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
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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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
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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