@inproceedings{armbrust-etal-2020-computational,
    title = "A Computational Analysis of Financial and Environmental Narratives within Financial Reports and its Value for Investors",
    author = {Armbrust, Felix  and
      Sch{\"a}fer, Henry  and
      Klinger, Roman},
    editor = "El-Haj, Dr Mahmoud  and
      Athanasakou, Dr Vasiliki  and
      Ferradans, Dr Sira  and
      Salzedo, Dr Catherine  and
      Elhag, Dr Ans  and
      Bouamor, Dr Houda  and
      Litvak, Dr Marina  and
      Rayson, Dr Paul  and
      Giannakopoulos, Dr George  and
      Pittaras, Nikiforos",
    booktitle = "Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "COLING",
    url = "https://aclanthology.org/2020.fnp-1.31",
    pages = "181--194",
    abstract = "Public companies are obliged to include financial and non-financial information within their cor- porate filings under Regulation S-K, in the United States (SEC, 2010). However, the requirements still allow for manager{'}s discretion. This raises the question to which extent the information is actually included and if this information is at all relevant for investors. We answer this question by training and evaluating an end-to-end deep learning approach (based on BERT and GloVe embeddings) to predict the financial and environmental performance of the company from the {``}Management{'}s Discussion and Analysis of Financial Conditions and Results of Operations{''} (MD{\&}A) section of 10-K (yearly) and 10-Q (quarterly) filings. We further analyse the mediating effect of the environmental performance on the relationship between the company{'}s disclosures and financial performance. Hereby, we address the results of previous studies regarding environ- mental performance. We find that the textual information contained within the MD{\&}A section does not allow for conclusions about the future (corporate) financial performance. However, there is evidence that the environmental performance can be extracted by natural language processing methods.",
}