Genetic programming optimization for a sentiment feedback strength based trading strategy

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@Article{journals/ijon/YangMLK17,

• author = "Steve Y. Yang and Sheung Yin Kevin Mo and Anqi Liu and Andrei Kirilenko",
• title = "Genetic programming optimization for a sentiment feedback strength based trading strategy",
• journal = "Neurocomputing",
• year = "2017",
• volume = "264",
• pages = "29--41",
• keywords = "genetic algorithms, genetic programming, News sentiment, Tweet sentiment, financial market, feedback",
• ISSN = "0925-2312",
• bibdate = "2017-09-16",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/ijon/ijon264.html#YangMLK17",
• URL = "https://orca.cardiff.ac.uk/id/eprint/109452/",
• URL = "https://orca.cardiff.ac.uk/id/eprint/109452/1/NEUCOM-D-16-00967-SentimentGP-Revision.pdf",
• DOI = "doi:10.1016/j.neucom.2016.10.103",
• size = "40 pages",
• abstract = "... empirical findings that news and social media Twitter messages (tweets) ... generate superior trading profits. With the trade-off between information speed and its reliability, this study aims to develop an optimal trading strategy using 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 percent Sterling ratio compared with 10.4 percent and 13.6 percent from the technical indicator-based strategies and the basic 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.",

}

Genetic Programming entries for Steve Y Yang Sheung Yin Kevin Mo Anqi Liu Andrei Kirilenko

Citations