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A Supervised Machine Learning in Financial Forecasting: Identifying Effective Models for the BIST100 Index

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08. Sept. 2025

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COVER HERUNTERLADEN

Adegbite, E., Guney, Y., Kwabi, F. and Tahir, S. 2019. Financial and Corporate Social Performance in the UK Listed Firms: The Relevance of Non-Linearity and Lag Effects. Review of Quantitative Finance and Accounting, 52, 105–158. DOI: 10.1007/s11156-018-0705-x. Adegbite E. Guney Y. Kwabi F. Tahir S. 2019 . Financial and Corporate Social Performance in the UK Listed Firms: The Relevance of Non-Linearity and Lag Effects . Review of Quantitative Finance and Accounting , 52 , 105 158 . 10.1007/s11156-018-0705-x . Open DOISearch in Google Scholar

Alamsyah, A., Kristanti, N. and Kristanti, F. T. 2021. Early Warning Model for Financial Distress Using Artificial Neural Network. IOP Conference Series: Materials Science and Engineering, 1098 (5), 052103. DOI: 10.1088/1757-899X/1098/5/052103. Alamsyah A. Kristanti N. Kristanti F. T. 2021 . Early Warning Model for Financial Distress Using Artificial Neural Network . IOP Conference Series: Materials Science and Engineering , 1098 ( 5 ), 052103 . 10.1088/1757-899X/1098/5/052103 . Open DOISearch in Google Scholar

Alfarhood, M., Alotaibi, R., Abdulrahim, B., Einieh, A., Almousa, M. and Alkhanifer, A. 2024. Predicting Flight Delays with Machine Learning: A Case Study from Saudi Arabian Airlines. International Journal of Aerospace Engineering, 2024, 3385463. DOI: 10.1155/2024/3385463. Alfarhood M. Alotaibi R. Abdulrahim B. Einieh A. Almousa M. Alkhanifer A. 2024 . Predicting Flight Delays with Machine Learning: A Case Study from Saudi Arabian Airlines . International Journal of Aerospace Engineering , 2024 , 3385463 . 10.1155/2024/3385463 . Open DOISearch in Google Scholar

Alizadegan, H., Radmehr, A. and Ilani, M. A. 2024. Forecasting Bitcoin Prices: A Comparative Study of Machine Learning and Deep Learning Algorithms. Preprint. DOI: 10.21203/rs.3.rs-4390390/v1. Alizadegan H. Radmehr A. Ilani M. A. 2024 . Forecasting Bitcoin Prices: A Comparative Study of Machine Learning and Deep Learning Algorithms . Preprint. 10.21203/rs.3.rs-4390390/v1 . Open DOISearch in Google Scholar

Alotaibi, T., Nazir, A., Alroobaea, R., Alotibi, M., Alsubeai, F., Alghamdi, A. and Alsulimani, T. 2018. Saudi Arabia Stock Market Prediction Using Neural Network. International Journal on Computer Science and Engineering, 10 (2), 62–70. DOI: 10.21817/ijcse/2018/v10i2/181002024. Alotaibi T. Nazir A. Alroobaea R. Alotibi M. Alsubeai F. Alghamdi A. Alsulimani T. 2018 . Saudi Arabia Stock Market Prediction Using Neural Network . International Journal on Computer Science and Engineering , 10 ( 2 ), 62 70 . 10.21817/ijcse/2018/v10i2/181002024 . Open DOISearch in Google Scholar

Alshboul, O., Shehadeh, A., Al Mamlook, R. E., Almasabha, G., Almuflih, A. S. and Alghamdi, S. Y. 2022. Prediction Liquidated Damages via Ensemble Machine Learning Model: Towards Sustainable Highway Construction Projects. Sustainability, 14 (15), 9303. DOI: 10.3390/su14159303. Alshboul O. Shehadeh A. Al Mamlook R. E. Almasabha G. Almuflih A. S. Alghamdi S. Y. 2022 . Prediction Liquidated Damages via Ensemble Machine Learning Model: Towards Sustainable Highway Construction Projects . Sustainability , 14 ( 15 ), 9303 . 10.3390/su14159303 . Open DOISearch in Google Scholar

