Outperforming the S&P 500: A Sentiment-Driven Backtest

In this case study, we explore how integrating sentiment data into an investment can significantly outperform a passive strategy. We conducted a backtest on the S&P 500, comparing the performance of an active, simple sentiment-powered strategy against the standard Buy-and-Hold.

How it is calculated:

The sentiment data was extracted from the application developed by Alpha Data Analytics that analyzes insights from over 50,000 global sources in 72 languages using artificial intelligence (AI), and SPDR S&P 500 ETF Trust daily (close) prices. 

Sentiment data has been used for daily portfolio re-balance based on whether market sentiment was positive or negative. Specifically, when week-over-week sentiment outpaced the price, the strategy was to buy and hold until sentiment remained higher than the price. Conversely, when the price exceeded sentiment, the action was to sell existent long and take an equivalent short position. For comparability, both sentiment and price were normalized using their rolling z-scores and rolling to ensure no look-forward biases (leakages) were introduced. 

Friction costs and transaction fees are excluded as they are negligible, given the narrow spreads of a highly liquid instrument like the SPY and the infrequent rebalancing, which occurs on average every three days. The period covered the previous year, from February 22, 2024, to the date of writing, February 22, 2025.

Results:

Twice Better Returns: Sentiment-driven strategy achieved three times better returns compared to the S&P 500 Buy-and-Hold over the same period. Annual return 17.5% vs. 39.6%, an excess of +22,1%.

Less Risk: The risk of sentiment approach measured by volatility was 21% lower than the Buy-and-Hold, and 16% lower measured by drawdowns, making it a safer investment option than traditional Buy-and-Hold. Annual Sharpe Ratio 1.15 vs. 3.33 also in favor of Sentiment data.

Conclusion:

The results showcase the advantage sentiment analysis brings to improve investment outcomes. By incorporating timely market sentiment data into portfolio management, investors can not only improve returns but also lower the risk. This case study demonstrates that a sentiment-driven approach can provide a superior alternative to conventional S&P 500 Buy-and-Hold. The explanation can be quite simple, by aggregating all available information with automated news analysis there is no more need to spend much time understanding market dynamics from news, events, or fundamentals just to end up in a data source bias, hit a paywall, struggle with a language barrier, censorship limitations or get overwhelmed by a market panic or fear of missing out (FOMO).

Disclaimer: This case study is for informational purposes only and does not constitute investment advice. Past performance is not a guarantee of future returns.