The Comparison of LSTM, LGBM, and CNN in Stock Volatility Prediction

DOI

10.2991/aebmr.k.220307.147How to use a DOI?

Keywords

Stock price volatility; Volatility prediction; Machine learning

Abstract

In financial markets, volatility reflects the magnitude of price fluctuations. Forecasting volatility will be an important measure of the future direction of the market. Measuring and prdedicting stock market volatility has received increasing attention from academics and the industry over the past few years. This paper will focus on predicting the actual volatility of stocks using CNN, LightGBM, and LSTM models, using a data-set from Kaggle to make predictions. The paper gives a throughout analysis of the comparison for the performance of the three models. After testing with the chosen dataset, it was found that LGBM is more suitable for the task of predicting short-term stock volatility.

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  - Jiabao Li
PY  - 2022
DA  - 2022/03/26
TI  - The Comparison of LSTM, LGBM, and CNN in Stock Volatility Prediction
BT  - Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022)
PB  - Atlantis Press
SP  - 905
EP  - 909
SN  - 2352-5428
UR  - https://doi.org/10.2991/aebmr.k.220307.147
DO  - 10.2991/aebmr.k.220307.147
ID  - Li2022
ER  -