Enhanced prediction of stock markets using a novel deep learning model PLSTM-TAL in urbanized smart cities

作     者：Latif, Saima Javaid, Nadeem Aslam, Faheem Aldegheishem, Abdulaziz Alrajeh, Nabil Bouk, Safdar Hussain 

作者机构：Department of Management Sciences COMSATS University Islamabad Islamabad 44000 Pakistan Department of Computer Science COMSATS University Islamabad Islamabad 44000 Pakistan International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Yunlin 64002 Taiwan School of Business Administration (SBA) Al Akhawayn University Ifrane 53003 Morocco Department of Urban Planning College of Architecture and Planning King Saud University Riyadh 11574 Saudi Arabia Department of Biomedical Technology College of Applied Medical Sciences King Saud University Riyadh 11633 Saudi Arabia Old Dominion University Hampton Blvd Norfolk 4114 United States 

出 版 物：《Heliyon》 (Heliyon)

年 卷 期：2024年第10卷第6期

页      面：e27747页

基　　金：King Saud University  KSU  (RSP2024R295) 

主　　题：Bayesian optimization Contractive autoencoder Ensemble empirical mode decomposition Peephole LSTM Stock market prediction Temporal attention layer Time series Urban planing 

摘      要：Accurate predictions of stock markets are important for investors and other stakeholders of the equity markets to formulate profitable investment strategies. The improved accuracy of a prediction model even with a slight margin can translate into considerable monetary returns. However, the stock markets prediction is regarded as an intricate research problem for the noise, complexity and volatility of the stocks data. In recent years, the deep learning models have been successful in providing robust forecasts for sequential data. We propose a novel deep learning-based hybrid classification model by combining peephole LSTM with temporal attention layer (TAL) to accurately predict the direction of stock markets. The daily data of four world indices including those of U.S., U.K., China and India, from 2005 to 2022, are examined. We present a comprehensive evaluation with preliminary data analysis, feature extraction and hyperparameters optimization for the problem of stock market prediction. TAL is introduced post peephole LSTM to select the relevant information with respect to time and enhance the performance of the proposed model. The prediction performance of the proposed model is compared with that of the benchmark models CNN, LSTM, SVM and RF using evaluation metrics of accuracy, precision, recall, F1-score, AUC-ROC, PR-AUC and MCC. The experimental results show the superior performance of our proposed model achieving better scores than the benchmark models for most evaluation metrics and for all datasets. The accuracy of the proposed model is 96% and 88% for U.K. and Chinese stock markets respectively and it is 85% for both U.S. and Indian markets. Hence, the stock markets of U.K. and China are found to be more predictable than those of U.S. and India. Significant findings of our work include that the attention layer enables peephole LSTM to better identify the long-term dependencies and temporal patterns in the stock markets data. Profitable and timely tradi