Machine Learning-Based Analysis of the Association Between Online Texts and Stock Price Movements
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F18%3A43913611" target="_blank" >RIV/62156489:43110/18:43913611 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.4114/intartif.vol21iss61pp95-110" target="_blank" >https://doi.org/10.4114/intartif.vol21iss61pp95-110</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.4114/intartif.vol21iss61pp95-110" target="_blank" >10.4114/intartif.vol21iss61pp95-110</a>
Alternative languages
Result language
angličtina
Original language name
Machine Learning-Based Analysis of the Association Between Online Texts and Stock Price Movements
Original language description
The paper presents the result of experiments that were designed with the goal of revealing the association between texts published in online environments (Yahoo! Finance, Facebook, and Twitter) and changes in stock prices of the corresponding companies at a micro level. The association between lexicon detected sentiment and stock price movements was not confirmed. It was, however, possible to reveal and quantify such association with the application of machine learning-based classification. From the experiments it was obvious that the data preparation procedure had a substantial impact on the results. Thus, different stock price smoothing, lags between the release of documents and related stock price changes, five levels of a minimal stock price change, three different weighting schemes for structured document representation, and six classifiers were studied. It has been shown that at least part of the movement of stock prices is associated with the textual content if a proper combination of processing parameters is selected.
Czech name
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Czech description
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Classification
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)
Result continuities
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)
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Inteligencia Artificial
ISSN
1137-3601
e-ISSN
—
Volume of the periodical
21
Issue of the periodical within the volume
61
Country of publishing house
ES - SPAIN
Number of pages
16
Pages from-to
95-110
UT code for WoS article
000609005300007
EID of the result in the Scopus database
2-s2.0-85046842152