Enhancing stock market Forecasting: A hybrid model for accurate prediction of S&P 500 and CSI 300 future prices
This paper investigates the challenging domain of stock market prediction, a significant aspect of financial markets. It focuses on developing predictive models to forecast stock prices accurately, vital for mitigating losses and maximizing gains amidst the inherent unpredictability and volatility o...
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| Vydané v: | Expert systems with applications Ročník 260; s. 125380 |
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| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
15.01.2025
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| ISSN: | 0957-4174 |
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| Abstract | This paper investigates the challenging domain of stock market prediction, a significant aspect of financial markets. It focuses on developing predictive models to forecast stock prices accurately, vital for mitigating losses and maximizing gains amidst the inherent unpredictability and volatility of the market. The study comprehensively analyzes various predictive models, including time series analysis and advanced machine learning techniques. It highlights the superiority of ensemble or hybrid models in enhancing prediction reliability. Central to this research is the development of a model incorporating detailed data collection, thorough analysis, and state-of-the-art machine learning methods, achieving notable predictive accuracy. This approach underscores the benefits of data-centric strategies in today’s rapidly evolving business environment and the widespread applicability of predictive analytics. The model outperforms conventional methods by decomposing time series into simpler components and optimizing hyperparameters, thereby enhancing prediction accuracy, as demonstrated by performance testing on the S&P 500 and CSI 300 indices. The RMSE, MAE, and R2 values of the MEME-AO-LSTM model are 27.12, 19.43, and 0.992, respectively, which serve as evidence of this. The model’s generalizability and high performance are demonstrated by its efficacy in a variety of major markets, including the NASDAQ 100, Nikkei 225, FTSE, DAX, SSE, and KOSPI. Additionally, the model’s adaptability under diverse market conditions is demonstrated through its evaluation of its robustness in response to significant events, such as the economic stimulus responses to the COVID-19 pandemic and the geopolitical tensions resulting from the tension and conflict between Russia and Ukraine. Consequently, the proposed methodology has the potential to help investors achieve substantial and advantageous returns. |
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| AbstractList | This paper investigates the challenging domain of stock market prediction, a significant aspect of financial markets. It focuses on developing predictive models to forecast stock prices accurately, vital for mitigating losses and maximizing gains amidst the inherent unpredictability and volatility of the market. The study comprehensively analyzes various predictive models, including time series analysis and advanced machine learning techniques. It highlights the superiority of ensemble or hybrid models in enhancing prediction reliability. Central to this research is the development of a model incorporating detailed data collection, thorough analysis, and state-of-the-art machine learning methods, achieving notable predictive accuracy. This approach underscores the benefits of data-centric strategies in today’s rapidly evolving business environment and the widespread