Computing trading strategies based on financial sentiment data using evolutionary optimization
This work addresses the challenge of improving trading strategies for investors using sentiment data, but it appears incremental as it applies existing evolutionary techniques to a specific financial dataset.
The paper tackled the problem of computing optimal trading strategies by applying evolutionary optimization to financial sentiment data from StockTwits, achieving numerical results for all DJIA stocks and comparing them to classical portfolio selection methods.
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.