Dynamic learning rate adjustment using volatility in LSTM models for KLCI forecasting

The prediction of financial market behaviour constitutes a multifaceted challenge, attributable to the underlying volatility and non-linear characteristics inherent within market data. Long Short-Term Memory (LSTM) models have demonstrated efficacy in capturing these complexities. This study propo...

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Bibliographic Details

Main Authors:	Abang Mohammad Hudzaifah, Abang Shakawi, Ani, Shabri
Format:	Article
Language:	en
Published:	Lviv Polytechnic National University 2025
Subjects:	QA Mathematics
Online Access:	http://ir.unimas.my/id/eprint/48254/1/Published%20Paper_MMC.pdf http://ir.unimas.my/id/eprint/48254/ https://science.lpnu.ua/mmc/all-volumes-and-issues/volume-12-number-1-2025/dynamic-learning-rate-adjustment-using-volatility https://doi.org/10.23939/mmc2025.01.158
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