Name
Abstract | ||
|---|---|---|
News from traditional media has been used to facilitate the prediction of stock movement for a long time. However, in recent times, online social networks (OSN) have played an increasing significant role as a platform for information sharing. News content posted on these OSN provides very useful insight about public moods. In this paper, we carefully select official accounts from China’s largest online social networks — Sina Weibo and analyze the news content crawled from these accounts by extracting sentiment features and Latent Dirichlet allocation (LDA) features. We then input these features together with technical indicators into a novel hybrid model called RNN-boost to predict the stock volatility in the Chinese stock market. The Shanghai-Shenzhen 300 Stock Index (HS300) is the use case for this research. Experimental results show that our model outperforms other prevalent methods and can achieve a good prediction performance. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.datak.2018.08.003 | Data & Knowledge Engineering |
Keywords | Field | DocType |
Recurrent neural networks,Adaboost,Time series prediction,Online social networks | Latent Dirichlet allocation,Social media,Social network,Information retrieval,Stock market index,Computer science,Volatility (finance),Stock market,Information sharing | Journal |
Volume | Issue | ISSN |
118 | 1 | 0169-023X |
Citations | PageRank | References |
3 | 0.37 | 11 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weiling Chen | 1 | 11 | 1.65 |
| Chai Kiat Yeo | 2 | 24 | 4.94 |
| Chiew Tong Lau | 3 | 406 | 35.82 |
| Bu Sung Lee | 4 | 452 | 35.22 |