A1126
Title: CEOs on social media and stock market predictability
Authors: Kang-Pyo Lee - Fairfield University (United States)
Suyong Song - University of Iowa (United States) [presenting]
Abstract: Machine learning techniques are applied to high-dimensional social media data from CEO postings, and they have been shown to be useful in predicting stock market indicators. We create a large, unique sample of CEO users on Twitter, and construct hashtag and sentiment time series. Findings confirm that the select list of hashtags and sentiments have predictive power on the stock return, trading volume, volatility, and stock price direction. We also find that the predictive power of CEO sentiments still stands after controlling for well-known macroeconomic and financial variables.
