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Academic Journal of Business & Management, 2022, 4(4); doi: 10.25236/AJBM.2022.040419.

Predictive Analysis of Cryptocurrency Residual Log-returns Based on Lightgbm


Zenan Ji1, Yihong Zhang2

Corresponding Author:
Zenan Ji

1School of Data Science, Zhejiang University of Finance & Economics, Hangzhou, 310018, China

2School of Science, North China Institute of Science & Technology, Langfang, 065201, China


Cryptocurrencies have taken an increasingly important place in the international financial markets. However, due to the complexity of the factors affecting cryptocurrencies and the high volatility of cryptocurrency prices, predicting the future trend of cryptocurrencies has been a difficult topic. In this paper, we establish a prediction model based on LightGBM, aiming to find the optimal machine learning algorithm to predict different cryptocurrencies. Firstly, we preprocessed the data to ensure data integrity and availability. Secondly, we trained the training set using the LighGBM algorithm. Then, to find the optimal LightGBM algorithm for different cryptocurrencies, we optimized the algorithm using Grid Search. Finally, we evaluated the results by Root Mean Squared Error (RMSE). It shows that the Root Mean Squared Error of the prediction results for all cryptocurrencies is below 8%, which means LightGBM can be a good choice for monitoring cryptocurrencies.


Cryptocurrency; Residual log-returns; LightGBM; Grid Search

Cite This Paper

Zenan Ji, Yihong Zhang. Predictive Analysis of Cryptocurrency Residual Log-returns Based on Lightgbm. Academic Journal of Business & Management (2022) Vol. 4, Issue 4: 96-100. https://doi.org/10.25236/AJBM.2022.040419.


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