NDLI: Sentiment and stock market volatility predictive modelling

Content Provider	IEEE Xplore Digital Library
Author	Olaniyan, R. Stamate, D. Ouarbya, L. Logofatu, D.
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Sci., Frankfurt Univ. of Appl. Sci., Frankfurt, Germany (Logofatu, D.) \|\| Dept. of Comput., Univ. of London, London, UK (Olaniyan, R.; Stamate, D.; Ouarbya, L.)
Abstract	The frequent ups and downs are characteristic to the stock market. The conventional standard models that assume that investors act rationally have not been able to capture the irregularities in the stock market patterns for years. As a result, behavioural finance is embraced to attempt to correct these model shortcomings by adding some factors to capture sentimental contagion which may be at play in determining the stock market. This paper assesses the predictive influence of sentiment on the stock market returns by using a non-parametric nonlinear approach that corrects specific limitations encountered in previous related work. In addition, the paper proposes a new approach to developing stock market volatility predictive models by incorporating a hybrid GARCH and artificial neural network framework, and proves the advantage of this framework over a GARCH only based framework. Our results reveal also that past volatility and positive sentiment appear to have strong predictive power over future volatility.
Sponsorship	IEEE Comput. Intell. Soc.
Starting Page	1
Ending Page	10
File Size	313306
Page Count	10
File Format	PDF
ISBN	9781467382724
DOI	10.1109/DSAA.2015.7344855
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-19
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Electric shock Volatility Artificial neural networks Predictive models Standards Sentiment Monte Carlo methods Monte Carlo simulations Neural networks Non-parametric test Benchmark testing GARCH Stock market Stock markets EGARCH Granger causality
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

ARTIFICIAL NEURAL NETWORKS - AN APPLICATION TO STOCK MARKET VOLATILITY

An empirical examination of investor sentiment and stock market volatility: evidence from India

Forecasting Chinese Stock Markets Volatility Based on Neural Network Combining

The Empirical Investigation of Relationship between Return, Volume and Volatility Dynamics in Indian Stock Market

Empirical Evidence on the Relationship between Stock Market Volatility and Macroeconomics Volatility in

Stock Index Forecasting for Vietnam's Stock Market

The Granger causality effect between the stock market and exchange rate volatility in the ASEAN 5 countries

COVID-19 Pandemic and Romanian Stock Market Volatility: A GARCH Approach

Dynamic Linkages of Exchange Rate and Stock Return

Sentiment and stock market volatility predictive modelling — A hybrid approach

Similar Documents

ARTIFICIAL NEURAL NETWORKS - AN APPLICATION TO STOCK MARKET VOLATILITY

An empirical examination of investor sentiment and stock market volatility: evidence from India

Forecasting Chinese Stock Markets Volatility Based on Neural Network Combining

The Empirical Investigation of Relationship between Return, Volume and Volatility Dynamics in Indian Stock Market

Empirical Evidence on the Relationship between Stock Market Volatility and Macroeconomics Volatility in

Stock Index Forecasting for Vietnam's Stock Market

The Granger causality effect between the stock market and exchange rate volatility in the ASEAN 5 countries

COVID-19 Pandemic and Romanian Stock Market Volatility: A GARCH Approach

Dynamic Linkages of Exchange Rate and Stock Return

Sentiment and stock market volatility predictive modelling — A hybrid approach