The Comparison of LSTM, LGBM, and CNN in Stock Volatility Prediction
Authors
Jiabao Li1, *
1Department of Engineering and Physical Sciences, School of Computing, The University of Leeds, Leeds, The United Kingdom
*Corresponding author. Email: ml2022jl@leeds.ac.uk
Corresponding Author
Jiabao Li
Available Online 26 March 2022.
- 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.
Cite this article
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 -