TY  - JOUR
ID  - scholars14133
N2  - The primary purpose of trading in stock markets is to profit from buying and selling listed stocks. However, numerous factors can influence the stock prices, such as the company's present financial situation, news, rumor, macroeconomics, psychological, economic, political, and geopolitical factors. Consequently, tremendous challenges already exist in predicting noisy stock prices. This paper proposes a hybrid model integrating the singular spectrum analysis (SSA) and the backpropagation neural network (BPNN) to forecast daily closing prices in stock markets. The model first decomposes the stock prices into several components using the SSA. Then, the extracted components are utilized for training BPNNs to forecast future prices. Compared with the BPNN, the hybrid SSA-BPNN model demonstrates a better predictive performance, indicating the SSA's ability to extract hidden information and reduce the noise effect of the original time series. © 2021 Lavoisier. All rights reserved.
IS  - 6
VL  - 35
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122723136&doi=10.18280%2fria.350606&partnerID=40&md5=bff149351800e7e03ea25cb8fe2bf06a
JF  - Revue d'Intelligence Artificielle
A1  - Fathi, A.Y.
A1  - El-Khodary, I.A.
A1  - Saafan, M.
Y1  - 2021///
TI  - A Hybrid Model Integrating Singular Spectrum Analysis and Backpropagation Neural Network for Stock Price Forecasting
SP  - 483
N1  - cited By 4
AV  - none
EP  - 488
SN  - 0992499X
PB  - International Information and Engineering Technology Association
ER  -