Neural Network-Based Financial Volatility Forecasting: A Systematic Review

: Ge Wenbo, Lalbakhsh Pooia, Isai Leigh, Lenskiy Artem, Suominen Hanna

Publisher: ASSOC COMPUTING MACHINERY

: 2023

: ACM Computing Surveys

: ACM COMPUTING SURVEYS

: ACM COMPUT SURV

: 14

: 55

: 1

: 30

: 0360-0300

: 1557-7341

DOI: https://doi.org/10.1145/3483596

: https://dl.acm.org/doi/10.1145/3483596

Volatility forecasting is an important aspect of finance as it dictates many decisions of market players. A snapshot of state-of-the-art neural network-based financial volatility forecasting was generated by examining 35 studies, published after 2015. Several issues were identified, such as the inability for easy and meaningful comparisons, and the large gap between modern machine learning models and those applied to volatility forecasting. A shared task was proposed to evaluate state-of-the-art models, and several promising ways to bridge the gap were suggested. Finally, adequate background was provided to serve as an introduction to the field of neural network volatility forecasting.