Long short-term memory autoencoder based network of financial indices

We present a novel approach for analyzing financial time series data using a Long Short-Term Memory Autoencoder (LSTMAE), a deep learning method. Our primary objective is to uncover intricate relationships among different stock indices, leading to the extraction of stock networks. We examine time se...

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Bibliographic Details
Published in:	Humanities & social sciences communications Vol. 12; no. 1; pp. 100 - 15
Main Authors:	Tuhin, Kamrul Hasan, Nobi, Ashadun, Rakib, Mahmudul Hasan, Lee, Jae Woo
Format:	Journal Article
Language:	English
Published:	London Palgrave Macmillan UK 29.01.2025 Springer Nature B.V Springer Nature
Subjects:	4014/2801 4014/4006 Causality COVID-19 Deep learning Economic crisis Extraction Financial institutions Health care Health services Humanities and Social Sciences Indexes International finance Machine learning Matrices Methods multidisciplinary National debt Networks Pandemics Property Public debt Rates of return Science Science (multidisciplinary) Securities markets Short term Short term memory Stock exchanges Thresholds Time series United States > US
ISSN:	2662-9992, 2662-9992
Online Access:	Get full text
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