Research Article
Stock Volatility Prediction Based on 1D-CNN and LightGBM
@INPROCEEDINGS{10.4108/eai.17-6-2022.2322733, author={Ningyun Dan and Yuxin Li and Zimo Nie and Yuan Li}, title={Stock Volatility Prediction Based on 1D-CNN and LightGBM}, proceedings={Proceedings of the International Conference on Information Economy, Data Modeling and Cloud Computing, ICIDC 2022, 17-19 June 2022, Qingdao, China}, publisher={EAI}, proceedings_a={ICIDC}, year={2022}, month={10}, keywords={stock volatility cnn lightgbm}, doi={10.4108/eai.17-6-2022.2322733} }- Ningyun Dan
Yuxin Li
Zimo Nie
Yuan Li
Year: 2022
Stock Volatility Prediction Based on 1D-CNN and LightGBM
ICIDC
EAI
DOI: 10.4108/eai.17-6-2022.2322733
Abstract
Stock price fluctuations often bring opportunities to investors. Predicting the trend of stock price fluctuation effectively can bring effective and feasible suggestions to investors. This paper uses the real data of the stock market as the data set, and adopts the fusion model based on 1D-CNN model and LightGBM model to predict the stock fluctuations. We first preprocess the original data and extract the important information in the data. Then we train the model and the prediction results are obtained. Experimental results show that the prediction performance of 1D-CNN and LightGBM fusion model is better than that of naive Bayes model and single XGBoost and LightGBM model.
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