Predicting Stock Price Dynamics

Challenge the traditionally used VAR(p) and GARCH models with neural networks


Jonas Mielck, University of Kiel / stackOcean GmbH


Prior research has shown that it is possible to include sentiment data in, for example, GARCH or VAR(p) models and thereby increase model accuracy. This projects key question is how these sentiment augmented models perform in comparison to machine learning approaches like neural networks. Therefore, the traditional sentiment augmented models are seen as benchmarks and different ML approaches will be tried to see if the accuracy can be increased.


A dataset containing 1761 days of stock specific trading data extended by sentiment data calculated from news about the company in the years 2013 - 2020. The dataset has dimensions: 1761 rows & 19 columns.

How you can contribute

I am happy about everyone who wants to work jointly on the project. You should have knowledge in at least one of the areas: time series statistics, data mining, sentiment analysis or machine learning models for time series data in general. I am also happy to simply exchange ideas on these topics.