Extraction of Forward-looking Financial Information for Stock Price Prediction from Annual Reports Using NLP Techniques

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dc.contributor.authorGlodd, Alexander
dc.contributor.authorHristova, Diana
dc.date.accessioned2022-12-27T19:18:51Z
dc.date.available2022-12-27T19:18:51Z
dc.date.issued2023-01-03
dc.description.abstractAnnual reports are one of the most important sources of information for financial decisions. They contain forward-looking statements (FLS), which describe future trends and expectations. Thus, several studies deal with the automated identification of FLS, where the latest ones involve a combination of a rule-based approach and machine learning classification. In this paper, we extend this research with state-of-the-art NLP methods. We use DistilBERT for FLS identification and determine their sentiment with FinBERT. The result is processed by a Random Forest model for stock price growth prediction of different periods. Our evaluation shows that DestilBERT achieves higher accuracies on FLS identification than existing methods. For short-term stock price rate prediction, the extracted FLS information together with historical stock data outperforms the sole use of historical stock data. For mid-term prediction, using FLS alone with DestilBERT shows the best result. Finally, in the long-term, FLS provide no benefit.
dc.format.extent10
dc.identifier.doi10.24251/HICSS.2023.679
dc.identifier.isbn978-0-9981331-6-4
dc.identifier.other7c0d2316-c89b-4175-b412-a39b7dedf1b4
dc.identifier.urihttps://hdl.handle.net/10125/103313
dc.language.isoeng
dc.relation.ispartofProceedings of the 56th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData Analytics, Leadership, Business Values
dc.subject10-k
dc.subjectannual report
dc.subjectbert
dc.subjectforward-looking statements
dc.subjectstock price prediction
dc.titleExtraction of Forward-looking Financial Information for Stock Price Prediction from Annual Reports Using NLP Techniques
dc.type.dcmitext
prism.startingpage5572

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