applicability of predictive analytics. The model outperforms conventional methods by decomposing time series into simpler components and optimizing hyperparameters, thereby enhancing prediction accuracy, as demonstrated by performance testing on the S&P 500 and CSI 300 indices. The RMSE, MAE, and R2 values of the MEME-AO-LSTM model are 27.12, 19.43, and 0.992, respectively, which serve as evidence of this. The model’s generalizability and high performance are demonstrated by its efficacy in a variety of major markets, including the NASDAQ 100, Nikkei 225, FTSE, DAX, SSE, and KOSPI. Additionally, the model’s adaptability under diverse market conditions is demonstrated through its evaluation of its robustness in response to significant events, such as the economic stimulus responses to the COVID-19 pandemic and the geopolitical tensions resulting from the tension and conflict between Russia and Ukraine. Consequently, the proposed methodology has the potential to help investors achieve substantial and advantageous returns. |
| ArticleNumber | 125380 |
| Author | Ge, Qing |
| Author_xml | – sequence: 1 givenname: Qing orcidid: 0009-0006-9689-7327 surname: Ge fullname: Ge, Qing email: zz1670@ynufe.edu.cn organization: International Business School, Yunnan University of Finance and Economics, Kunming, Yunnan, 650221, China |
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| Cites_doi | 10.1016/j.measurement.2020.108185 10.1016/j.jhydrol.2010.10.001 10.1016/j.heliyon.2023.e20801 10.1098/rspa.2009.0502 10.1016/j.neucom.2024.127524 10.1016/j.najef.2024.102194 10.54060/jmss.v3i1.42 10.1016/j.resglo.2024.100199 10.1007/s10462-020-09838-1 10.3390/pr9091551 10.1016/j.asoc.2023.110356 10.1002/fut.22335 10.1016/j.cie.2021.107250 10.3390/info15030136 10.24014/ijaidm.v7i1.28594 10.1016/j.ecolind.2023.109882 10.14569/IJACSA.2024.01506111 10.1007/s10690-023-09412-z 10.1016/j.eti.2023.103018 10.30812/matrik.v22i2.2287 10.1016/j.heliyon.2023.e15332 10.1016/j.eswa.2023.121424 10.1016/j.frl.2022.102872 10.1016/j.heliyon.2022.e10718 10.1007/s10462-019-09754-z 10.1016/j.ins.2022.05.088 10.1016/j.asoc.2023.110799 10.1016/j.energy.2024.130493 10.1088/1742-6596/1746/1/012014 10.1007/s44196-022-00140-2 10.1016/j.procs.2022.12.115 10.1016/j.eswa.2023.121204 10.3390/app13031429 10.2139/ssrn.4416226 10.1016/j.eswa.2022.117239 10.1002/ijfe.1782 |
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| Keywords | ANN CNN Std SLFN DL GRU MEMD SVR MLP BPNN TNN Aquila Optimizer EEMD RBF Multivariate Empirical Mode Decomposition Financial Markets CSI 300 EMD MSE SZSE DWT SLFFN CWT EWT IMF LSTM SSE S&P 500 MAPE RMSE SDTP BiLSTM VMD AO MAE Stock price |
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| References | Pangestu, R. A., Vitianingsih, A. V., Kacung, S., Maukar, A. L., & Noertjahyana, A. (2024). Comparative Analysis of Support Vector Regression and Linear Regression Models to Predict Apple Inc. Share Prices. Andrada-Félix, Fernández-Rodríguez, Sosvilla-Rivero (b0025) 2024; 74 Baek (b0035) 2024; 31 1–23. Gao, Li, Lu (b0070) 2023; 9 Tiwari, Chatterjee (b0190) 2010; 394 Rehman, Mandic (b0175) 2010; 466 Moreno, Seman, Stefenon, dos Santos Coelho, Mariani (b0145) 2024; 292 Chen, Y., Zhao, P., Zhang, Z., Bai, J., & Guo, Y. (2022). A Stock Price Forecasting Model Integrating Complementary Ensemble Empirical Mode Decomposition and Independent Component Analysis. (3). https://doi.org/10.3390/info15030136. Wang, Jia, Abualigah, Liu, Zheng (b0205) 2021; 9 Nti, Adekoya, Weyori (b0150) 2020; 53 Upadhyay, N. K., Singh, V., Singh, S., & Khanna, P. (2023). Enhancing Stock Market Predictability: A Comparative Analysis of RNN And