A Model Combining LightGBM and Neural Network for High-frequency Realized Volatility Forecasting

DOI

10.2991/aebmr.k.220307.473How to use a DOI?

Keywords

Realized volatility forecasting; Ensemble learning model; LightGBM; Neural network

Abstract

The financial market is a nonlinear and frequently changing complex dynamic. Volatility, as one of the important indicators to measure the return of financial assets, occupies an indispensable position in the field of financial measurement. With the development of machine learning and massive data technology, there is an increasing demand for volatility prediction. In this paper, an ensemble learning model mainly based on the LightGBM algorithm and supplemented with a neural network is constructed. The model achieves the prediction of high-frequency realized volatility using ultra-high frequency stock market data and through the method of moving windows in finance. The superiority of the LightGBM-NN model is verified by comparing it with the single LightGBM model. The LightGBM-NN model produces less error and has higher accuracy, precision, and F1 score. The lightGBM-NN model has advanced the application of LightGBM in the field of financial measurement, which brings new ideas on how to handle the massive data efficiently and fast in the stock market.

Copyright

© 2022 The Authors. Published by Atlantis Press International B.V.

Open Access

This is an open access article under the CC BY-NC license.

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TY  - CONF
AU  - Xiang Zhang
PY  - 2022
DA  - 2022/03/26
TI  - A Model Combining LightGBM and Neural Network for High-frequency Realized Volatility Forecasting
BT  - Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022)
PB  - Atlantis Press
SP  - 2906
EP  - 2912
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.220307.473
DO  - 10.2991/aebmr.k.220307.473
ID  - Zhang2022
ER  -