Adapting Natural Language Processing Strategies for Stock Price Prediction
| dc.contributor.author | Voigt, Frederic | |
| dc.contributor.editor | Stolzenburg, Frieder | |
| dc.date.accessioned | 2023-09-20T04:20:44Z | |
| dc.date.available | 2023-09-20T04:20:44Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | Due to the parallels between Natural Language Processing (NLP) and stock price prediction (SPP) as a time series problem, an attempt is made to interpret SPP as an NLP problem. As adaptable techniques word vector representations, pre-trained language models, advanced recurrent neural networks, unsupervised learning methods, and multimodal methods are introduced and it is outlined how they can be transferred into the stock prediction domain. | en |
| dc.identifier.doi | 10.18420/ki2023-dc-03 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/42403 | |
| dc.language.iso | en | |
| dc.pubPlace | Bonn | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | DC@KI2023: Proceedings of Doctoral Consortium at KI 2023 | |
| dc.subject | Stock Price Prediction; Financial Analysis; Quantitative Analysis; Fundamental Analysis; NLP | en |
| dc.title | Adapting Natural Language Processing Strategies for Stock Price Prediction | en |
| dc.type | Text | |
| gi.citation.endPage | 29 | |
| gi.citation.startPage | 20 | |
| gi.conference.date | 45195 | |
| gi.conference.location | Berlin | |
| gi.conference.sessiontitle | Doctoral Consortium at KI 2023 | |
| gi.document.quality | digidoc |
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