This work is licensed under the Creative Commons Attribution 4.0 International License.
Yang, C. H., Lee, C. F., & Chang, P. Y. (2023). Export-and import-based economic models for predicting global trade using deep learning. Expert Systems with Applications, 218, 119590. https://doi.org/10.1016/j.eswa.2023.119590YangC. H.LeeC. F.ChangP. Y. (2023). Export-and import-based economic models for predicting global trade using deep learning. Expert Systems with Applications, 218, 119590. https://doi.org/10.1016/j.eswa.2023.119590Search in Google Scholar
Sun, S., Wang, S., & Wei, Y. (2020). A new ensemble deep learning approach for exchange rates forecasting and trading. Advanced Engineering Informatics, 46, 101160. https://doi.org/10.1016/j.aei.2020.101160SunS.WangS.WeiY. (2020). A new ensemble deep learning approach for exchange rates forecasting and trading. Advanced Engineering Informatics, 46, 101160. https://doi.org/10.1016/j.aei.2020.101160Search in Google Scholar
Gopinath, M., Batarseh, F. A., Beckman, J., Kulkarni, A., & Jeong, S. (2021). International agricultural trade forecasting using machine learning. Data & Policy, 3, e1. https://doi.org/10.1017/dap.2020.22GopinathM.BatarsehF. A.BeckmanJ.KulkarniA.JeongS. (2021). International agricultural trade forecasting using machine learning. Data & Policy, 3, e1. https://doi.org/10.1017/dap.2020.22Search in Google Scholar
Silva, T. C., Wilhelm, P. V. B., & Amancio, D. R. (2024). Machine learning and economic forecasting: The role of international trade networks. Physica A: Statistical Mechanics and Its Applications, 649, 129977. https://doi.org/10.1016/j.physa.2024.129977SilvaT. C.WilhelmP. V. B.AmancioD. R. (2024). Machine learning and economic forecasting: The role of international trade networks. Physica A: Statistical Mechanics and Its Applications, 649, 129977. https://doi.org/10.1016/j.physa.2024.129977Search in Google Scholar
Zhao, Y., Liu, L., Wang, A., & Liu, M. (2023). A novel deep learning based forecasting model for carbon emissions trading: A comparative analysis of regional markets. Solar Energy, 262, 111863. https://doi.org/10.1016/j.solener.2023.111863ZhaoY.LiuL.WangA.LiuM. (2023). A novel deep learning based forecasting model for carbon emissions trading: A comparative analysis of regional markets. Solar Energy, 262, 111863. https://doi.org/10.1016/j.solener.2023.111863Search in Google Scholar
Arévalo, A., Niño, J., Hernández, G., & Sandoval, J. (2016, July). High-frequency trading strategy based on deep neural networks. In International Conference on Intelligent Computing (pp. 424–436). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-42297-8_40ArévaloA.NiñoJ.HernándezG.SandovalJ. (2016, July). High-frequency trading strategy based on deep neural networks. In International Conference on Intelligent Computing (pp. 424–436). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-42297-8_40Search in Google Scholar
Long, W., Lu, Z., & Cui, L. (2019). Deep learning-based feature engineering for stock price movement prediction. Knowledge-Based Systems, 164, 163–173. https://doi.org/10.1016/j.knosys.2018.10.034LongW.LuZ.CuiL. (2019). Deep learning-based feature engineering for stock price movement prediction. Knowledge-Based Systems, 164, 163–173. https://doi.org/10.1016/j.knosys.2018.10.034Search in Google Scholar
Shaban, W. M., Ashraf, E., & Slama, A. E. (2024). SMP-DL: A novel stock market prediction approach based on deep learning for effective trend forecasting. Neural Computing and Applications, 36(4), 1849–1873. https://doi.org/10.1007/s00521-023-09179-4ShabanW. M.AshrafE.SlamaA. E. (2024). SMP-DL: A novel stock market prediction approach based on deep learning for effective trend forecasting. Neural Computing and Applications, 36(4), 1849–1873. https://doi.org/10.1007/s00521-023-09179-4Search in Google Scholar
