SMA), while sentiment signals are extracted from news articles with VADER and further validated using the Google Gemini large language model. These signals are incorporated into expected return estimates and used in a constrained mean-variance optimization framework. Backtesting across multiple cryptocurrencies shows that the integrated approach outperforms traditional benchmarks, including momentum strategy, Bitcoin Long-Short strategy, and an equal-weighted portfolio, achieving stronger risk-adjusted returns and more consistent cumulative growth. Furthermore, comparing the sentiment-only and technical-only strategies shows that incorporating sentiment information alongside technical indicators can lead to more consistent performance gains. However, the strategies exhibit substantial drawdowns that coincide with known periods of market stress, indicating that additional risk-management components are required to improve stability.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.