TY - GEN
T1 - Entropy based measure sentiment analysis in the financial market
AU - Song, Qiang
AU - Almahdi, Saud
AU - Yang, Steve Y.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - Financial markets are usually affected by the news media which would allow us to consider the news sentiment effect on the market. In this study we propose the sentiment shock and sentiment trend. We provide an entropy based analysis for the two and compare them with the original sentiment score where we quantify the effectiveness of each sentiment in different financial sectors. We show that the sentiment shock and the sentiment trend are more efficient for cyclical sectors and both have a better capability to reduce the noise in the original signal around it's mean. Also, we design a dynamic sentiment shock and trend trading strategy with weekly evaluations where we show that a strategy based on sentiment shock or trend can have a superior performance compared to a buy-and-hold strategy in terms of risk-adjusted return.
AB - Financial markets are usually affected by the news media which would allow us to consider the news sentiment effect on the market. In this study we propose the sentiment shock and sentiment trend. We provide an entropy based analysis for the two and compare them with the original sentiment score where we quantify the effectiveness of each sentiment in different financial sectors. We show that the sentiment shock and the sentiment trend are more efficient for cyclical sectors and both have a better capability to reduce the noise in the original signal around it's mean. Also, we design a dynamic sentiment shock and trend trading strategy with weekly evaluations where we show that a strategy based on sentiment shock or trend can have a superior performance compared to a buy-and-hold strategy in terms of risk-adjusted return.
UR - http://www.scopus.com/inward/record.url?scp=85046128549&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046128549&partnerID=8YFLogxK
U2 - 10.1109/SSCI.2017.8285193
DO - 10.1109/SSCI.2017.8285193
M3 - Conference contribution
AN - SCOPUS:85046128549
T3 - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
SP - 1
EP - 5
BT - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
T2 - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
Y2 - 27 November 2017 through 1 December 2017
ER -