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Genetic programming optimization for a sentiment feedback strength based trading strategy

Yang, Steve Y., Mo, Sheung Yin Kevin, Liu, Anqi and Kirilenko, Andrei A. 2017. Genetic programming optimization for a sentiment feedback strength based trading strategy. Neurocomputing 264 , pp. 29-41. 10.1016/j.neucom.2016.10.103

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Abstract

This study is motivated by the empirical findings that news and social me- dia Twitter messages (tweets) exhibit persistent predictive power on financial market movement. Based on the evidence that tweets are faster than news in revealing new market information, whereas news is regarded broadly a more reliable source of information than tweets, we propose a superior trading strat- egy based on the sentiment feedback strength between the news and tweets using generic programming optimization method. The key intuition behind this feedback strength based approach is that the joint momentum of the two sentiment series leads to significant market signals, which can be exploited to generate superior trading profits. With the trade-off between information speed and its reliability, this study aims to develop an optimal trading strategy us- ing investors' sentiment feedback strength with the objective to maximize risk adjusted return measured by the Sterling ratio. We find that the sentiment feed- back based strategies yield superior market returns with low maximum draw- down over the period from 2012 to 2015. In comparison, the strategies based on the sentiment feedback indicator generate over 14.7% Sterling ratio compared with 10.4% and 13.6% from the technical indicator-based strategies and the ba- sic buy-and-hold strategy respectively. After considering transaction costs, the sentiment indicator based strategy outperforms the technical indicator based strategy consistently. Backtesting shows that the advantage is statistically significant. The result suggests that the sentiment feedback indicator provides support in controlling loss with lower maximum drawdown.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Mathematics
Publisher: Elsevier
ISSN: 0925-2312
Date of First Compliant Deposit: 26 February 2018
Date of Acceptance: 16 October 2016
Last Modified: 24 Jul 2018 13:11
URI: http://orca.cf.ac.uk/id/eprint/109452

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