An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination

Abstract

Abstract In this study, the hourly directions of eight banking stocks in Borsa Istanbul were predicted using linear-based, deep-learning (LSTM) and ensemble learning (LightGBM) models. These models were trained with four different feature sets and their performances were evaluated in terms of accuracy and F-measure metrics. While the first experiments directly used the own stock features as the model inputs, the second experiments utilized reduced stock features through Variational AutoEncoders (VAE). In the last experiments, in order to grasp the effects of the other banking stocks on individ

Abstract by Hakan Gündüz, Financial Innovation (2021) — licensed CC BY 4.0.

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