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<p>Financial prediction is complex due to the stochastic nature of the stock market. Semi-structured financial documents present comprehensive financial data in tabular formats, such as earnings, profit-loss statements, and balance sheets, and can often contain rich technical analysis along with a textual discussion of corporate history, and management analysis, compliance, and risks. Existing research focuses on the textual and audio modalities of financial disclosures from company conference calls to forecast stock volatility and price movement, but ignores the rich tabular data available in financial reports. Moreover, the economic realm is still plagued with a severe under-representation of various communities spanning diverse demographics, gender, and native speakers. In this work, we show that combining tabular data from financial semi-structured documents with text transcripts and audio recordings not only improves stock volatility and price movement prediction by 5-12% but also reduces gender bias caused due to audio-based neural networks by over 30%.</p>
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Mathur, Puneet; Goyal, Mihir; Sawhney, Ramit; Mathur, Ritik; Leidner, Jochen L.; Dernoncourt, Franck; Manocha, Dinesh (2022): DocFin: Multimodal Financial Prediction and Bias Mitigation using Semi-structured Documents. Findings of the Association for Computational Linguistics: EMNLP 2022 (Empirical Methods in Natural Language Processing), December 2022, Abu Dhabi, United Arab Emirates, S. 1933-1940.
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The website of Coburg University of Applied Sciences was translated using translation software provided by a third-party provider such as DeepL. The official text is the German version of the website. No liability is assumed, either explicitly or implicitly, for the accuracy, reliability, or correctness of the translations into another language.

DocFin: Multimodal Financial Prediction and Bias Mitigation using Semi-structured Documents

Financial prediction is complex due to the stochastic nature of the stock market. Semi-structured financial documents present comprehensive financial data in tabular formats, such as earnings, profit-loss statements, and balance sheets, and can often contain rich technical analysis along with a textual discussion of corporate history, and management analysis, compliance, and risks. Existing research focuses on the textual and audio modalities of financial disclosures from company conference calls to forecast stock volatility and price movement, but ignores the rich tabular data available in financial reports. Moreover, the economic realm is still plagued with a severe under-representation of various communities spanning diverse demographics, gender, and native speakers. In this work, we show that combining tabular data from financial semi-structured documents with text transcripts and audio recordings not only improves stock volatility and price movement prediction by 5-12% but also reduces gender bias caused due to audio-based neural networks by over 30%.

Titel:

DocFin: Multimodal Financial Prediction and Bias Mitigation using Semi-structured Documents

Veröffentlichungsdatum:

01.12.2022

Publikationsart:

Beitrag zu wissenschaftlicher Konferenz/Tagung

Forschungsschwerpunkt:

kein FSP zugeordnet

Medien:

Findings of the Association for Computational Linguistics: EMNLP 2022 (Empirical Methods in Natural Language Processing), December 2022, Abu Dhabi, United Arab Emirates

DOI:

Weblink:

Heft:

Band:

Artikelnummer:

ISBN:

Autoren:

Puneet Mathur, Mihir Goyal, Ramit Sawhney, Ritik Mathur, Jochen Leidner, Franck Dernoncourt, Dinesh Manocha

Medien:

Findings of the Association for Computational Linguistics: EMNLP 2022 (Empirical Methods in Natural Language Processing), December 2022, Abu Dhabi, United Arab Emirates

Herausgeber:

Association for Computational Linguistics

Seiten:

1933-1940

Open Access:

Peer reviewed:

Ja

Zitierung:

Mathur, Puneet; Goyal, Mihir; Sawhney, Ramit; Mathur, Ritik; Leidner, Jochen L.; Dernoncourt, Franck; Manocha, Dinesh (2022): DocFin: Multimodal Financial Prediction and Bias Mitigation using Semi-structured Documents. Findings of the Association for Computational Linguistics: EMNLP 2022 (Empirical Methods in Natural Language Processing), December 2022, Abu Dhabi, United Arab Emirates, S. 1933-1940.

Autoren:

Puneet Mathur, Mihir Goyal, Ramit Sawhney, Ritik Mathur, Jochen Leidner, Franck Dernoncourt, Dinesh Manocha