TY - JOUR
T1 - Genetic programming optimization for a sentiment feedback strength based trading strategy
AU - Yang, Steve Y.
AU - Mo, Sheung Yin Kevin
AU - Liu, Anqi
AU - Kirilenko, Andrei A.
N1 - Publisher Copyright:
© 2017 Elsevier B.V.
PY - 2017/11/15
Y1 - 2017/11/15
N2 - This study is motivated by the empirical findings that news and social media 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 strategy 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 using investors’ sentiment feedback strength with the objective to maximize risk adjusted return measured by the Sterling ratio. We find that the sentiment feedback based strategies yield superior market returns with low maximum drawdown 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 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.
AB - This study is motivated by the empirical findings that news and social media 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 strategy 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 using investors’ sentiment feedback strength with the objective to maximize risk adjusted return measured by the Sterling ratio. We find that the sentiment feedback based strategies yield superior market returns with low maximum drawdown 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 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.
KW - Feedback
KW - Financial market
KW - Genetic programming
KW - News sentiment
KW - Tweet sentiment
UR - http://www.scopus.com/inward/record.url?scp=85021763905&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85021763905&partnerID=8YFLogxK
U2 - 10.1016/j.neucom.2016.10.103
DO - 10.1016/j.neucom.2016.10.103
M3 - Article
AN - SCOPUS:85021763905
SN - 0925-2312
VL - 264
SP - 29
EP - 41
JO - Neurocomputing
JF - Neurocomputing
ER -