Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6948329 | Decision Support Systems | 2018 | 39 Pages |
Abstract
Share valuations are known to adjust to new information entering the market, such as regulatory disclosures. We study whether the language of such news items can improve short-term and especially long-term (24â¯months) forecasts of stock indices. For this purpose, this work utilizes predictive models suited to high-dimensional data and specifically compares techniques for data-driven and knowledge-driven dimensionality reduction in order to avoid overfitting. Our experiments, based on 75,927 ad hoc announcements from 1996-2016, reveal the following results: in the long run, text-based models succeed in reducing forecast errors below baseline predictions from historic lags at a statistically significant level. Our research provides implications to business applications of decision-support in financial markets, especially given the growing prevalence of index ETFs (exchange traded funds).
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Information Systems
Authors
Stefan Feuerriegel, Julius Gordon,