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

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 mar...

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Published in:Heliyon Vol. 10; no. 6; p. e27747
Main Authors: Latif, Saima, Javaid, Nadeem, Aslam, Faheem, Aldegheishem, Abdulaziz, Alrajeh, Nabil, Bouk, Safdar Hussain
Format: Journal Article
Language:English
Published: England Elsevier Ltd 30.03.2024
Elsevier
Subjects:
Bayesian optimization
China
Contractive autoencoder
data collection
Ensemble empirical mode decomposition
India
Peephole LSTM
prediction
stakeholders
stock exchange
Stock market prediction
Temporal attention layer
Time series
Urban planing
urbanization
China
India
Contractive autoencoder
Urban planing
Peephole LSTM
Temporal attention layer
Time series
Ensemble empirical mode decomposition
Bayesian optimization
Stock market prediction
ISSN:2405-8440, 2405-8440
Online Access:Get full text
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Summary: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 trading strategies can be formulated based on our proposed prediction model.
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ISSN:2405-8440
2405-8440
DOI:10.1016/j.heliyon.2024.e27747