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

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About: Forecasting stock prices using sentiment information in annual reports - A neural network and support vector regression approach Goto Sponge NotDistinct Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
rdfs:seeAlso	http://www.wseas.org/multimedia/journals/economics/2013/235702-202.pdf
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 and technical analysis ratios have significant effect on the long-run return, too. 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 and technical analysis ratios have significant effect on the long-run return, too. (en)
Title	Forecasting stock prices using sentiment information in annual reports - A neural network and support vector regression approach Forecasting stock prices using sentiment information in annual reports - A neural network and support vector regression approach (en)
skos:prefLabel	Forecasting stock prices using sentiment information in annual reports - A neural network and support vector regression approach Forecasting stock prices using sentiment information in annual reports - A neural network and support vector regression approach (en)
skos:notation	RIV/00216275:25410/13:39896546!RIV14-GA0-25410___
http://linked.open...avai/predkladatel	Fakulta ekonomicko-správní
http://linked.open...avai/riv/aktivita	P
http://linked.open...avai/riv/aktivity	P(GA13-10331S)
http://linked.open...iv/cisloPeriodika	4
http://linked.open...vai/riv/dodaniDat	2014
http://linked.open...aciTvurceVysledku	Hájek, Petr Olej, Vladimír Myšková, Renáta
http://linked.open.../riv/druhVysledku	J - Článek v odborném periodiku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Univerzita Pardubice / Fakulta ekonomicko-správní
http://linked.open...dnocenehoVysledku	75432
http://linked.open...ai/riv/idVysledku	RIV/00216275:25410/13:39896546
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	stock price; sentiment analysis; prediction; neural networks; forecasting; annual report; support vector regression (en)
http://linked.open.../riv/klicoveSlovo	annual report sentiment analysis neural networks prediction forecasting stock price support vector regression
http://linked.open...odStatuVydavatele	GR - Řecká republika
http://linked.open...ontrolniKodProRIV	[60B2FD525F13]
http://linked.open...i/riv/nazevZdroje	WSEAS Transactions on Business and Economics
http://linked.open...in/vavai/riv/obor	AE
http://linked.open...ichTvurcuVysledku	3 (xsd:int)
http://linked.open...cetTvurcuVysledku	3 (xsd:int)
http://linked.open...vavai/riv/projekt	The role of text information in corporate financial distress prediction models – country-specific and industry-specific approaches
http://linked.open...UplatneniVysledku	2013
http://linked.open...v/svazekPeriodika	10
http://linked.open...iv/tvurceVysledku	Hájek, Petr Myšková, Renáta Olej, Vladimír
issn	1109-9526
number of pages	13 (xsd:int)
http://localhost/t...ganizacniJednotka	25410

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