LSTM Models for Retail Investors. Abualigah, Yousri, Abd Elaziz, Ewees, Al-qaness, Gandomi (b0005) 2021; 157 Hani’ah, M., Abdullah, M. Z., Sabilla, W. I., Akbar, S., & Shafara, D. R. (2023). Google Trends and Technical Indicator based Machine Learning for Stock Market Prediction. Van Houdt, Mosquera, Nápoles (b0200) 2020; 53 Balcilar, Ozdemir, Ozdemir (b0040) 2021; 26 Kumar (b0105) 2024; 580 Yao, Zhang, Zhao (b0220) 2023; 142 R.G. Ahangar M. Yahyazadehfar H. Pournaghshband The comparison of methods artificial neural network with linear regression using specific variables for prediction stock price in Tehran stock exchange ArXiv Preprint 2010 ArXiv:1003.1457. , Zhang, Qin, Zhang, Bao, Zhang, Liu (b0230) 2022; 202 . Atri, Teka, Kouki (b0030) 2023; 9 Jia, Liuyang, Xu (b0090) 2023; 32 Li, Liu, Wu, Chen (b0110) 2020; 166 Chen, L., Wu, T., Wang, Z., Lin, X., & Cai, Y. (2023). A novel hybrid BPNN model based on adaptive evolutionary Artificial Bee Colony Algorithm for water quality index prediction. Pagliaro, A. (2023). Oanh (b0155) 2022; 8 Botunac, I., Bosna, J., & Matetić, M. (2024). Optimization of Traditional Stock Market Strategies Using the LSTM Hybrid Approach. Liu, Sayed, Sivaraman, Alshahrani, Venkatesan, Thajudeen, Al-Bahrani, Hadrawi, Yasin (b0130) 2023; 30 Yiming (b0225) 2024; 15 (December 2022), 109882. https://doi.org/10.1016/j.ecolind.2023.109882. Wang, Bouri, Ferreira, Shahzad, Ferrer (b0210) 2022; 48 Deng, Huang, Hasan, Bao (b0065) 2022; 607 Tao, Wu, Wang (b0185) 2024; 237 L. Xia X. Liu L. Wang Forecasting Framework Using Hybrid Modeling and Support Vector Regression Journal of Physics: Conference Series Volume 1746, Issue 1, Page 012014 2021 ISSN 1742–6588 1742–6596 10.1088/1742-6596/1746/1/012014. Ahuja, Kumar, Goyal, Kaur, Sachdeva, Solanki (b0015) 2023; 2023 Jin, Yuan, Long, Li, Lian (b0100) 2022; 42 Mintarya, Halim, Angie, Achmad, Kurniawan (b0140) 2023; 216 Ali, Khan, Alshanbari, El-Bagoury (b0020) 2023; 13 Bhandari, Rimal, Pokhrel, Rimal, Dahal, Khatri (b0045) 2022; 9 Olayungbo, Zhuparova, Al-Faryan, Ojo (b0160) 2024; 8 Jiang, Chen, Jiang, Ni, Su (b0095) 2023; 147 (April 2023), 121204. https://doi.org/10.1016/j.eswa.2023.121204. Liu, Yang, Su, Cao (b0125) 2024; 15 Song, Chen, Xia, Ding, Xu (b0180) 2022; 260 (1). https://doi.org/10.1007/s44196-022-00140-2. Ma, D., Yuan, D., Huang, M., & Dong, L. (2024). VGC-GAN: A multi-graph convolution adversarial network for stock price prediction. Oanh (10.1016/j.eswa.2024.125380_b0155) 2022; 8 Balcilar (10.1016/j.eswa.2024.125380_b0040) 2021; 26 Wang (10.1016/j.eswa.2024.125380_b0205) 2021; 9 Olayungbo (10.1016/j.eswa.2024.125380_b0160) 2024; 8 Tiwari (10.1016/j.eswa.2024.125380_b0190) 2010; 394 Ali (10.1016/j.eswa.2024.125380_b0020) 2023; 13 Mintarya (10.1016/j.eswa.2024.125380_b0140) 2023; 216 10.1016/j.eswa.2024.125380_b0060 Bhandari (10.1016/j.eswa.2024.125380_b0045) 2022; 9 Moreno (10.1016/j.eswa.2024.125380_b0145) 2024; 292 Atri (10.1016/j.eswa.2024.125380_b0030) 2023; 9 Song (10.1016/j.eswa.2024.125380_b0180) 2022; 260 Jin (10.1016/j.eswa.2024.125380_b0100) 2022; 42 Van Houdt (10.1016/j.eswa.2024.125380_b0200) 2020; 53 Liu (10.1016/j.eswa.2024.125380_b0125) 2024; 15 Wang (10.1016/j.eswa.2024.125380_b0210) 2022; 48 10.1016/j.eswa.2024.125380_b0165 Liu (10.1016/j.eswa.2024.125380_b0130) 2023; 30 Nti (10.1016/j.eswa.2024.125380_b0150) 2020; 53 Jia (10.1016/j.eswa.2024.125380_b0090) 2023; 32 Rehman (10.1016/j.eswa.2024.125380_b0175) 2010; 466 Gao (10.1016/j.eswa.2024.125380_b0070) 2023; 9 Deng (10.1016/j.eswa.2024.125380_b0065) 2022; 607 Kumar (10.1016/j.eswa.2024.125380_b0105) 2024; 580 Tao (10.1016/j.eswa.2024.125380_b0185) 2024; 237 