Computing trading strategies based on financial sentiment data using evolutionary optimization

Ronald Hochreiter ()

Papers from arXiv.org

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.

Date: 2015-04
New Economics Papers: this item is included in nep-cmp
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1504.02972

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