用新闻情绪分析做交易，三年收益超50%，显著跑赢大盘。
Backtesting Sentiment Signals for Trading: Evaluating the Viability of Alpha Generation from Sentiment Analysis
- 用三种模型分析道琼斯30股新闻情绪，生成交易信号。
- 回归模型28个月收益达50.63%，远超买入持有策略。
- 适合量化交易、金融工程研究者参考，验证情绪价值。
情感分析在产品评论中广泛应用，也通过微博和新闻影响金融市场。尽管已有大量关于情绪驱动金融的研究，但多数聚焦于句子级分类，忽视其在实际交易中的应用。本研究填补这一空白，通过回测评估基于情绪的交易策略生成正阿尔法的能力。使用三种模型（两种分类、一种回归）对道琼斯30只股票的新闻文章进行情绪预测，与买入持有基准策略对比。结果显示所有模型均产生正收益，其中回归模型在28个月内实现50.63%的收益率，显著优于基准策略。这表明情绪分析在提升投资策略和金融决策方面具有潜力。
原文摘要 · Abstract (English)
Sentiment analysis, widely used in product reviews, also impacts financial markets by influencing asset prices through microblogs and news articles. Despite research in sentiment-driven finance, many studies focus on sentence-level classification, overlooking its practical application in trading. This study bridges that gap by evaluating sentiment-based trading strategies for generating positive alpha. We conduct a backtesting analysis using sentiment predictions from three models (two classification and one regression) applied to news articles on Dow Jones 30 stocks, comparing them to the benchmark Buy&Hold strategy. Results show all models produced positive returns, with the regression model achieving the highest return of 50.63% over 28 months, outperforming the benchmark Buy&Hold strategy. This highlights the potential of sentiment in enhancing investment strategies and financial decision-making.
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