Multi-level stacking of LSTM recurrent models for predicting stock-market indices

Multi-level stacking of LSTM recurrent models for predicting stock-market indices

The ability to predict stock-market indices is important to investors and financial decision-makers. However, the uncertainty of available information makes accurate prediction extremely challenging. In this work, we propose and validate a multi-level stacking model of long short-term memory (LSTM)...

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Bibliographic Details
Main Authors: Fatima Tfaily, Mohamad M. Fouad
Format: Article
Language:English
Published: AIMS Press 2022-05-01
Series:Data Science in Finance and Economics
Subjects:
predicting stock prices
long short-term memory
multi-level stacking
financial prediction
ensemble learning
Online Access:https://www.aimspress.com/article/doi/10.3934/DSFE.2022007?viewType=HTML

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https://www.aimspress.com/article/doi/10.3934/DSFE.2022007?viewType=HTML

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