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Tuesday, January 24 • 4:30pm - 6:00pm
Poster: News Analytics for forecasting Price of Crude Oil

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News plays a key role in financial markets. As news sources, frequency and volume continue to grow, it is becoming increasingly difficult for analysts and traders to analyze every news that is published daily. As markets react rapidly to news, effective models that incorporate news data are highly sought after. This is not only useful for trading and fund management, but also for risk control. Major news events can have a significant impact on the market and investor sentiment, resulting in rapid changes to market price and value of traded commodities. A solution that can significantly reduce the time spent to gather timely trading insights from daily news would be of great value for traders, asset managers, hedge fund managers, market research analysts as well as retail investors.
As news is primarily unstructured textual data, it is hard to analyze with traditional computer models. However, with recent advances in NLP and machine learning technology; we can automate news gathering, filtering, and analysis to generate quantitative sentiment scores from textual narratives. The solution should help address queries such as given below:
1. Is today’s news likely to have an upward or a downward impact on the price of crude oil?
2. Which news is likely to have the most impact on the price of crude oil?
3. What has been the impact of similar news in the past?
The most commonly traded commodity worldwide is Crude Oil and its various derivatives. Hence we decided to build an application to analyze news for crude oil. We have used historical news and market price data from publicly available sources to develop and tune our algorithms including machine learning models. The solution was developed using open source stack with python and related ecosystem tools. Interactive data visualizations were developed using flask, AngularJS and Highcharts.

Tuesday January 24, 2017 4:30pm - 6:00pm
BioScience Research Collaborative Event Hall 6500 Main Street, Houston, TX 77030-1402

Attendees (1)