Impact of News on the Commodity Market: Dataset and Results

TL;DR

This paper introduces a framework for extracting diverse informational dimensions from news headlines related to commodities, demonstrating its significant impact on gold price predictions using a novel annotated dataset.

Contribution

It presents a new framework for extracting multi-dimensional news information and a dataset of annotated headlines, extending analysis beyond sentiment to include price movements and comparisons.

Findings

Extracted information significantly influences gold price predictions

The dataset contains 11,412 annotated news headlines from 2000-2019

Framework captures various news dimensions beyond sentiment

Abstract

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.