Stock Returns

Abstract

The Stock Returns Project was to assist the research of our client, Ziqian Song, to analyze the language used in the financial news and social media discussions surrounding stock-related events, and to derive meaningful insights from this data. Our end goal was to build meaningful tools that can help the client analyze information surrounding the events that may, in the future, predict a stock price move.

The project involved collecting, preprocessing, and analyzing textual data as well as working with stock data from the Wharton Research Data Services (WRDS). Data collection included 49 main categories and 335 subcategories of corporate events with 4.6 million related news and press releases. Following data collection, one of the tasks included identifying 20 influential events for case studies. These are events that have many news reports and tweets surrounding them. We picked 10 stocks that saw significant increases in stock price (surge stocks) and 10 that saw significant decreases in stock price (plunge stocks) to run our data analysis on.

Once we selected our 20 companies to evaluate, we used Python modules to scrape Twitter and Google data relating to each company and their specific case study event. By collecting different predictive features (e.g., emotions, top words, topics), we could find valuable correlations between the events and their discussion online. In our findings, we identified words that appeared the most for both surge and plunge stocks, sentiment on Twitter surrounding each respective event, and larger market trends that surrounded each event.