Genetic programming optimization for a sentiment feedback strength based trading strategy
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @Article{journals/ijon/YangMLK17,
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author = "Steve Y. Yang and Sheung Yin Kevin Mo and Anqi Liu and
Andrei Kirilenko",
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title = "Genetic programming optimization for a sentiment
feedback strength based trading strategy",
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journal = "Neurocomputing",
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year = "2017",
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volume = "264",
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pages = "29--41",
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keywords = "genetic algorithms, genetic programming, News
sentiment, Tweet sentiment, financial market,
feedback",
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ISSN = "0925-2312",
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bibdate = "2017-09-16",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/ijon/ijon264.html#YangMLK17",
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URL = "https://orca.cardiff.ac.uk/id/eprint/109452/",
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URL = "https://orca.cardiff.ac.uk/id/eprint/109452/1/NEUCOM-D-16-00967-SentimentGP-Revision.pdf",
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DOI = "doi:10.1016/j.neucom.2016.10.103",
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size = "40 pages",
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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