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

Kód výsledku v 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>

Výsledek na webu

<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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Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

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

Popis výsledku anglicky

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

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

AE - Řízení, správa a administrativa

OECD FORD obor

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Projekt

<a href="/cs/project/GA13-10331S" target="_blank" >GA13-10331S: Úloha textové informace v modelech predikce finanční tísně podniků - přístupy specifické podle států a průmyslových odvětví</a><br>

Návaznosti

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

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název periodika

WSEAS Transactions on Business and Economics

ISSN

1109-9526

e-ISSN

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Svazek periodika

10

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

GR - Řecká republika

Počet stran výsledku

13

Strana od-do

293-305

Kód UT WoS článku

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EID výsledku v databázi Scopus

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