Text-Mining in Streams of Textual Data Using Time Series Applied to Stock Market

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F18%3A43914774" target="_blank" >RIV/62156489:43110/18:43914774 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.11118/actaun201866061573" target="_blank" >https://doi.org/10.11118/actaun201866061573</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.11118/actaun201866061573" target="_blank" >10.11118/actaun201866061573</a>

Result language
angličtina
Original language name
Text-Mining in Streams of Textual Data Using Time Series Applied to Stock Market
Original language description
Each day, a lot of text data is generated. This data comes from various sources and may contain valuable information. In this article, we use text mining methods to discover if there is a connection between news articles and changes of the S&P 500 stock index. The index values and documents were divided into time windows according to the direction of the index value changes. We achieved a classification accuracy of 65-74 %.
Czech name
—
Czech description
—

Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
<a href="/en/project/GA16-26353S" target="_blank" >GA16-26353S: Sentiment and its impact on stock markets</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical
Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis
ISSN
1211-8516
e-ISSN
—
Volume of the periodical
66
Issue of the periodical within the volume
6
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
8
Pages from-to
1573-1580
UT code for WoS article
—
EID of the result in the Scopus database
2-s2.0-85060693556

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