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Integrating sentiment analysis and topic detection in financial news for stock movement prediction

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F18%3A39913510" target="_blank" >RIV/00216275:25410/18:39913510 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1145/3278252.3278267" target="_blank" >http://dx.doi.org/10.1145/3278252.3278267</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1145/3278252.3278267" target="_blank" >10.1145/3278252.3278267</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Integrating sentiment analysis and topic detection in financial news for stock movement prediction

  • Original language description

    Media-expressed information in financial news are critical for stock market prediction. Nevertheless, researchers have primarily focused on the role of sentiment analysis in predicting stock returns and volatility. Here we show that topics discussed in the financial news may carry additional important information. We use a combination of sentiment analysis (using finance-specific dictionary-based approach) and topic detection (using latent dirichlet allocation) to predict one-day-ahead stock movements of major US companies. The proposed system employs a deep neural network to model complex stock market relations. We demonstrate the effectiveness of this approach compared to baselines, such as support vector machines and sentiment- and topic-based models used separately.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    50206 - Finance

Result continuities

  • Project

    <a href="/en/project/GA16-19590S" target="_blank" >GA16-19590S: Topic and sentiment analysis of multiple textual sources for corporate financial decision-making</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2018

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    ICBIM 18 : Proceedings of the 2nd International Conference on Business and Information Management

  • ISBN

    978-1-4503-6545-1

  • ISSN

  • e-ISSN

    neuvedeno

  • Number of pages

    5

  • Pages from-to

    158-162

  • Publisher name

    ACM (Association for Computing Machinery)

  • Place of publication

    New York

  • Event location

    Barcelona

  • Event date

    Sep 20, 2018

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000458690700032