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 language | English |
|---|---|
| Title of host publication | Proceedings of 2017 IEEE International Conference on Big Data and Smart Computing (BigComp) |
| Place of Publication | South Korea |
| Publisher | IEEE |
| Pages | 451-452 |
| ISBN (Print) | 9781509030156, 9781509030149 |
| DOIs | |
| Publication status | Published - 2017 |
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