10.1016/j.eswa.2024.125380_b0195 10.1016/j.eswa.2024.125380_b0050 10.1016/j.eswa.2024.125380_b0170 Yao (10.1016/j.eswa.2024.125380_b0220) 2023; 142 Andrada-Félix (10.1016/j.eswa.2024.125380_b0025) 2024; 74 10.1016/j.eswa.2024.125380_b0135 Abualigah (10.1016/j.eswa.2024.125380_b0005) 2021; 157 Baek (10.1016/j.eswa.2024.125380_b0035) 2024; 31 10.1016/j.eswa.2024.125380_b0055 10.1016/j.eswa.2024.125380_b0010 10.1016/j.eswa.2024.125380_b0075 Zhang (10.1016/j.eswa.2024.125380_b0230) 2022; 202 Li (10.1016/j.eswa.2024.125380_b0110) 2020; 166 Ahuja (10.1016/j.eswa.2024.125380_b0015) 2023; 2023 Jiang (10.1016/j.eswa.2024.125380_b0095) 2023; 147 Yiming (10.1016/j.eswa.2024.125380_b0225) 2024; 15 10.1016/j.eswa.2024.125380_b0215 |
| References_xml | – volume: 2023 start-page: 1 year: 2023 end-page: 5 ident: b0015 article-title: Stock Price Prediction By Applying Machine Learning Techniques publication-title: International Conference on Emerging Smart Computing and Informatics (ESCI) – reference: (3). https://doi.org/10.3390/info15030136. – volume: 74 year: 2024 ident: b0025 article-title: A crisis like no other? Financial market analogies of the COVID-19-cum-Ukraine war crisis publication-title: The North American Journal of Economics and Finance – volume: 32 start-page: 200 year: 2023 ident: b0090 article-title: Multi-scale Dynamic Hedging of CSI 300 Index Futures Based on EMD-DCC-GARCH publication-title: Operations Research and Management Science – volume: 466 start-page: 1291 year: 2010 end-page: 1302 ident: b0175 article-title: Multivariate empirical mode decomposition publication-title: Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences – volume: 142 year: 2023 ident: b0220 article-title: Stock index forecasting based on multivariate empirical mode decomposition and temporal convolutional networks publication-title: Applied Soft Computing – volume: 15 year: 2024 ident: b0225 article-title: Review and Analysis of Financial Market Movements: Google Stock Case Study publication-title: International Journal of Advanced Computer Science & Applications – volume: 147 year: 2023 ident: b0095 article-title: A granular sigmoid extreme learning machine and its application in a weather forecast publication-title: Applied Soft Computing – reference: (December 2022), 109882. https://doi.org/10.1016/j.ecolind.2023.109882. – volume: 8 year: 2022 ident: b0155 article-title: The impact of COVID-19 vaccination on stock market: Is there any difference between developed and developing countries? publication-title: Heliyon – volume: 260 year: 2022 ident: b0180 article-title: Application of a novel signal decomposition prediction model in minute sea level prediction publication-title: Ocean Engineering – reference: (1). https://doi.org/10.1007/s44196-022-00140-2. – reference: Botunac, I., Bosna, J., & Matetić, M. (2024). Optimization of Traditional Stock Market Strategies Using the LSTM Hybrid Approach. – volume: 9 start-page: e15332 year: 2023 ident: b0030 article-title: Does US full vaccination against COVID-19 immunize correspondingly S&P500 index: Evidence from the NARDL approach publication-title: Heliyon – reference: Upadhyay, N. K., Singh, V., Singh, S., & Khanna, P. (2023). Enhancing Stock Market Predictability: A Comparative Analysis of RNN And LSTM Models for Retail Investors. – volume: 30 year: 2023 ident: b0130 article-title: Novel and robust machine learning model to optimize biodiesel production from algal oil using CaO and CaO/Al2O3 as catalyst: Sustainable green energy publication-title: Environmental Technology & Innovation – volume: 9 year: 2022 ident: b0045 article-title: Predicting stock market index using