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Goldsmiths - University of London

Social Web-based Anxiety Index's Predictive Information on S&P 500 Revisited

Olaniyan, Rapheal; Stamate, Daniel and Logofatu, Doina. 2015. Social Web-based Anxiety Index's Predictive Information on S&P 500 Revisited. In: Alexander Gammerman; Vladimir Vovk and Harris Papadopoulos, eds. Statistical Learning and Data Sciences. 9047 Egham UK: Springer, pp. 203-213. ISBN 978-3-319-17090-9 [Book Section]


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Abstract or Description

There has been an increasing interest recently in examining
the possible relationships between emotions expressed online and stock markets. Most of the previous studies claiming that emotions have predictive influence on the stock market do so by developing various machine learning predictive models, but do not validate their claims rigorously
by analysing the statistical significance of their findings. In turn, the few works that attempt to statistically validate such claims suffer from important limitations of their statistical approaches. In particular, stock market data exhibit erratic volatility, and this time-varying volatility makes any possible relationship between these variables non-linear, which tends to statistically invalidate linear based approaches. Our work tackles this kind of limitations, and extends linear frameworks by proposing a new, non-linear statistical approach that accounts for non-linearity and heteroscedasticity.

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Book Section


Data Science, Machine Learning, Statistical Learning, Financial Forecasting, Sentiment Analysis, Social Web Mining, Monte Carlo Simulation

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15 April 2015Published

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Date Deposited:

05 Apr 2016 07:11

Last Modified:

10 Jul 2018 00:37


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