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.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
Performance graphs are generated using the PerformanceAnalytics R package [22].
References
Feldman, R.: Techniques and applications for sentiment analysis. Commun. ACM 56(4), 82–89 (2013)
Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1–8 (2011)
Oliveira, N., Cortez, P., Areal, N.: On the predictability of stock market behavior using StockTwits sentiment and posting volume. Lect. Notes Comput. Sci. 8154, 355–365 (2013)
Smailović, J., Grčar, M., Lavrač, N., Žnidaršič, M.: Stream-based active learning for sentiment analysis in the financial domain. Inf. Sci. 285, 181–203 (2014)
Markowitz, H.: Portfolio selection. J. Financ. 7(1), 77–91 (1952)
Gottschalk, L.A., Gleser, G.C.: Measurement of Psychological States Through the Content Analysis of Verbal Behaviour. University of California Press (1969)
Surowiecki, J.: The Wisdom of Crowds. Anchor Books (2005)
Das, S.R., Chen, M.Y.: Yahoo! for Amazon: sentiment extraction from small talk on the web. Manage. Sci. 53(9), 1375–1388 (2007)
Tumarkin, R., Whitelaw, R.F.: News or noise? Internet postings and stock prices. Financ. Anal. J. 57(3), 41–51 (2001)
Oliveira, N., Cortez, P., Areal, N.: Automatic creation of stock market lexicons for sentiment analysis using StockTwits data. In: Proceedings of the 18th International Database Engineering & Applications Symposium, ACM, pp. 115–123 (2014)
Brabazon, A., O’Neill, M. (eds.): Natural computing in computational finance. Volume 100 of Studies in Computational Intelligence. Springer (2008)
Brabazon, A., O’Neill, M. (eds.): Natural computing in computational finance, volume 2. Volume 185 of Studies in Computational Intelligence. Springer (2009)
Brabazon, A., O’Neill, M., Maringer, D. (eds.): Natural computing in computational finance, volume 3. Volume 293 of Studies in Computational Intelligence. Springer (2010)
Bradley, R.G., Brabazon, A., O’Neill, M.: Evolving trading rule-based policies. Lect. Notes Comput. Sci. 6025, 251–260 (2010)
Brabazon, A., O’Neill, M.: Evolving technical trading rules for spot foreign-exchange markets using grammatical evolution. Comput. Manage. Sci. 1(3–4), 311–327 (2004)
Brabazon, A., O’Neill, M.: Intra-day trading using grammatical evolution. In: Brabazon, A., O’Neill, M. (eds.) Biologically Inspired Algorithms for Financial Modelling, pp. 203–210. Springer (2006)
Lipinski, P., Korczak, J.J.: Performance measures in an evolutionary stock trading expert system. Lect. Notes Comput. Sci. 3039, 835–842 (2004)
Sharpe, W.F.: The sharpe ratio. J. Portfolio Manage. 21(1), 49–58 (1994)
Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput. Surv. 35(3), 268–308 (2003)
R Core Team: R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2014)
DeMiguel, V., Garlappi, L., Uppal, R.: Optimal versus naive diversification: How inefficient is the 1/n portfolio strategy? Rev. Financ. Stud. 22(5), 1915–1953 (2009)
Peterson, B.G., Carl, P.: PerformanceAnalytics: econometric tools for performance and risk analysis. R package version 1.4.3541 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-19824-8_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19823-1
Online ISBN: 978-3-319-19824-8
eBook Packages: EngineeringEngineering (R0)