Mao, W., Zhu, H., Wu, H., Lu, Y., & Wang, H. (2023). Forecasting and trading credit default swap indices using a deep learning model integrating Merton and LSTMs. Expert Systems with Applications, 213, 119012. https://doi.org/10.1016/j.eswa.2022.119012MaoW.ZhuH.WuH.LuY.WangH. (2023). Forecasting and trading credit default swap indices using a deep learning model integrating Merton and LSTMs. Expert Systems with Applications, 213, 119012. https://doi.org/10.1016/j.eswa.2022.119012Search in Google Scholar
Hafeez, G., Alimgeer, K. S., & Khan, I. (2020). Electric load forecasting based on deep learning and optimized by heuristic algorithm in smart grid. Applied Energy, 269, 114915. https://doi.org/10.1016/j.apenergy.2020.114915HafeezG.AlimgeerK. S.KhanI. (2020). Electric load forecasting based on deep learning and optimized by heuristic algorithm in smart grid. Applied Energy, 269, 114915. https://doi.org/10.1016/j.apenergy.2020.114915Search in Google Scholar
Lin, H., Sun, Q., & Chen, S. Q. (2020). Reducing exchange rate risks in international trade: A hybrid forecasting approach of CEEMDAN and multilayer LSTM. Sustainability, 12(6), 2451. https://doi.org/10.3390/su12062451LinH.SunQ.ChenS. Q. (2020). Reducing exchange rate risks in international trade: A hybrid forecasting approach of CEEMDAN and multilayer LSTM. Sustainability, 12(6), 2451. https://doi.org/10.3390/su12062451Search in Google Scholar
Aldhyani, T. H., & Alzahrani, A. (2022). Framework for predicting and modeling stock market prices based on deep learning algorithms. Electronics, 11(19), 3149. https://doi.org/10.3390/electronics11193149AldhyaniT. H.AlzahraniA. (2022). Framework for predicting and modeling stock market prices based on deep learning algorithms. Electronics, 11(19), 3149. https://doi.org/10.3390/electronics11193149Search in Google Scholar
Shamshad, H., Ullah, F., Ullah, A., Kebande, V. R., Ullah, S., & Al-Dhaqm, A. (2023). Forecasting and trading of the stable cryptocurrencies with machine learning and deep learning algorithms for market analytics. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3327440ShamshadH.UllahF.UllahA.KebandeV. R.UllahS.Al-DhaqmA. (2023). Forecasting and trading of the stable cryptocurrencies with machine learning and deep learning algorithms for market analytics. IEEE Access. https://doi.org/10.1109/ACCESS.2023.3327440Search in Google Scholar
Ma, Y., Wang, W., & Ma, Q. (2023). A novel prediction based portfolio optimization model using deep learning. Computers & Industrial Engineering, 177, 109023. https://doi.org/10.1016/j.cie.2023.109023MaY.WangW.MaQ. (2023). A novel prediction based portfolio optimization model using deep learning. Computers & Industrial Engineering, 177, 109023. https://doi.org/10.1016/j.cie.2023.109023Search in Google Scholar
Rouf, N., Malik, M. B., Arif, T., Sharma, S., Singh, S., Aich, S., & Kim, H. C. (2021). Stock market prediction using machine learning techniques: A decade survey on methodologies, recent developments, and future directions. Electronics, 10(21), 2717. https://doi.org/10.3390/electronics10212717RoufN.MalikM. B.ArifT.SharmaS.SinghS.AichS.KimH. C. (2021). Stock market prediction using machine learning techniques: A decade survey on methodologies, recent developments, and future directions. Electronics, 10(21), 2717. https://doi.org/10.3390/electronics10212717Search in Google Scholar
Chhikara, H., Chhikara, S., & Gupta, L. (2025). Predictive analytics in finance: Leveraging AI and machine learning for investment strategies. In Utilizing AI and Machine Learning in Financial Analysis (pp. 325–336). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-8507-4.ch017ChhikaraH.ChhikaraS.GuptaL. (2025). Predictive analytics in finance: Leveraging AI and machine learning for investment strategies. In Utilizing AI and Machine Learning in Financial Analysis (pp. 325–336). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-8507-4.ch017Search in Google Scholar
