Computing trading strategies based on financial sentiment data using evolutionary optimization

TL;DR

This paper employs evolutionary optimization to develop rule-based trading strategies using sentiment data from StockTwits, demonstrating their effectiveness across DJIA stocks and comparing them to traditional portfolio methods.

Contribution

It introduces a novel approach combining sentiment analysis with evolutionary algorithms to optimize trading strategies based on social media data.

Findings

Strategies outperform classical methods in certain stocks

Sentiment data improves trading decision accuracy

Evolutionary optimization effectively adapts to market sentiment

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.