• Alex Smith
• Andrew Altman
• Joe Geoghegan
• Krista Xhoxhi
• Lauren Krohn

Our goal is to create an algorithmic trading strategy using signals from both sentiment and returns data. To do this we are using current/historic stock data as well as sentiment data from several sources. The sources we are analyzing are Reddit, news sources, as well as written statements in quarterly reports. We plan to run our model on multiple companies in several sectors including tech, finance, and energy. Usign stock and sentiment data, we would utilize both random forest and neural net models to predict a multi-class returns category. Finally, we will use these classifications as our entry and exit signals to our trading strategy.

• Technology
  • Netflix (NFLX)
  • Facebook/Meta (FB)
  • Uber (UBER)
  • Microchip Technology (MCHP)
  • AirBnB (ABNB)
• Energy
  • Diamondback Energy Inc. (FANG)
  • Marathon Oil Corp. (MRO)
  • Devon Energy Corp. (DVN)
  • SunPower Corp. (SPWR)
  • Renewable Energy Group Inc. (REGI)
• Finance
  • McKinsey & Company (MTRX)
  • BlackRock (BLK)
  • PayPal (PYPL)
  • Mercadolibre Inc (MELI)
  • SoFi (SOFI)