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Computing Trading Strategies Based on Financial Sentiment Data Using Evolutionary Optimization

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Mendel 2015 (ICSC-MENDEL 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 378))

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Abstract

In this paper we apply evolutionary optimization techniques to compute optimal rule-based trading strategies based on financial sentiment data. The sentiment data was extracted from the social media service StockTwits to accommodate the level of bullishness or bearishness of the online trading community towards certain stocks. Numerical results for all stocks from the Dow Jones Industrial Average (DJIA) index are presented and a comparison to classical risk-return portfolio selection is provided.

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Notes

  1. 1.

    http://www.psychsignal.com/.

  2. 2.

    http://www.liwc.net/.

  3. 3.

    http://www.stocktwits.com/.

  4. 4.

    http://www.quandl.com/.

  5. 5.

    Performance graphs are generated using the PerformanceAnalytics R package [22].

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Author information

Authors and Affiliations

  1. Department of Finance, Accounting and Statistics, WU Vienna University of Economics and Business, Wien, Austria

    Ronald Hochreiter

Authors
  1. Ronald Hochreiter
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Correspondence to Ronald Hochreiter .

Editor information

Editors and Affiliations

  1. Faculty of Mechanical Engineering, Department of Applied Computer Science, Brno University of Technology, Brno, Czech Republic

    Radek Matoušek

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Hochreiter, R. (2015). Computing Trading Strategies Based on Financial Sentiment Data Using Evolutionary Optimization. In: Matoušek, R. (eds) Mendel 2015. ICSC-MENDEL 2016. Advances in Intelligent Systems and Computing, vol 378. Springer, Cham. https://doi.org/10.1007/978-3-319-19824-8_15

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  • DOI: https://doi.org/10.1007/978-3-319-19824-8_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19823-1

  • Online ISBN: 978-3-319-19824-8

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