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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Main Authors: Chang-Long Jiang, Yi-Kuang Tsai, Zhen-En Shao, Shih-Hsiung Lee, Cheng-Che Hsueh, Ko-Wei Huang
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Applied Sciences
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Online Access:https://www.mdpi.com/2076-3417/14/23/11380
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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
collection DOAJ
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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institution Kabale University
issn 2076-3417
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publishDate 2024-12-01
publisher MDPI AG
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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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