  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.