LSTM publication-title: Machine Learning with Applications – volume: 607 start-page: 297 year: 2022 end-page: 321 ident: b0065 article-title: Multi-step-ahead stock price index forecasting using long short-term memory model with multivariate empirical mode decomposition publication-title: Information Sciences – volume: 53 start-page: 5929 year: 2020 end-page: 5955 ident: b0200 article-title: A review on the long short-term memory model publication-title: Artificial Intelligence Review – volume: 166 year: 2020 ident: b0110 article-title: An optimized VMD method and its applications in bearing fault diagnosis publication-title: Measurement – reference: (April 2023), 121204. https://doi.org/10.1016/j.eswa.2023.121204. – reference: Pangestu, R. A., Vitianingsih, A. V., Kacung, S., Maukar, A. L., & Noertjahyana, A. (2024). Comparative Analysis of Support Vector Regression and Linear Regression Models to Predict Apple Inc. Share Prices. – volume: 9 year: 2021 ident: b0205 article-title: An improved hybrid aquila optimizer and harris hawks algorithm for solving industrial engineering optimization problems publication-title: Processes – reference: . 1–23. – volume: 237 year: 2024 ident: b0185 article-title: Series decomposition Transformer with period-correlation for stock market index prediction publication-title: Expert Systems with Applications – volume: 48 year: 2022 ident: b0210 article-title: A grey-based correlation with multi-scale analysis: S&P 500 VIX and individual VIXs of large US company stocks publication-title: Finance Research Letters – volume: 9 year: 2023 ident: b0070 article-title: Impact of COVID-19 on investor sentiment in China’s stock markets publication-title: Heliyon – reference: Chen, Y., Zhao, P., Zhang, Z., Bai, J., & Guo, Y. (2022). A Stock Price Forecasting Model Integrating Complementary Ensemble Empirical Mode Decomposition and Independent Component Analysis. – volume: 216 start-page: 96 year: 2023 end-page: 102 ident: b0140 article-title: Machine learning approaches in stock market prediction: A systematic literature review publication-title: Procedia Computer Science – reference: Ma, D., Yuan, D., Huang, M., & Dong, L. (2024). VGC-GAN: A multi-graph convolution adversarial network for stock price prediction. – reference: R.G. Ahangar M. Yahyazadehfar H. Pournaghshband The comparison of methods artificial neural network with linear regression using specific variables for prediction stock price in Tehran stock exchange ArXiv Preprint 2010 ArXiv:1003.1457. – volume: 31 start-page: 205 year: 2024 end-page: 220 ident: b0035 article-title: A CNN-LSTM Stock Prediction Model Based on Genetic Algorithm Optimization publication-title: Asia-Pacific Financial Markets – reference: , – volume: 42 start-page: 1352 year: 2022 end-page: 1368 ident: b0100 article-title: Price discovery in the CSI 300 Index derivatives markets publication-title: Journal of Futures Markets – reference: Hani’ah, M., Abdullah, M. Z., Sabilla, W. I., Akbar, S., & Shafara, D. R. (2023). Google Trends and Technical Indicator based Machine Learning for Stock Market Prediction. – volume: 26 start-page: 153 year: 2021 end-page: 170 ident: b0040 article-title: Dynamic return and volatility spillovers among S&P 500, crude oil, and gold publication-title: International Journal of Finance & Economics – volume: 13 start-page: 1429 year: 2023 ident: b0020 article-title: Prediction of complex stock market data using an improved hybrid emd-lstm model publication-title: Applied Sciences – volume: 580 year: 2024 ident: b0105 article-title: Recurrent context layered radial basis function neural network for the identification