Backtesting Sentiment Signals for Trading: Evaluating the Viability of Alpha Generation from Sentiment Analysis
This work addresses the gap in practical trading applications of sentiment analysis for investors, though it is incremental as it builds on existing sentiment-driven finance research.
The study tackled the problem of applying sentiment analysis to trading by backtesting sentiment-based strategies on Dow Jones 30 stocks, finding that all models generated positive returns, with a regression model achieving 50.63% over 28 months and outperforming a benchmark.
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.