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Goldsmiths - University of London

Sentiment and stock market volatility predictive modelling - A hybrid approach

Olaniyan, Rapheal; Stamate, Daniel; Ouarbya, Lahcen and Logofatu, Doina. 2015. Sentiment and stock market volatility predictive modelling - A hybrid approach. In: Eric Gaussier; Longbing Cao; Patrick Gallinari; James Kwok; Gabriela Pasi and Osmar Zaiane, eds. Data Science and Advanced Analytics (DSAA), 2015. 36678 2015. IEEE International Conference on. Paris: IEEE, pp. 1-10. ISBN 978-1-4673-8272-4 [Book Section]

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Abstract or Description

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.

Item Type:

Book Section

Keywords:

behavioural sciences computing;neural nets;sentiment analysis;stock markets;GARCH;artificial neural network framework;behavioural finance;nonparametric nonlinear approach;sentimental contagion;stock market;volatility predictive modelling;Benchmark testing;Electric shock;Monte Carlo methods;Neural networks;Predictive models;Standards;Stock markets;EGARCH;GARCH;Granger causality;Monte Carlo simulations;artificial neural networks;non-parametric test;sentiment;stock market;volatility

Departments, Centres and Research Units:

Computing

Dates:

DateEvent
19 November 2015Published

Item ID:

17664

Date Deposited:

05 Apr 2016 07:30

Last Modified:

09 Jul 2018 14:42

URI:

http://research.gold.ac.uk/id/eprint/17664

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