PREDICTING ABNORMAL STOCK RETURN VOLATILITY USING TEXTUAL ANALYSIS OF NEWS - A META-LEARNING APPROACH

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F18%3A39913349" target="_blank" >RIV/00216275:25410/18:39913349 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.amfiteatrueconomic.ro/ArticolEN.aspx?CodArticol=2703" target="_blank" >http://www.amfiteatrueconomic.ro/ArticolEN.aspx?CodArticol=2703</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.24818/EA/2018/47/185" target="_blank" >10.24818/EA/2018/47/185</a>

Jazyk výsledku

angličtina

Název v původním jazyce

PREDICTING ABNORMAL STOCK RETURN VOLATILITY USING TEXTUAL ANALYSIS OF NEWS - A META-LEARNING APPROACH

Popis výsledku v původním jazyce

Textual analysis of news articles is increasingly important in predicting stock prices. Previous research has intensively utilized the textual analysis of news and other firm-related documents in volatility prediction models. It has been demonstrated that the news may be related to abnormal stock price behavior subsequent to their dissemination. However, previous studies to date have tended to focus on linear regression methods in predicting volatility. Here, we show that non-linear models can be effectively employed to explain the residual variance of the stock price. Moreover, we use meta-learning approach to simulate the decision-making process of various investors. The results suggest that this approach significantly improves the prediction accuracy of abnormal stock return volatility. The fact that the length of news articles is more important than news sentiment in predicting stock return volatility is another important finding. Notably, we show that Rotation forest performs particularly well in terms of both the accuracy of abnormal stock return volatility and the performance on imbalanced volatility data.

Název v anglickém jazyce

PREDICTING ABNORMAL STOCK RETURN VOLATILITY USING TEXTUAL ANALYSIS OF NEWS - A META-LEARNING APPROACH

Popis výsledku anglicky

Textual analysis of news articles is increasingly important in predicting stock prices. Previous research has intensively utilized the textual analysis of news and other firm-related documents in volatility prediction models. It has been demonstrated that the news may be related to abnormal stock price behavior subsequent to their dissemination. However, previous studies to date have tended to focus on linear regression methods in predicting volatility. Here, we show that non-linear models can be effectively employed to explain the residual variance of the stock price. Moreover, we use meta-learning approach to simulate the decision-making process of various investors. The results suggest that this approach significantly improves the prediction accuracy of abnormal stock return volatility. The fact that the length of news articles is more important than news sentiment in predicting stock return volatility is another important finding. Notably, we show that Rotation forest performs particularly well in terms of both the accuracy of abnormal stock return volatility and the performance on imbalanced volatility data.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50204 - Business and management

Projekt

<a href="/cs/project/GA16-19590S" target="_blank" >GA16-19590S: Analýza témat a sentimentu vícenásobných textových zdrojů pro finanční rozhodování podniků</a><br>

Návaznosti

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

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

Amfiteatru Economic

ISSN

1582-9146

e-ISSN

—

Svazek periodika

20

Číslo periodika v rámci svazku

47

Stát vydavatele periodika

RO - Rumunsko

Počet stran výsledku

17

Strana od-do

185-201

Kód UT WoS článku

000427829800012

EID výsledku v databázi Scopus

2-s2.0-85041616998