Backtesting Sentiment Signals for Trading: Evaluating the Viability of Alpha Generation from Sentiment Analysis

TL;DR

This paper evaluates sentiment analysis-based trading strategies through backtesting on Dow Jones 30 stocks, demonstrating that sentiment signals can generate positive alpha and outperform traditional buy-and-hold approaches.

Contribution

It provides a comprehensive backtesting framework for sentiment-driven trading strategies, comparing multiple models and demonstrating their profitability in real market conditions.

Findings

All sentiment models produced positive returns.

The regression model achieved a 50.63% return over 28 months.

Sentiment signals outperformed the benchmark Buy&Hold strategy.

Abstract

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.