Forecasting corporate financial performance using sentiment in annual reports for stakeholders' decision-making

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F14%3A39898554" target="_blank" >RIV/00216275:25410/14:39898554 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.3846/20294913.2014.979456" target="_blank" >http://dx.doi.org/10.3846/20294913.2014.979456</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3846/20294913.2014.979456" target="_blank" >10.3846/20294913.2014.979456</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Forecasting corporate financial performance using sentiment in annual reports for stakeholders' decision-making

Popis výsledku v původním jazyce

This paper is aimed at examining the role of annual reports' sentiment in forecasting financial performance. The sentiment (tone, opinion) is assessed using several categorization schemes in order to explore various aspects of language used in the annualreports of U.S. companies. Further, we employ machine learning methods and neural networks to predict financial performance expressed in terms of the Z-score bankruptcy model. Eleven categories of sentiment (ranging from negative and positive to activeand common) are used as the inputs of the prediction models. Support vector machines provide the highest forecasting accuracy. This evidence suggests that there exist non-linear relationships between the sentiment and financial performance. The results indicate that the sentiment information is an important forecasting determinant of financial performance and, thus, can be used to support decision-making process of corporate stakeholders.

Název v anglickém jazyce

Forecasting corporate financial performance using sentiment in annual reports for stakeholders' decision-making

Popis výsledku anglicky

This paper is aimed at examining the role of annual reports' sentiment in forecasting financial performance. The sentiment (tone, opinion) is assessed using several categorization schemes in order to explore various aspects of language used in the annualreports of U.S. companies. Further, we employ machine learning methods and neural networks to predict financial performance expressed in terms of the Z-score bankruptcy model. Eleven categories of sentiment (ranging from negative and positive to activeand common) are used as the inputs of the prediction models. Support vector machines provide the highest forecasting accuracy. This evidence suggests that there exist non-linear relationships between the sentiment and financial performance. The results indicate that the sentiment information is an important forecasting determinant of financial performance and, thus, can be used to support decision-making process of corporate stakeholders.

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

2014

Kód důvěrnosti údajů

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

Název periodika

Technological and Economic Development of Economy

ISSN

2029-4913

e-ISSN

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Svazek periodika

20

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

LT - Litevská republika

Počet stran výsledku

18

Strana od-do

721-738

Kód UT WoS článku

000346354400006

EID výsledku v databázi Scopus

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