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面向加密货币投资组合的情绪感知均值—方差优化

文章 arXiv papers · 作者: Qizhao Chen
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总结

本文提出一种结合技术信号与新闻情绪的动态加密货币投资组合方法。该方法使用14日相对强弱指数和简单移动平均线表示动量，由VADER分析新闻情绪，并使用Google Gemini进行验证。组合信号用于受约束均值—方差优化框架中的预期收益估计。

据报告，针对多种加密货币进行的回测，在风险调整后收益和累计增长方面超过了动量策略、比特币多空策略和等权重投资组合。对仅使用情绪、仅使用技术指标以及两者结合的方案进行比较后，结果表明加入情绪可能提升表现的一致性。论文还报告称，市场承压时期出现了大幅回撤，并指出风险管理仍有待解决。描述没有提供精确的表现数据、交易成本假设、投资组合约束或数据时点详情，因此无法根据这段摘要独立评估所声称收益及其稳健性。

核心观点

  • 该策略将RSI和SMA信号与加密货币新闻情绪结合起来。
  • VADER生成情绪信号，并使用大型语言模型进行验证。
  • 这些信号为受约束均值—方差投资组合优化器提供预期收益依据。
  • 报告的回测优于几项所述基准，并发现结合不同类型信号带来的收益表现更一致。
  • 市场承压期间出现的大幅回撤表明仍需加强风险控制。

标签

加密资产市场情绪技术指标投资组合构建回测风险管理
全文
# 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.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。