of nonlinear dynamical systems publication-title: Neurocomputing – volume: 292 year: 2024 ident: b0145 article-title: Enhancing wind speed forecasting through synergy of machine learning, singular spectral analysis, and variational mode decomposition publication-title: Energy – volume: 394 start-page: 458 year: 2010 end-page: 470 ident: b0190 article-title: Development of an accurate and reliable hourly flood forecasting model using wavelet–bootstrap–ANN (WBANN) hybrid approach publication-title: Journal of Hydrology – volume: 202 year: 2022 ident: b0230 article-title: Transformer-based attention network for stock movement prediction publication-title: Expert Systems with Applications – volume: 8 year: 2024 ident: b0160 article-title: Global oil price and stock markets in oil exporting and European countries: Evidence during the Covid-19 and the Russia-Ukraine war publication-title: Research in Globalization – volume: 15 year: 2024 ident: b0125 article-title: A Hybrid Framework for Evaluating Financial Market Price: An Analysis of the Hang Seng Index Case Study publication-title: International Journal of Advanced Computer Science & Applications – reference: Chen, L., Wu, T., Wang, Z., Lin, X., & Cai, Y. (2023). A novel hybrid BPNN model based on adaptive evolutionary Artificial Bee Colony Algorithm for water quality index prediction. – reference: . – reference: Pagliaro, A. (2023). – volume: 157 year: 2021 ident: b0005 article-title: Aquila Optimizer: A novel meta-heuristic optimization algorithm publication-title: Computers & Industrial Engineering – volume: 53 start-page: 3007 year: 2020 end-page: 3057 ident: b0150 article-title: A systematic review of fundamental and technical analysis of stock market predictions publication-title: Artificial Intelligence Review – reference: L. Xia X. Liu L. Wang Forecasting Framework Using Hybrid Modeling and Support Vector Regression Journal of Physics: Conference Series Volume 1746, Issue 1, Page 012014 2021 ISSN 1742–6588 1742–6596 10.1088/1742-6596/1746/1/012014. – volume: 166 year: 2020 ident: 10.1016/j.eswa.2024.125380_b0110 article-title: An optimized VMD method and its applications in bearing fault diagnosis publication-title: Measurement doi: 10.1016/j.measurement.2020.108185 – volume: 2023 start-page: 1 year: 2023 ident: 10.1016/j.eswa.2024.125380_b0015 article-title: Stock Price Prediction By Applying Machine Learning Techniques publication-title: International Conference on Emerging Smart Computing and Informatics (ESCI) – volume: 394 start-page: 458 issue: 3–4 year: 2010 ident: 10.1016/j.eswa.2024.125380_b0190 article-title: Development of an accurate and reliable hourly flood forecasting model using wavelet–bootstrap–ANN (WBANN) hybrid approach publication-title: Journal of Hydrology doi: 10.1016/j.jhydrol.2010.10.001 – volume: 9 issue: 10 year: 2023 ident: 10.1016/j.eswa.2024.125380_b0070 article-title: Impact of COVID-19 on investor sentiment in China’s stock markets publication-title: Heliyon doi: 10.1016/j.heliyon.2023.e20801 – volume: 466 start-page: 1291 issue: 2117 year: 2010 ident: 10.1016/j.eswa.2024.125380_b0175 article-title: Multivariate empirical mode decomposition publication-title: Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences doi: 10.1098/rspa.2009.0502 – volume: 580 year: 2024 ident: 10.1016/j.eswa.2024.125380_b0105 article-title: Recurrent context layered radial basis function neural network for the identification of nonlinear dynamical systems publication-title: Neurocomputing doi: 10.1016/j.neucom.2024.127524 – volume: 9 issue: February year: 2022 ident: 10.1016/j.eswa.2024.125380_b0045 article-title: Predicting