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Adapting Natural Language Processing Strategies for Stock Price Prediction

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2023

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Gesellschaft für Informatik e.V.

Zusammenfassung

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.

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Voigt, Frederic (2023): Adapting Natural Language Processing Strategies for Stock Price Prediction. DC@KI2023: Proceedings of Doctoral Consortium at KI 2023. DOI: 10.18420/ki2023-dc-03. Gesellschaft für Informatik e.V.. pp. 20-29. Doctoral Consortium at KI 2023. Berlin. 45195

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