A novel cryptocurrency price trend forecasting model based on LightGBM

【Author】 Sun Xiaolei; Liu Mingxi; Sima Zeqian

【Source】FINANCE RESEARCH LETTERS

【影响因子】9.848

【Abstract】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.

【Keywords】Cryptocurrency; Trend forecasting; LightGBM; Forecasting performance

【发表时间】2020 JAN

【收录时间】2022-01-02

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