Market impact analysis via sentimental transfer learning

Xiaodong LI, Haoran XIE, Tak Lam WONG, Fu Lee WANG

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

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. Copyright © 2017 IEEE.
Original languageEnglish
Title of host publicationProceedings of 2017 IEEE International Conference on Big Data and Smart Computing (BigComp)
Place of PublicationSouth Korea
PublisherIEEE
Pages451-452
ISBN (Print)9781509030156, 9781509030149
Publication statusPublished - 2017

Fingerprint

Experiments

Citation

Li, X., Xie, H., Wong, T.-L., & Wang, F. L. (2017). Market impact analysis via sentimental transfer learning. In Proceedings of 2017 IEEE International Conference on Big Data and Smart Computing (BigComp) (pp. 451-452). South Korea: IEEE.

Keywords

  • Dictionaries
  • Finance
  • Training
  • Benchmark testing