Name
Abstract | ||
|---|---|---|
As an increasing number of investors post their opinions or show their decisions on public platforms, a critical challenge is to make trading decisions by considering opinions from online investors. In this paper, by taking the real-world data from Stocktwits as an example, we develop FollowAKOInvestor, a systematic two-step framework to utilize sentiments from various kinds of investors to forecast stocks. First, FollowAKOInvestor divides investors into various groups according to their expertise levels and extract sentiments from different investor groups as features. Then, it uses machine learning techniques to make trading decisions by learning a prediction function to appropriately combine sentiments from different kinds of investors. The intuition of FollowAKOInvestor is that sentiments extracted from all kinds of investors (including experts and non-experts) can help us invest in stocks. Extensive data analysis and experiments show that FollowAKOInvestor generates high-yield portfolios. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICTAI50040.2020.00137 | 2020 IEEE 32ND INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI) |
Keywords | DocType | ISSN |
Stock Prediction, Machine Learning, Fintech | Conference | 1082-3409 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Chang | 1 | 0 | 0.34 |
| Yujie Ding | 2 | 0 | 0.34 |
| Wenting Tu | 3 | 85 | 9.48 |