stock market index using LSTM publication-title: Machine Learning with Applications – volume: 74 year: 2024 ident: 10.1016/j.eswa.2024.125380_b0025 article-title: A crisis like no other? Financial market analogies of the COVID-19-cum-Ukraine war crisis publication-title: The North American Journal of Economics and Finance doi: 10.1016/j.najef.2024.102194 – ident: 10.1016/j.eswa.2024.125380_b0195 doi: 10.54060/jmss.v3i1.42 – volume: 8 year: 2024 ident: 10.1016/j.eswa.2024.125380_b0160 article-title: Global oil price and stock markets in oil exporting and European countries: Evidence during the Covid-19 and the Russia-Ukraine war publication-title: Research in Globalization doi: 10.1016/j.resglo.2024.100199 – volume: 53 start-page: 5929 year: 2020 ident: 10.1016/j.eswa.2024.125380_b0200 article-title: A review on the long short-term memory model publication-title: Artificial Intelligence Review doi: 10.1007/s10462-020-09838-1 – volume: 9 issue: 9 year: 2021 ident: 10.1016/j.eswa.2024.125380_b0205 article-title: An improved hybrid aquila optimizer and harris hawks algorithm for solving industrial engineering optimization problems publication-title: Processes doi: 10.3390/pr9091551 – volume: 142 year: 2023 ident: 10.1016/j.eswa.2024.125380_b0220 article-title: Stock index forecasting based on multivariate empirical mode decomposition and temporal convolutional networks publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2023.110356 – volume: 42 start-page: 1352 issue: 7 year: 2022 ident: 10.1016/j.eswa.2024.125380_b0100 article-title: Price discovery in the CSI 300 Index derivatives markets publication-title: Journal of Futures Markets doi: 10.1002/fut.22335 – volume: 260 issue: February year: 2022 ident: 10.1016/j.eswa.2024.125380_b0180 article-title: Application of a novel signal decomposition prediction model in minute sea level prediction publication-title: Ocean Engineering – volume: 157 year: 2021 ident: 10.1016/j.eswa.2024.125380_b0005 article-title: Aquila Optimizer: A novel meta-heuristic optimization algorithm publication-title: Computers & Industrial Engineering doi: 10.1016/j.cie.2021.107250 – ident: 10.1016/j.eswa.2024.125380_b0050 doi: 10.3390/info15030136 – ident: 10.1016/j.eswa.2024.125380_b0170 doi: 10.24014/ijaidm.v7i1.28594 – ident: 10.1016/j.eswa.2024.125380_b0055 doi: 10.1016/j.ecolind.2023.109882 – volume: 15 issue: 6 year: 2024 ident: 10.1016/j.eswa.2024.125380_b0125 article-title: A Hybrid Framework for Evaluating Financial Market Price: An Analysis of the Hang Seng Index Case Study publication-title: International Journal of Advanced Computer Science & Applications doi: 10.14569/IJACSA.2024.01506111 – volume: 31 start-page: 205 issue: 2 year: 2024 ident: 10.1016/j.eswa.2024.125380_b0035 article-title: A CNN-LSTM Stock Prediction Model Based on Genetic Algorithm Optimization publication-title: Asia-Pacific Financial Markets doi: 10.1007/s10690-023-09412-z – volume: 30 year: 2023 ident: 10.1016/j.eswa.2024.125380_b0130 article-title: Novel and robust machine learning model to optimize biodiesel production from algal oil using CaO and CaO/Al2O3 as catalyst: Sustainable green energy publication-title: Environmental Technology & Innovation doi: 10.1016/j.eti.2023.103018 – ident: 10.1016/j.eswa.2024.125380_b0075 doi: 10.30812/matrik.v22i2.2287 – volume: 9 start-page: e15332 issue: 4 year: 2023 ident: 10.1016/j.eswa.2024.125380_b0030 article-title: Does US full vaccination against COVID-19 immunize correspondingly S&P500 index: Evidence from the NARDL approach publication-title: Heliyon doi: 10.1016/j.heliyon.2023.e15332 – volume: 237 year: 2024 ident: 10.1016/j.eswa.2024.125380_b0185 