Transformer-based Summarization and Sentiment Analysis of SEC 10-K Annual Reports for Company Performance Prediction

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dc.contributor.authorHsieh , Hsin-Ting
dc.contributor.authorHristova, Diana
dc.date.accessioned2021-12-24T17:32:55Z
dc.date.available2021-12-24T17:32:55Z
dc.date.issued2022-01-04
dc.description.abstractAnnual reports published by companies contain important insights regarding their performance and are often analyzed in a manual, subjective manner. We address this point by combining the streams of research on text summarization and topic modelling with the one on sentiment analysis. Our approach consists of the steps of text summarization using BERTSUMEXT, topic modelling with LDA, sentiment analysis with FinBERT, and performance prediction with Decision Trees and Random Forest. The result provides decision makers with an interpretable and condensed representation of the content of annual reports, together with its relationship to future company performance. We evaluate our approach on 10-K reports, demonstrating both its interpretability for analysts and explanatory power regarding future company performance.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2022.218
dc.identifier.isbn978-0-9981331-5-7
dc.identifier.urihttp://hdl.handle.net/10125/79550
dc.language.isoeng
dc.relation.ispartofProceedings of the 55th 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.subjectMachine Learning and Predictive Analytics in Accounting, Finance, and Management
dc.subjectbert
dc.subjectlda
dc.subjectsentiment
dc.subjectstock price prediction
dc.subjectsummarization
dc.titleTransformer-based Summarization and Sentiment Analysis of SEC 10-K Annual Reports for Company Performance Prediction
dc.type.dcmitext

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