Evaluating Impact of Social Media Posts by Executives on Stock Prices
This work addresses stock price prediction for investors by incorporating social media sentiment, but it is incremental as it applies existing methods to new data sources.
The paper tackled the problem of predicting stock closing prices by integrating sentiment from social media posts, specifically from Twitter and Reddit, with historical stock data using time series models, and found that including social media data improved predictions, with executive posts yielding greater improvements.
Predicting stock market movements has always been of great interest to investors and an active area of research. Research has proven that popularity of products is highly influenced by what people talk about. Social media like Twitter, Reddit have become hotspots of such influences. This paper investigates the impact of social media posts on close price prediction of stocks using Twitter and Reddit posts. Our objective is to integrate sentiment of social media data with historical stock data and study its effect on closing prices using time series models. We carried out rigorous experiments and deep analysis using multiple deep learning based models on different datasets to study the influence of posts by executives and general people on the close price. Experimental results on multiple stocks (Apple and Tesla) and decentralised currencies (Bitcoin and Ethereum) consistently show improvements in prediction on including social media data and greater improvements on including executive posts.