Forecasting stock prices using sentiment information in annual reports - A neural network and support vector regression approach

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F13%3A39896546" target="_blank" >RIV/00216275:25410/13:39896546 - isvavai.cz</a>
Result on the web
<a href="http://www.wseas.org/multimedia/journals/economics/2013/235702-202.pdf" target="_blank" >http://www.wseas.org/multimedia/journals/economics/2013/235702-202.pdf</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Forecasting stock prices using sentiment information in annual reports - A neural network and support vector regression approach
Original language description
Stock price forecasting has been mostly realized using quantitative information. However, recent studies have demonstrated that sentiment information hidden in corporate annual reports can be successfully used to predict short-run stock price returns. Soft computing methods, like neural networks and support vector regression, have shown promising results in the forecasting of stock price due to their ability to model complex non-linear systems. In this paper, we apply several neural networks and ?-support vector regression models to predict the yearly change in the stock price of U.S. firms. We demonstrate that neural networks and ?-support vector regression perform better than linear regression models especially when using the sentiment information. The change in the sentiment of annual reports seems to be an important determinant of long-run stock price change. Concretely, the negative and uncertainty categories of terms were the key factors of the stock price return. Profitability a
Czech name
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Czech description
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Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
AE - Management, administration and clerical work
OECD FORD branch
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Project
<a href="/en/project/GA13-10331S" target="_blank" >GA13-10331S: The role of text information in corporate financial distress prediction models – country-specific and industry-specific approaches</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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