Alshehri, A. S. 2023. Predicting Cryptocurrency Returns Using Classification and Regression Machine Learning Models. Journal of Electrical Systems, 20 (4s), 539–553. DOI: 10.52783/jes.2065. Alshehri A. S. 2023 . Predicting Cryptocurrency Returns Using Classification and Regression Machine Learning Models . Journal of Electrical Systems , 20 ( 4s ), 539 553 . 10.52783/jes.2065 . Open DOISearch in Google Scholar

An, Z., Jiang, K. and Zheng, J. R. 2023. Features of Realized Volatility Analysis and Return Predicting Based on LGBM and RNN Model. Applied and Computational Engineering, 27, 38–48. DOI: 10.54254/2755-2721/27/20230133. An Z. Jiang K. Zheng J. R. 2023 . Features of Realized Volatility Analysis and Return Predicting Based on LGBM and RNN Model . Applied and Computational Engineering , 27 , 38 48 . 10.54254/2755-2721/27/20230133 . Open DOISearch in Google Scholar

Anand, M., Velu, A. and Whig, P. 2022. Prediction of Loan Behaviour with Machine Learning Models for Secure Banking. Journal of Computer Science and Engineering, 3 (1), 1–13. DOI: 10.36596/jcse.v3i1.237. Anand M. Velu A. Whig P. 2022 . Prediction of Loan Behaviour with Machine Learning Models for Secure Banking . Journal of Computer Science and Engineering , 3 ( 1 ), 1 13 . 10.36596/jcse.v3i1.237 . Open DOISearch in Google Scholar

Aydoğmuş, H. Y., Ekinci, A., Erdal, H. İ. and Erdal, H. 2015. Optimizing the Monthly Crude Oil Price Forecasting Accuracy via Bagging Ensemble Models. Journal of Economics and International Finance, 7 (5), 127–136. DOI: 10.5897/JEIF2014.0629. Aydoğmuş H. Y. Ekinci A. Erdal H. İ. Erdal H. 2015 . Optimizing the Monthly Crude Oil Price Forecasting Accuracy via Bagging Ensemble Models . Journal of Economics and International Finance , 7 ( 5 ), 127 136 . 10.5897/JEIF2014.0629 . Open DOISearch in Google Scholar

Bačanin, N., Živković, M., Stoean, C., Antonijević, M., Janičijević, S., Šarac, M. and Štrumberger, I. 2022. Application of Natural Language Processing and Machine Learning Boosted with Swarm Intelligence for Spam Email Filtering. Mathematics, 10 (22), 4173. DOI: 10.3390/math10224173. Bačanin N. Živković M. Stoean C. Antonijević M. Janičijević S. Šarac M. Štrumberger I. 2022 . Application of Natural Language Processing and Machine Learning Boosted with Swarm Intelligence for Spam Email Filtering . Mathematics , 10 ( 22 ), 4173 . 10.3390/math10224173 . Open DOISearch in Google Scholar

Bai, Y., Bai, P., Zhang, X. and Li, H. 2023. Financial Asset Volatility Forecasting using LSTM with Intraday High-Low Price Information. In Vilas, G., Shvets, Y. and Mallick, H. (eds.). MSEA 2023: Proceedings of the 2nd International Conference on Mathematical Statistics and Economic Analysis. DOI: 10.4108/eai.26-5-2023.2334349. Bai Y. Bai P. Zhang X. Li H. 2023 . Financial Asset Volatility Forecasting using LSTM with Intraday High-Low Price Information . In Vilas G. Shvets Y. Mallick H. (eds.). MSEA 2023: Proceedings of the 2nd International Conference on Mathematical Statistics and Economic Analysis . 10.4108/eai.26-5-2023.2334349 . Open DOISearch in Google Scholar