article-title: Series decomposition Transformer with period-correlation for stock market index prediction publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2023.121424 – volume: 48 year: 2022 ident: 10.1016/j.eswa.2024.125380_b0210 article-title: A grey-based correlation with multi-scale analysis: S&P 500 VIX and individual VIXs of large US company stocks publication-title: Finance Research Letters doi: 10.1016/j.frl.2022.102872 – volume: 8 issue: 9 year: 2022 ident: 10.1016/j.eswa.2024.125380_b0155 article-title: The impact of COVID-19 vaccination on stock market: Is there any difference between developed and developing countries? publication-title: Heliyon doi: 10.1016/j.heliyon.2022.e10718 – volume: 32 start-page: 200 issue: 9 year: 2023 ident: 10.1016/j.eswa.2024.125380_b0090 article-title: Multi-scale Dynamic Hedging of CSI 300 Index Futures Based on EMD-DCC-GARCH publication-title: Operations Research and Management Science – ident: 10.1016/j.eswa.2024.125380_b0010 – volume: 53 start-page: 3007 issue: 4 year: 2020 ident: 10.1016/j.eswa.2024.125380_b0150 article-title: A systematic review of fundamental and technical analysis of stock market predictions publication-title: Artificial Intelligence Review doi: 10.1007/s10462-019-09754-z – volume: 607 start-page: 297 year: 2022 ident: 10.1016/j.eswa.2024.125380_b0065 article-title: Multi-step-ahead stock price index forecasting using long short-term memory model with multivariate empirical mode decomposition publication-title: Information Sciences doi: 10.1016/j.ins.2022.05.088 – volume: 147 year: 2023 ident: 10.1016/j.eswa.2024.125380_b0095 article-title: A granular sigmoid extreme learning machine and its application in a weather forecast publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2023.110799 – volume: 292 year: 2024 ident: 10.1016/j.eswa.2024.125380_b0145 article-title: Enhancing wind speed forecasting through synergy of machine learning, singular spectral analysis, and variational mode decomposition publication-title: Energy doi: 10.1016/j.energy.2024.130493 – ident: 10.1016/j.eswa.2024.125380_b0215 doi: 10.1088/1742-6596/1746/1/012014 – volume: 15 issue: 4 year: 2024 ident: 10.1016/j.eswa.2024.125380_b0225 article-title: Review and Analysis of Financial Market Movements: Google Stock Case Study publication-title: International Journal of Advanced Computer Science & Applications – ident: 10.1016/j.eswa.2024.125380_b0060 doi: 10.1007/s44196-022-00140-2 – volume: 216 start-page: 96 year: 2023 ident: 10.1016/j.eswa.2024.125380_b0140 article-title: Machine learning approaches in stock market prediction: A systematic literature review publication-title: Procedia Computer Science doi: 10.1016/j.procs.2022.12.115 – ident: 10.1016/j.eswa.2024.125380_b0135 doi: 10.1016/j.eswa.2023.121204 – volume: 13 start-page: 1429 issue: 3 year: 2023 ident: 10.1016/j.eswa.2024.125380_b0020 article-title: Prediction of complex stock market data using an improved hybrid emd-lstm model publication-title: Applied Sciences doi: 10.3390/app13031429 – ident: 10.1016/j.eswa.2024.125380_b0165 doi: 10.2139/ssrn.4416226 – volume: 202 year: 2022 ident: 10.1016/j.eswa.2024.125380_b0230 article-title: Transformer-based attention network for stock movement prediction publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2022.117239 – volume: 26 start-page: 153 issue: 1 year: 2021 ident: 10.1016/j.eswa.2024.125380_b0040 article-title: Dynamic return and volatility spillovers among S&P 500, crude oil, and gold publication-title: International Journal of Finance & Economics doi: 10.1002/ijfe.1782 |
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