Abstract
We uncover latent topics embedded in the management discussion and analysis (MD&A) of financial reports from the listed companies in the US, and we examine the evolution of topics found by a dynamic topic modelling method - Dynamic Embedding Topic Model. Using more than 203k reports with 40M sentences ranging from 1997 to 2017, we find 30 interpretable topics. The evolution of topics follows economics cycles and major industrial events. We validate the significance of these latent topics by the state-of-the-art performance of a simple bankruptcy ensemble classifier trained on both novel features - topical distributed representation of the MD&A, and accounting features.
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Notes
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Electronic Data Gathering, Analysis, and Retrieval system - SEC.
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We did not use the modal weak words because the uncertainty wordlist includes all words in that list, and the modal moderate list is also excluded since it does not have words with strong sentiment modification.
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In Statement of Recommended Accounting Standards No. 15, Financial Accounting Standards Board (FASB).
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“U.S. coal production dropped by more than 10% in 2015 to 897 million short tons, the lowest production level since 1986”, US Energy Information Administration, https://www.eia.gov/todayinenergy/detail.php?id=28732, Retrieved 3rd Mar, 2020.
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Acknowledgments
We appreciate the fruitful discussions with Professor Jonathan Crook, Professor Galina Andreeva, Professor Raffaella Calabrese and other researchers at Business School, University of Edinburgh when Hung Ba was supported by JAIST Research Grant and DRF Grant No. 238003 to work as a visiting scholar. Other errors retain our own.
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Nguyen, BH., Kiyoaki, S., Huynh, VN. (2021). Topics in Financial Filings and Bankruptcy Prediction with Distributed Representations of Textual Data. In: Dong, Y., Ifrim, G., Mladenić, D., Saunders, C., Van Hoecke, S. (eds) Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12461. Springer, Cham. https://doi.org/10.1007/978-3-030-67670-4_19
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