A2 Refereed review article in a scientific journal
Neural Network-Based Financial Volatility Forecasting: A Systematic Review
Authors: Ge Wenbo, Lalbakhsh Pooia, Isai Leigh, Lenskiy Artem, Suominen Hanna
Publisher: ASSOC COMPUTING MACHINERY
Publication year: 2023
Journal: ACM Computing Surveys
Journal name in source: ACM COMPUTING SURVEYS
Journal acronym: ACM COMPUT SURV
Article number: 14
Volume: 55
Issue: 1
First page : 1
Last page: 30
Number of pages: 30
ISSN: 0360-0300
eISSN: 1557-7341
DOI: https://doi.org/10.1145/3483596
Web address : https://dl.acm.org/doi/10.1145/3483596
Abstract
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.
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.
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