Impact of News on the Commodity Market: Dataset and Results
This work addresses the need for more nuanced news analysis in financial markets, particularly for commodities like gold, but it is incremental as it builds on existing sentiment-based methods.
The authors tackled the problem of extracting diverse information from news headlines beyond sentiment to predict commodity prices, specifically applying their framework to gold and finding that the extracted information significantly impacts future gold prices.
Over the last few years, machine learning based methods have been applied to extract information from news flow in the financial domain. However, this information has mostly been in the form of the financial sentiments contained in the news headlines, primarily for the stock prices. In our current work, we propose that various other dimensions of information can be extracted from news headlines, which will be of interest to investors, policy-makers and other practitioners. We propose a framework that extracts information such as past movements and expected directionality in prices, asset comparison and other general information that the news is referring to. We apply this framework to the commodity "Gold" and train the machine learning models using a dataset of 11,412 human-annotated news headlines (released with this study), collected from the period 2000-2019. We experiment to validate the causal effect of news flow on gold prices and observe that the information produced from our framework significantly impacts the future gold price.