An investigation into correlations between financial sentiment and prices in financial markets
An investigation into correlations between financial sentiment and prices in financial markets
There is now a small but growing literature showing some relationship between sentiment contained within blogs, online news article and message boards and price movements in financial markets. Typically, researchers use keyword searching to find financially relevant messages, then rate them in terms of their how positive or negative the sentiment they contain is in relation to prices.
Through an exploratory analysis of the statistical nature of word frequency movements on Twitter, we highlight some issues with this approach and define how a sentiment variable could be constructed to generate well specified linear regression models.
We then address a second issue of how to model time. Current research has used units of a day or week for both sentiment and price series. There is no discussion in the literature in this area as to what the best unit of time might be, or indeed, if there is a weekly topology to sentiment price correlations. We present two models which explore how these factors affect sentiment-price correlations.
Finally we present results correlating financial sentiment on Twitter to the price of the Standard and Poor's Index of 500 Leading Shares. We report both contemporaneous (R squared values up to 0.35) and predictive correlations (R squared values up to 0.27) between our sentiment metric and prices. Scale and weekly topology both appear significant factors that would benefit inclusion in future models.
978-1-4503-1889-1
99-108
Association for Computing Machinery
Gaskell, Paul
9d855bff-86a2-4995-965a-3cbd968c5b8a
McGroarty, Frank
693a5396-8e01-4d68-8973-d74184c03072
Tiropanis, Thanassis
d06654bd-5513-407b-9acd-6f9b9c5009d8
May 2013
Gaskell, Paul
9d855bff-86a2-4995-965a-3cbd968c5b8a
McGroarty, Frank
693a5396-8e01-4d68-8973-d74184c03072
Tiropanis, Thanassis
d06654bd-5513-407b-9acd-6f9b9c5009d8
Gaskell, Paul, McGroarty, Frank and Tiropanis, Thanassis
(2013)
An investigation into correlations between financial sentiment and prices in financial markets.
In WebSci '13 Proceedings of the 5th Annual ACM Web Science Conference.
Association for Computing Machinery.
.
(doi:10.1145/2464464.2464510).
Record type:
Conference or Workshop Item
(Paper)
Abstract
There is now a small but growing literature showing some relationship between sentiment contained within blogs, online news article and message boards and price movements in financial markets. Typically, researchers use keyword searching to find financially relevant messages, then rate them in terms of their how positive or negative the sentiment they contain is in relation to prices.
Through an exploratory analysis of the statistical nature of word frequency movements on Twitter, we highlight some issues with this approach and define how a sentiment variable could be constructed to generate well specified linear regression models.
We then address a second issue of how to model time. Current research has used units of a day or week for both sentiment and price series. There is no discussion in the literature in this area as to what the best unit of time might be, or indeed, if there is a weekly topology to sentiment price correlations. We present two models which explore how these factors affect sentiment-price correlations.
Finally we present results correlating financial sentiment on Twitter to the price of the Standard and Poor's Index of 500 Leading Shares. We report both contemporaneous (R squared values up to 0.35) and predictive correlations (R squared values up to 0.27) between our sentiment metric and prices. Scale and weekly topology both appear significant factors that would benefit inclusion in future models.
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e-pub ahead of print date: 1 January 2013
Published date: May 2013
Venue - Dates:
Proceedings of the 5th Annual ACM Web Science Conference, Paris, France, 2013-05-02 - 2013-05-04
Organisations:
Centre for Digital, Interactive & Data Driven Marketing
Identifiers
Local EPrints ID: 356113
URI: http://eprints.soton.ac.uk/id/eprint/356113
ISBN: 978-1-4503-1889-1
PURE UUID: 875a31de-953e-4f2b-979d-b59cbf5338e3
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Date deposited: 10 Sep 2013 10:59
Last modified: 16 Mar 2024 03:57
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Contributors
Author:
Paul Gaskell
Author:
Frank McGroarty
Author:
Thanassis Tiropanis
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