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A novel cryptocurrency price trend forecasting model based on LightGBM

Author

Listed:
  • Sun, Xiaolei
  • Liu, Mingxi
  • Sima, Zeqian

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.

Suggested Citation

  • Sun, Xiaolei & Liu, Mingxi & Sima, Zeqian, 2020. "A novel cryptocurrency price trend forecasting model based on LightGBM," Finance Research Letters, Elsevier, vol. 32(C).
    • Handle: RePEc:eee:finlet:v:32:y:2020:i:c:s1544612318307918
    DOI: 10.1016/j.frl.2018.12.032
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    References listed on IDEAS

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