A novel cryptocurrency price trend forecasting model based on LightGBM
文献类型：期刊论文
| 作者 | Sun Xiaolei; Liu Mingxi; Sima Zeqian |
| 刊名 | FINANCE RESEARCH LETTERS
 |
| 出版日期 | 2020 |
| 卷号 | 32 |
| DOI | 10.1016/j.frl.2018.12.032 |
| 英文摘要 | Forecasting cryptocurrency prices is crucial for investors. In this paper, we adopt a novel Gradient Boosting Decision Tree (GBDT) algorithm, Light Gradient Boosting Machine (LightGBM), to forecast the price trend (falling, or not falling) of cryptocurrency market. In order to utilize market information, we combine the daily data of 42 kinds of primary cryptocurrencies with key economic indicators. Results show that the robustness of the LightGBM model is better than the other methods, and the comprehensive strength of the cryptocurrencies impacts the forecasting performance. This can effectively guide investors in constructing an appropriate cryptocurrency portfolio and mitigate risks. |
| 语种 | 英语 |
| 源URL | [http://ir.casisd.cn/handle/190111/9774]  |
| 专题 | 中国科学院科技战略咨询研究院 |
| 推荐引用方式 GB/T 7714 | Sun Xiaolei,Liu Mingxi,Sima Zeqian. A novel cryptocurrency price trend forecasting model based on LightGBM[J]. FINANCE RESEARCH LETTERS,2020,32. |
| APA | Sun Xiaolei,Liu Mingxi,&Sima Zeqian.(2020).A novel cryptocurrency price trend forecasting model based on LightGBM.FINANCE RESEARCH LETTERS,32. |
| MLA | Sun Xiaolei,et al."A novel cryptocurrency price trend forecasting model based on LightGBM".FINANCE RESEARCH LETTERS 32(2020). |
入库方式： OAI收割
来源：科技战略咨询研究院
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