Predicting stock return volatility using sentiment analysis of corporate annual reports

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F21%3A39917733" target="_blank" >RIV/00216275:25410/21:39917733 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.taylorfrancis.com/chapters/edit/10.4324/9781003037903-5/predicting-stock-return-volatility-using-sentiment-analysis-corporate-annual-reports-petr-hajek-renata-myskova-vladimir-olej" target="_blank" >https://www.taylorfrancis.com/chapters/edit/10.4324/9781003037903-5/predicting-stock-return-volatility-using-sentiment-analysis-corporate-annual-reports-petr-hajek-renata-myskova-vladimir-olej</a>

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Predicting stock return volatility using sentiment analysis of corporate annual reports

Popis výsledku v původním jazyce

This chapter focuses on the short-term volatility of corporate stocks and the causes that triggered it, and derives implied volatility based on historical volatility. Chin et al. examined the links among stock market volatility, market sentiment, macroeconomic indicators, and spread volatility over the persisting long-term component and the temporary short-term component. They found no empirical evidence of the link between the volatile component and macroeconomic indicators but found that the intermediate component was linked to variations in market sentiment. The chapter aims to propose a machine learning-based model for predicting short-term stock return volatility and study the effect of annual report filing on abnormal changes in firms’ stock returns using the proposed model. It demonstrates that mining corporate annual reports can be effective in predicting short-term stock return volatility using a three-day event window.

Název v anglickém jazyce

Predicting stock return volatility using sentiment analysis of corporate annual reports

Popis výsledku anglicky

This chapter focuses on the short-term volatility of corporate stocks and the causes that triggered it, and derives implied volatility based on historical volatility. Chin et al. examined the links among stock market volatility, market sentiment, macroeconomic indicators, and spread volatility over the persisting long-term component and the temporary short-term component. They found no empirical evidence of the link between the volatile component and macroeconomic indicators but found that the intermediate component was linked to variations in market sentiment. The chapter aims to propose a machine learning-based model for predicting short-term stock return volatility and study the effect of annual report filing on abnormal changes in firms’ stock returns using the proposed model. It demonstrates that mining corporate annual reports can be effective in predicting short-term stock return volatility using a three-day event window.

Druh

C - Kapitola v odborné knize

CEP obor

—

OECD FORD obor

50206 - Finance

Projekt

<a href="/cs/project/GA19-15498S" target="_blank" >GA19-15498S: Modelování emocí ve verbální a neverbální manažerské komunikaci pro predikci podnikových finančních rizik</a><br>

Návaznosti

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

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

Essentials of Machine Learning in Finance and Accounting

ISBN

978-0-367-48083-7

Počet stran výsledku

21

Strana od-do

75-95

Počet stran knihy

258

Název nakladatele

Taylor &amp; Francis Ltd.

Místo vydání

Abingdon

Kód UT WoS kapitoly

—