Case Study Commodities Trading with Market Sentiment

In this case study, we explore how using sentiment data in an investment focused on commodities can significantly outperform a passive strategy. We conducted a backtest on the Gold and Oil WTI, comparing the performance of an active, simple sentiment-powered strategy against the standard Buy-and-Hold.

Case of Gold with GLD ETF

How it is calculated:

The sentiment data was extracted from the Commodities Navigator, an application developed by Alpha Data Analytics that analyzes insights from over 50,000 global sources in 72 languages using artificial intelligence (AI), and GLD ETF 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 a GLD ETF and the infrequent rebalancing, which occurs on average every four days. The period covered the previous year, from February 22, 2024, to the date of writing, February 23, 2025.

Results for Gold:

One-third Better Returns: Sentiment-driven strategy achieved returns one-third better compared to the GLD Buy-and-Hold over the same period. Annual return 37.3% vs. 47%, an excess of +9.64%.

Less Risk: The risk of sentiment approach measured by volatility was 24% lower than the Buy-and-Hold, and 15% lower measured by drawdowns, making it a safer investment option than traditional Buy-and-Hold. Annual also in favor of sentiment data from Commodities Navigator.