A Sentiment Analysis Approach to the Prediction of Market Volatility
- URL: http://arxiv.org/abs/2012.05906v1
- Date: Thu, 10 Dec 2020 01:15:48 GMT
- Title: A Sentiment Analysis Approach to the Prediction of Market Volatility
- Authors: Justina Deveikyte, Helyette Geman, Carlo Piccari, Alessandro Provetti
- Abstract summary: We have explored the relationship between sentiment extracted from financial news and tweets and FTSE100 movements.
The sentiment captured from news headlines could be used as a signal to predict market returns; the same does not apply for volatility.
We developed an accurate classifier for the prediction of market volatility in response to the arrival of new information.
- Score: 62.997667081978825
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Prediction and quantification of future volatility and returns play an
important role in financial modelling, both in portfolio optimization and risk
management. Natural language processing today allows to process news and social
media comments to detect signals of investors' confidence. We have explored the
relationship between sentiment extracted from financial news and tweets and
FTSE100 movements. We investigated the strength of the correlation between
sentiment measures on a given day and market volatility and returns observed
the next day. The findings suggest that there is evidence of correlation
between sentiment and stock market movements: the sentiment captured from news
headlines could be used as a signal to predict market returns; the same does
not apply for volatility. Also, in a surprising finding, for the sentiment
found in Twitter comments we obtained a correlation coefficient of -0.7, and
p-value below 0.05, which indicates a strong negative correlation between
positive sentiment captured from the tweets on a given day and the volatility
observed the next day. We developed an accurate classifier for the prediction
of market volatility in response to the arrival of new information by deploying
topic modelling, based on Latent Dirichlet Allocation, to extract feature
vectors from a collection of tweets and financial news. The obtained features
were used as additional input to the classifier. Thanks to the combination of
sentiment and topic modelling our classifier achieved a directional prediction
accuracy for volatility of 63%.
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