<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="dereli-saraclar-2019-convolutional">
    <titleInfo>
        <title>Convolutional Neural Networks for Financial Text Regression</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Neşat</namePart>
        <namePart type="family">Dereli</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Murat</namePart>
        <namePart type="family">Saraclar</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2019-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Fernando</namePart>
            <namePart type="family">Alva-Manchego</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Eunsol</namePart>
            <namePart type="family">Choi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Daniel</namePart>
            <namePart type="family">Khashabi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Florence, Italy</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <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.</abstract>
    <identifier type="citekey">dereli-saraclar-2019-convolutional</identifier>
    <identifier type="doi">10.18653/v1/P19-2046</identifier>
    <location>
        <url>https://aclanthology.org/P19-2046</url>
    </location>
    <part>
        <date>2019-07</date>
        <extent unit="page">
            <start>331</start>
            <end>337</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Convolutional Neural Networks for Financial Text Regression
%A Dereli, Neşat
%A Saraclar, Murat
%Y Alva-Manchego, Fernando
%Y Choi, Eunsol
%Y Khashabi, Daniel
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F dereli-saraclar-2019-convolutional
%X 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.
%R 10.18653/v1/P19-2046
%U https://aclanthology.org/P19-2046
%U https://doi.org/10.18653/v1/P19-2046
%P 331-337

Markdown (Informal)

[Convolutional Neural Networks for Financial Text Regression](https://aclanthology.org/P19-2046) (Dereli & Saraclar, ACL 2019)

• Convolutional Neural Networks for Financial Text Regression (Dereli & Saraclar, ACL 2019)

ACL

• Neşat Dereli and Murat Saraclar. 2019. Convolutional Neural Networks for Financial Text Regression. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 331–337, Florence, Italy. Association for Computational Linguistics.