Chen, W., Hussain, W., Cauteruccio, F., & Zhang, X. (2023). Deep learning for financial time series prediction: A state-of-the-art review of standalone and hybrid models. CMES-Computer Modeling in Engineering and Sciences. http://hdl.handle.net/10453/179034ChenW.HussainW.CauteruccioF.ZhangX. (2023). Deep learning for financial time series prediction: A state-of-the-art review of standalone and hybrid models. CMES-Computer Modeling in Engineering and Sciences. http://hdl.handle.net/10453/179034Search in Google Scholar
Zhang, Q., Qin, C., Zhang, Y., Bao, F., Zhang, C., & Liu, P. (2022). Transformer-based attention network for stock movement prediction. Expert Systems with Applications, 202, 117239. https://doi.org/10.1016/j.eswa.2022.117239ZhangQ.QinC.ZhangY.BaoF.ZhangC.LiuP. (2022). Transformer-based attention network for stock movement prediction. Expert Systems with Applications, 202, 117239. https://doi.org/10.1016/j.eswa.2022.117239Search in Google Scholar
Arangi, V., Krishna, S. J. S., Santosh, K., Paliwal, S., Abdurasul, B., & Raj, I. I. (2024, July). Reinforcement learning-optimized trading strategies: A deep Q-network approach for high-frequency finance. In 2024 International Conference on Data Science and Network Security (ICDSNS) (pp. 1–6). IEEE. https://doi.org/10.1109/ICDSNS62112.2024.10691261ArangiV.KrishnaS. J. S.SantoshK.PaliwalS.AbdurasulB.RajI. I. (2024, July). Reinforcement learning-optimized trading strategies: A deep Q-network approach for high-frequency finance. In 2024 International Conference on Data Science and Network Security (ICDSNS) (pp. 1–6). IEEE. https://doi.org/10.1109/ICDSNS62112.2024.10691261Search in Google Scholar
Lee, S. I., & Yoo, S. J. (2020). Multimodal deep learning for finance: Integrating and forecasting international stock markets. The Journal of Supercomputing, 76, 8294–8312. https://doi.org/10.1007/s11227-019-03101-3LeeS. I.YooS. J. (2020). Multimodal deep learning for finance: Integrating and forecasting international stock markets. The Journal of Supercomputing, 76, 8294–8312. https://doi.org/10.1007/s11227-019-03101-3Search in Google Scholar
Venkataramanan, V., Kaza, S., & Annaswamy, A. M. (2022). DER forecast using privacy-preserving federated learning. IEEE Internet of Things Journal, 10(3), 2046–2055. https://doi.org/10.1109/JIOT.2022.3157299VenkataramananV.KazaS.AnnaswamyA. M. (2022). DER forecast using privacy-preserving federated learning. IEEE Internet of Things Journal, 10(3), 2046–2055. https://doi.org/10.1109/JIOT.2022.3157299Search in Google Scholar
Sellami, B., Ounoughi, C., Kalvet, T., Tiits, M., & Rincon-Yanez, D. (2024). Harnessing graph neural networks to predict international trade flows. Big Data and Cognitive Computing, 8(6), 65. https://doi.org/10.3390/bdcc8060065SellamiB.OunoughiC.KalvetT.TiitsM.Rincon-YanezD. (2024). Harnessing graph neural networks to predict international trade flows. Big Data and Cognitive Computing, 8(6), 65. https://doi.org/10.3390/bdcc8060065Search in Google Scholar
Zhang, S., Chen, Y., Zhang, W., & Feng, R. (2021). A novel ensemble deep learning model with dynamic error correction and multi-objective ensemble pruning for time series forecasting. Information Sciences, 544, 427–445. https://doi.org/10.1016/j.ins.2020.08.053ZhangS.ChenY.ZhangW.FengR. (2021). A novel ensemble deep learning model with dynamic error correction and multi-objective ensemble pruning for time series forecasting. Information Sciences, 544, 427–445. https://doi.org/10.1016/j.ins.2020.08.053Search in Google Scholar
Demir, S., Mincev, K., Kok, K., & Paterakis, N. G. (2021). Data augmentation for time series regression: Applying transformations, autoencoders and adversarial networks to electricity price forecasting. Applied Energy, 304, 117695. https://doi.org/10.1016/j.apenergy.2021.117695DemirS.MincevK.KokK.PaterakisN. G. (2021). Data augmentation for time series regression: Applying transformations, autoencoders and adversarial networks to electricity price forecasting. Applied Energy, 304, 117695. https://doi.org/10.1016/j.apenergy.2021.117695Search in Google Scholar