In summary, the goal of the project is to contribute to the development of the next generation of volatility prediction models that combine sentiment signals with advanced statistical methods. In addition, we plan to apply a Bayesian regression model that jointly uses low-frequency and high-frequency return data to generate more precise volatility estimates and, consequently, predictions.

We will investigate various model extensions to account for phenomena such as regime switches, non-linearities, and interactions among predictors. This allows for dealing with high-dimensional data and for doing Bayesian model averaging which results in improved predictions compared to using one single model. Our proposed approach is based on Bayesian regression models with spike-and-slab priors. The analysis includes both methods for automatically generate sentiment lexicons and. Our research questions are then (i) whether sentiment analysis and internet activity variables provide predictive power when used jointly with a large set of other economic predictors, (ii) what is the nature of this potential impact, and (iii) how can this information be used to generate improved volatility predictions. See how Google Trends is being used across the world, by newsrooms, charities, and more. Sentiment analysis of natural language texts is a large and growing field. We use state-of-the-art sentiment analysis tools to process a large amount of heterogeneous data gathered from various sources which include Twitter, StockTwits, Reddiit, Google Finance, Nexis, Google Trends and Wikipedia. We propose to investigate the impact of sentiment and activity volumes of social media, financial news, and search engines on stock volatility. A pipeline, implemented as a web service, allows a decision maker to monitor Twitter’s sentiment regarding Google Trends, enabling users to choose geographic areas for their monitors, and aims to bridge the gap among Google search query data and sentiments that emerge on Twitter about these trends.

Google trends sentiment analysis