Multi-model Forecasting for Finance

Daniel Jader Pellattiero () and Antonio Candelieri ()
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Daniel Jader Pellattiero: University of Studies of Milano-Bicocca
Antonio Candelieri: University of Studies of Milano-Bicocca

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2024, pp 248-254 from Springer

Abstract: Abstract This paper presents a novel approach for forecasting stock prices. Specifically, the approach consists of an ensemble of various deep learning models, namely “multi-model”. Each deep learning model produces its own forecast, then all the forecasts are combined into a unique one, according to different strategies and depending on different error metrics. The final forecasts provided by the multi-model have resulted in more reliable predictions than those provided by the individual deep learning models.

Keywords: forecasting; stock markets; deep learning; neural networks; recurrent neural networks; wavelet thresholding; CEEMDAN (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-64273-9_41

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DOI: 10.1007/978-3-031-64273-9_41

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