<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="del-corro-hoffart-2021-stock">
    <titleInfo>
        <title>From Stock Prediction to Financial Relevance: Repurposing Attention Weights to Assess News Relevance Without Manual Annotations</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Luciano</namePart>
        <namePart type="family">Del Corro</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Johannes</namePart>
        <namePart type="family">Hoffart</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Third Workshop on Economics and Natural Language Processing</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Udo</namePart>
            <namePart type="family">Hahn</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Veronique</namePart>
            <namePart type="family">Hoste</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Amanda</namePart>
            <namePart type="family">Stent</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Punta Cana, Dominican Republic</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We present a method to automatically identify financially relevant news using stock price movements and news headlines as input. The method repurposes the attention weights of a neural network initially trained to predict stock prices to assign a relevance score to each headline, eliminating the need for manually labeled training data. Our experiments on the four most relevant US stock indices and 1.5M news headlines show that the method ranks relevant news highly, positively correlated with the accuracy of the initial stock price prediction task.</abstract>
    <identifier type="citekey">del-corro-hoffart-2021-stock</identifier>
    <identifier type="doi">10.18653/v1/2021.econlp-1.6</identifier>
    <location>
        <url>https://aclanthology.org/2021.econlp-1.6</url>
    </location>
    <part>
        <date>2021-11</date>
        <extent unit="page">
            <start>45</start>
            <end>49</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T From Stock Prediction to Financial Relevance: Repurposing Attention Weights to Assess News Relevance Without Manual Annotations
%A Del Corro, Luciano
%A Hoffart, Johannes
%Y Hahn, Udo
%Y Hoste, Veronique
%Y Stent, Amanda
%S Proceedings of the Third Workshop on Economics and Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F del-corro-hoffart-2021-stock
%X We present a method to automatically identify financially relevant news using stock price movements and news headlines as input. The method repurposes the attention weights of a neural network initially trained to predict stock prices to assign a relevance score to each headline, eliminating the need for manually labeled training data. Our experiments on the four most relevant US stock indices and 1.5M news headlines show that the method ranks relevant news highly, positively correlated with the accuracy of the initial stock price prediction task.
%R 10.18653/v1/2021.econlp-1.6
%U https://aclanthology.org/2021.econlp-1.6
%U https://doi.org/10.18653/v1/2021.econlp-1.6
%P 45-49

Markdown (Informal)

[From Stock Prediction to Financial Relevance: Repurposing Attention Weights to Assess News Relevance Without Manual Annotations](https://aclanthology.org/2021.econlp-1.6) (Del Corro & Hoffart, ECONLP 2021)

• From Stock Prediction to Financial Relevance: Repurposing Attention Weights to Assess News Relevance Without Manual Annotations (Del Corro & Hoffart, ECONLP 2021)

ACL

• Luciano Del Corro and Johannes Hoffart. 2021. From Stock Prediction to Financial Relevance: Repurposing Attention Weights to Assess News Relevance Without Manual Annotations. In Proceedings of the Third Workshop on Economics and Natural Language Processing, pages 45–49, Punta Cana, Dominican Republic. Association for Computational Linguistics.