A2 Vertaisarvioitu katsausartikkeli tieteellisessä lehdessä
Neural Network-Based Financial Volatility Forecasting: A Systematic Review
Tekijät: Ge Wenbo, Lalbakhsh Pooia, Isai Leigh, Lenskiy Artem, Suominen Hanna
Kustantaja: ASSOC COMPUTING MACHINERY
Julkaisuvuosi: 2023
Journal: ACM Computing Surveys
Tietokannassa oleva lehden nimi: ACM COMPUTING SURVEYS
Lehden akronyymi: ACM COMPUT SURV
Artikkelin numero: 14
Vuosikerta: 55
Numero: 1
Aloitussivu: 1
Lopetussivu: 30
Sivujen määrä: 30
ISSN: 0360-0300
eISSN: 1557-7341
DOI: https://doi.org/10.1145/3483596
Verkko-osoite: https://dl.acm.org/doi/10.1145/3483596
Tiivistelmä
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.