Hybrid Crow Search Algorithm–LSTM System for Enhanced Stock Price Forecasting
This study presents a hybrid crow search algorithm–long short-term memory (CSLSTM) system for forecasting stock prices. This system allows investors to effectively avoid risks and enhance profits by predicting the closing price the following day. This method utilizes a stacking ensemble of long shor...
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MDPI AG
2024-12-01
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author | Chang-Long Jiang Yi-Kuang Tsai Zhen-En Shao Shih-Hsiung Lee Cheng-Che Hsueh Ko-Wei Huang |
author_facet | Chang-Long Jiang Yi-Kuang Tsai Zhen-En Shao Shih-Hsiung Lee Cheng-Che Hsueh Ko-Wei Huang |
author_sort | Chang-Long Jiang |
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description | This study presents a hybrid crow search algorithm–long short-term memory (CSLSTM) system for forecasting stock prices. This system allows investors to effectively avoid risks and enhance profits by predicting the closing price the following day. This method utilizes a stacking ensemble of long short-term memory (LSTM) networks, with the crow search algorithm (CSA) optimizing the weights assigned to the predictions from multiple LSTM models. To improve the overall accuracy, this system leverages three distinct datasets: technical analysis indicators; price fluctuation limits; and variation mode decomposition (VMD) subsignal sequences. The predictions for the three reference-data types are more comprehensive than single-model or single-data-type approaches. The prediction accuracies of the recurrent neural network, gate recurrent unit, and the LSTM network for five stocks were compared. The proposed CSLSTM system outperforms the other standalone models. Furthermore, we conducted backtesting to demonstrate that the prediction information from our model could generate profit in the stock market, enabling users to benefit from complex stock-market dynamics. The stock prices in this study are expressed in New Taiwan Dollars (TWD), the official currency of Taiwan. |
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id | doaj-art-8f81a00873974b0d997ca88b6726b5e7 |
institution | Kabale University |
issn | 2076-3417 |
language | English |
publishDate | 2024-12-01 |
publisher | MDPI AG |
record_format | Article |
series | Applied Sciences |
spelling | doaj-art-8f81a00873974b0d997ca88b6726b5e72024-12-13T16:23:44ZengMDPI AGApplied Sciences2076-34172024-12-0114231138010.3390/app142311380Hybrid Crow Search Algorithm–LSTM System for Enhanced Stock Price ForecastingChang-Long Jiang0Yi-Kuang Tsai1Zhen-En Shao2Shih-Hsiung Lee3Cheng-Che Hsueh4Ko-Wei Huang5Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City 80778, TaiwanDepartment of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City 80778, TaiwanDepartment of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City 80778, TaiwanDepartment of Intelligent Commerce, National Kaohsiung University of Science and Technology, Kaohsiung City 80778, TaiwanDepartment of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City 80778, TaiwanDepartment of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City 80778, TaiwanThis study presents a hybrid crow search algorithm–long short-term memory (CSLSTM) system for forecasting stock prices. This system allows investors to effectively avoid risks and enhance profits by predicting the closing price the following day. This method utilizes a stacking ensemble of long short-term memory (LSTM) networks, with the crow search algorithm (CSA) optimizing the weights assigned to the predictions from multiple LSTM models. To improve the overall accuracy, this system leverages three distinct datasets: technical analysis indicators; price fluctuation limits; and variation mode decomposition (VMD) subsignal sequences. The predictions for the three reference-data types are more comprehensive than single-model or single-data-type approaches. The prediction accuracies of the recurrent neural network, gate recurrent unit, and the LSTM network for five stocks were compared. The proposed CSLSTM system outperforms the other standalone models. Furthermore, we conducted backtesting to demonstrate that the prediction information from our model could generate profit in the stock market, enabling users to benefit from complex stock-market dynamics. The stock prices in this study are expressed in New Taiwan Dollars (TWD), the official currency of Taiwan.https://www.mdpi.com/2076-3417/14/23/11380metaheuristic algorithmlong short-term memory (LSTM)crow search algorithm (CSA)stock-price forecastingtime-series forecasting |
spellingShingle | Chang-Long Jiang Yi-Kuang Tsai Zhen-En Shao Shih-Hsiung Lee Cheng-Che Hsueh Ko-Wei Huang Hybrid Crow Search Algorithm–LSTM System for Enhanced Stock Price Forecasting Applied Sciences metaheuristic algorithm long short-term memory (LSTM) crow search algorithm (CSA) stock-price forecasting time-series forecasting |
title | Hybrid Crow Search Algorithm–LSTM System for Enhanced Stock Price Forecasting |
title_full | Hybrid Crow Search Algorithm–LSTM System for Enhanced Stock Price Forecasting |
title_fullStr | Hybrid Crow Search Algorithm–LSTM System for Enhanced Stock Price Forecasting |
title_full_unstemmed | Hybrid Crow Search Algorithm–LSTM System for Enhanced Stock Price Forecasting |
title_short | Hybrid Crow Search Algorithm–LSTM System for Enhanced Stock Price Forecasting |
title_sort | hybrid crow search algorithm lstm system for enhanced stock price forecasting |
topic | metaheuristic algorithm long short-term memory (LSTM) crow search algorithm (CSA) stock-price forecasting time-series forecasting |
url | https://www.mdpi.com/2076-3417/14/23/11380 |
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