TY - GEN
T1 - Market impact analysis via sentimental transfer learning
AU - Li, Xiaodong
AU - Xie, Haoran
AU - Wong, Tak Lam
AU - Wang, Fu Lee
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/3/17
Y1 - 2017/3/17
N2 - The problem that how to improve the market impact prediction performances of predictors that are trained based on stocks with few market news is studied in this preliminary work. We propose sentimental transfer learning to transfer the knowledge learned from news-rich stocks that are within the same sector to the news-poor stocks. News articles of both kinds of stocks are mapped into the same feature space that are constructed by sentiment dimensions. New predictors are then trained in the sentimental space in contrast to the traditional ones. Experiments based on the data of Hong Kong stocks are conducted. From the early results, it could be seen that the proposed approach is convincing.
AB - The problem that how to improve the market impact prediction performances of predictors that are trained based on stocks with few market news is studied in this preliminary work. We propose sentimental transfer learning to transfer the knowledge learned from news-rich stocks that are within the same sector to the news-poor stocks. News articles of both kinds of stocks are mapped into the same feature space that are constructed by sentiment dimensions. New predictors are then trained in the sentimental space in contrast to the traditional ones. Experiments based on the data of Hong Kong stocks are conducted. From the early results, it could be seen that the proposed approach is convincing.
UR - http://www.scopus.com/inward/record.url?scp=85017638408&partnerID=8YFLogxK
U2 - 10.1109/BIGCOMP.2017.7881754
DO - 10.1109/BIGCOMP.2017.7881754
M3 - Conference contribution
AN - SCOPUS:85017638408
T3 - 2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017
SP - 451
EP - 452
BT - 2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017
T2 - 2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017
Y2 - 13 February 2017 through 16 February 2017
ER -