A2 Vertaisarvioitu katsausartikkeli tieteellisessä lehdessä

Neural Network-Based Financial Volatility Forecasting: A Systematic Review




TekijätGe Wenbo, Lalbakhsh Pooia, Isai Leigh, Lenskiy Artem, Suominen Hanna

KustantajaASSOC COMPUTING MACHINERY

Julkaisuvuosi2023

JournalACM Computing Surveys

Tietokannassa oleva lehden nimiACM COMPUTING SURVEYS

Lehden akronyymiACM COMPUT SURV

Artikkelin numero 14

Vuosikerta55

Numero1

Aloitussivu1

Lopetussivu30

Sivujen määrä30

ISSN0360-0300

eISSN1557-7341

DOIhttps://doi.org/10.1145/3483596

Verkko-osoitehttps://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.



Last updated on 2024-26-11 at 16:25