> For the complete documentation index, see [llms.txt](https://opencampus.gitbook.io/coding-waterkant/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://opencampus.gitbook.io/coding-waterkant/projects/predicting-stock-price-dynamics.md). # Predicting Stock Price Dynamics ### Contact Jonas Mielck, University of Kiel / stackOcean GmbH ### Description 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. ### Dataset 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. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://opencampus.gitbook.io/coding-waterkant/projects/predicting-stock-price-dynamics.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.