A novel cryptocurrency price trend forecasting model based on LightGBM

Xiaolei Sun, Mingxi Liu and Zeqian Sima

Finance Research Letters, 2020, vol. 32, issue C

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 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (61)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:32:y:2020:i:c:s1544612318307918

DOI: 10.1016/j.frl.2018.12.032

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