A Two-Delay Combination Model for Stock Price Prediction

Manlika Ratchagit () and Honglei Xu
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Manlika Ratchagit: School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth, WA 6845, Australia
Honglei Xu: School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth, WA 6845, Australia

Mathematics, 2022, vol. 10, issue 19, 1-21

Abstract: This paper proposes a new linear combination model to predict the closing prices on multivariate financial data sets. The new approach integrates two delays of deep learning methods called the two-delay combination model. The forecasts are derived from three different deep learning models: the multilayer perceptron (MLP), the convolutional neural network (CNN) and the long short-term memory (LSTM) network. Moreover, the weight combination of our proposed model is estimated using the differential evolution (DE) algorithm. The proposed model is built and tested for three high-frequency stock data in financial markets—Microsoft Corporation (MSFT), Johnson & Johnson (JNJ) and Pfizer Inc. (PFE). The individual and combination forecast methods are compared using the root mean square error (RMSE) and the mean absolute percentage error (MAPE). The state-of-the-art combination models used in this paper are the equal weight (EW), the inverse of RMSE (INV-RMSE) and the variance-no-covariance (VAR-NO-CORR) methods. These comparisons demonstrate that our proposed approach using DE weight’s optimization has significantly lower forecast errors than the individual model and the state-of-the-art weight combination procedures for all experiments. Consequently, combining two delay deep learning models using differential evolution weights can effectively improve the stock price prediction.

Keywords: combination method; differential evolution; stock price prediction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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