Morningstar is interested in adding Social Media into our analytics as a predictive indicator of market volitility. Increasingly individuals are sharing "stock tips" thru social media outlets such as /r/WallStreetBets as a way to build momentum. In many ways it's the same ol' "pump and dump" but for the Social Media age.
As you work your way thru this Hackathon you will help Morningstar understand successful techniques on how to extract data from the /r/WallStreetBets website, and other similar subreddits or other similar sites. Upon completion of this challenge our Data Science Team will review your matches, your approach and your results. From there they will select the most interesting submissions and ask you to present your work to the team during a 30 minute review. The winners will be selected from those top submissions.
Morningstar is working with Argonne Labs to build a disinformation indicator on how sensitive a company is to disinformation shared on Social Media. For example, how likely is Dominion Voting Systems impacted by Social Media posts shared leading up to and beyond the US Election. Led by our CDO, Alex Golbin, Morningstar is working to publish this "VIX" for Social Media Disruption and even measuring how sensitive a particular company/sector is to disinformation.
In this Hackathon, you will attempt to build a program to scrape /r/WallStreetBets and extract out likely company ticker references. You will then use those matches files to populate the schema defined in the sample_extract_structure.xlsx file with your matches plus the raw data that you used to derive the match.
Although the assignment is intended to be done in Python (a common language for Data Science) and all examples are in Python, prior experience in Python is not required. It is a fairly simple language to learn and use.