Skip to content

Using Stock Prices as Ground Truth in Sentiment Analysis to Generate Profitable Trading Signals

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publication2018 IEEE Symposium Series on Computational Intelligence (SSCI 2018)
Subtitle of host publicationProceedings of a meeting held 18-21 November 2018, Bangalore, India.
EditorsSuresh Sundaram
Publisher or commissioning bodyInstitute of Electrical and Electronics Engineers (IEEE)
Pages1868-1875
Number of pages8
ISBN (Electronic)9781538692769
ISBN (Print)9781538692776
DOIs
DateAccepted/In press - 1 Sep 2018
DateE-pub ahead of print - 18 Nov 2018
DatePublished (current) - 28 Jan 2019
Event8th IEEE Symposium Series on Computational Intelligence, SSCI 2018 - Bangalore, India
Duration: 18 Nov 201821 Nov 2018

Conference

Conference8th IEEE Symposium Series on Computational Intelligence, SSCI 2018
CountryIndia
CityBangalore
Period18/11/1821/11/18

Abstract

The increasing availability of “big” (large volume) social media data has motivated a great deal of research in applying sentiment analysis to predict the movement of prices within financial markets. Previous work in this field investigates how the true sentiment of text (i.e. positive or negative opinions) can be used for financial predictions, based on the assumption that sentiments expressed online are representative of the true market sentiment. Here we consider the converse idea, that using the stock price as the ground-truth in the system may be a better indication of sentiment. Tweets are labelled as Buy or Sell dependent on whether the stock price discussed rose or fell over the following hour, and from this, stock-specific dictionaries are built for individual companies. A Bayesian classifier is used to generate stock predictions, which are input to an automated trading algorithm. Placing 468 trades over a 1 month period yields a return rate of 5.18%, which annualises to approximately 83% per annum. This approach performs significantly better than random chance and outperforms two baseline sentiment analysis methods tested.

    Research areas

  • Automated Trading, Financial Engineering, Financial Markets, Machine Learning, Sentiment Analysis

Event

8th IEEE Symposium Series on Computational Intelligence, SSCI 2018

Duration18 Nov 201821 Nov 2018
CityBangalore
CountryIndia

Event: Conference

Download statistics

No data available

Documents

Documents

  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the accepted author manuscript (AAM). The final published version (version of record) is available online via IEEE at https://doi.org/10.1109/SSCI.2018.8628841 . Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 503 KB, PDF document

    Licence: Other

DOI

View research connections

Related faculties, schools or groups