@inproceedings{dereli-saraclar-2019-convolutional,
    title = "Convolutional Neural Networks for Financial Text Regression",
    author = "Dereli, Ne{\c{s}}at  and
      Saraclar, Murat",
    editor = "Alva-Manchego, Fernando  and
      Choi, Eunsol  and
      Khashabi, Daniel",
    booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
    month = jul,
    year = "2019",
    address = "Florence, Italy",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/P19-2046/",
    doi = "10.18653/v1/P19-2046",
    pages = "331--337",
    abstract = "Forecasting financial volatility of a publicly-traded company from its annual reports has been previously defined as a text regression problem. Recent studies use a manually labeled lexicon to filter the annual reports by keeping sentiment words only. In order to remove the lexicon dependency without decreasing the performance, we replace bag-of-words model word features by word embedding vectors. Using word vectors increases the number of parameters. Considering the increase in number of parameters and excessive lengths of annual reports, a convolutional neural network model is proposed and transfer learning is applied. Experimental results show that the convolutional neural network model provides more accurate volatility predictions than lexicon based models."
}