Is there any sentiment or animals’ spirits in the financial markets’ zoo? A narrative machine-learning analysis of the relationship between news in media and returns on the Chinese commodity futures market
Billio and co-authors (2026) study news sentiment and text signals in commodities using a large language model. The abstract mentions excess returns but not a benchmark, an out-of-sample test, transaction costs, multiple testing or the size of the universe.
“We finally design a long-short trading strategy that consistently outperforms several benchmark approaches.”
AssetCommoditiesfutures
StrategyNews sentiment
TechniqueLarge language modeltransformer
GeographyChina
Also stated: Excess return, Long-short, Text and news
Missing from abstract: benchmark, out-of-sample test, costs, multiple testing, universe
Institutional-grade audit of this strategy.
£5 — Request the audit- Audit of the paper claims accounting for costs, luck, crises, originality, geography, etc.
- Reproduction code integratable into Bloomberg, IBKR, etc.
- Bias-free datasets for backtesting included
- Delivered in 2 working days
The paper's neighbourhoodNews sentiment and text signals by year
The family is counted across the whole universe. Asset, method and authors are counted inside the family.
| Similar papers | citation | same method | same asset | same authors | |
|---|---|---|---|---|---|
| Crude oil arbitrage and momentum trading strategies with targeting volatility by using Large Language Models by Garmash | 2026 | ||||
| When Text Becomes Tradable: Financial-domain Language Models and the Asymmetric Absorption of News in Agricultural Futures Markets by Parvini et al. | 2026 | ||||
| A Replication Study of Zhang (2025): Why News Sentiment Fails to Predict Asset Returns by Andrews | 2026 | ||||
| QuantHarness: Price-Driven Multi-Agent LLMs for High-Frequency Trading by Xiong et al. | 2026 | ||||
| Execution-Aware Alpha Mining: Teaching LLM Factor Agents to Account for Trading Costs by Nagireddy | 2026 |