Market impact analysis via sentimental transfer learning

Xiaodong Li, Haoran Xie, Tak Lam Wong, Fu Lee Wang

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)

4 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages451-452
Number of pages2
ISBN (Electronic)9781509030156
DOIs
Publication statusPublished - 17 Mar 2017
Externally publishedYes
Event2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017 - Jeju Island, Korea, Republic of
Duration: 13 Feb 201716 Feb 2017

Publication series

Name2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017

Conference

Conference2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017
CountryKorea, Republic of
CityJeju Island
Period13/02/1716/02/17

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Li, X., Xie, H., Wong, T. L., & Wang, F. L. (2017). Market impact analysis via sentimental transfer learning. In 2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017 (pp. 451-452). [7881754] (2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIGCOMP.2017.7881754