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Computing Trading Strategies Based on Financial Sentiment Data Using Evolutionary Optimization

  • Ronald Hochreiter
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 378)

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

Evolutionary optimization Sentiment analysis Technical trading Portfolio optimization 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Finance, Accounting and StatisticsWU Vienna University of Economics and BusinessWienAustria

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