Bellotti, T., Matousek, R. and Stewart, C. 2011. A Note Comparing Support Vector Machines and Ordered Choice Models’ Predictions of International Banks’ Ratings. Decision Support Systems, 51 (3), 682–687. DOI: 10.1016/j.dss.2011.03.008. Bellotti T. Matousek R. Stewart C. 2011 . A Note Comparing Support Vector Machines and Ordered Choice Models’ Predictions of International Banks’ Ratings . Decision Support Systems , 51 ( 3 ), 682 687 . 10.1016/j.dss.2011.03.008 . Open DOISearch in Google Scholar

Benchikh, S., Tarik, J., Roa, L. and Elmehdi, N. 2024. Impact of Feature Selection on the Prediction of Global Horizontal Irradiation under Ouarzazate City Climate. Data & Metadata, 3, 363. DOI: 10.56294/dm2024363. Benchikh S. Tarik J. Roa L. Elmehdi N. 2024 . Impact of Feature Selection on the Prediction of Global Horizontal Irradiation under Ouarzazate City Climate . Data & Metadata , 3 , 363 . 10.56294/dm2024363 . Open DOISearch in Google Scholar

Beyaz, K. and Efe, M. Ö. 2019. Forecasting BIST100 Index with Neural Network Ensembles. In 2019 11th International Conference on Electrical and Electronics Engineering (ELECO), 940–944. DOI: 10.23919/ELECO47770.2019.8990659. Beyaz K. Efe M. Ö. 2019 . Forecasting BIST100 Index with Neural Network Ensembles . In 2019 11th International Conference on Electrical and Electronics Engineering (ELECO) , 940 944 . 10.23919/ELECO47770.2019.8990659 . Open DOISearch in Google Scholar

Bhalke, D. G., Bhingarde, D., Deshmukh, S. and Dhere, D. 2022. Stock Price Prediction Using Long Short Term Memory. SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology, 14 (2s), 271–273. DOI: 10.18090/samriddhi.v14spli02.12. Bhalke D. G. Bhingarde D. Deshmukh S. Dhere D. 2022 . Stock Price Prediction Using Long Short Term Memory . SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology , 14 ( 2s ), 271 273 . 10.18090/samriddhi.v14spli02.12 . Open DOISearch in Google Scholar

Bhuiyan, M. A. M., Zurita-Valdebenito, C., Alam, M. S. and Sarmin, N. 2023. Short and Long-term Forecasting of Emerging Market Data using ARIMA-based and Boosting Machine Learning Algorithms. In Proceedings of the 5th International Conference on Statistics: Theory and Applications (ICSTA’23). DOI: 10.11159/icsta23.158. Bhuiyan M. A. M. Zurita-Valdebenito C. Alam M. S. Sarmin N. 2023 . Short and Long-term Forecasting of Emerging Market Data using ARIMA-based and Boosting Machine Learning Algorithms . In Proceedings of the 5th International Conference on Statistics: Theory and Applications (ICSTA’23) . 10.11159/icsta23.158 . Open DOISearch in Google Scholar

Bluwstein, K., Buckmann, M., Joseph, A., Kapadia, S. and Şimşek, Ö. 2023. Credit Growth, the Yield Curve and Financial Crisis Prediction: Evidence from a Machine Learning Approach. Journal of International Economics, 145, 103773. DOI: 10.1016/j.jinteco.2023.103773. Bluwstein K. Buckmann M. Joseph A. Kapadia S. Şimşek Ö. 2023 . Credit Growth, the Yield Curve and Financial Crisis Prediction: Evidence from a Machine Learning Approach . Journal of International Economics , 145 , 103773 . 10.1016/j.jinteco.2023.103773 . Open DOISearch in Google Scholar

Breiman, L. 2001. Random Forests. Machine Learning, 45, 5–32. DOI: 10.1023/A:1010933404324. Breiman L. 2001 . Random Forests . Machine Learning , 45 , 5 32 . 10.1023/A:1010933404324 . Open DOISearch in Google Scholar

Busari, G. A., Kwak, N. W. and Lim, D. H. 2021. An Application of AdaBoost-GRU Ensemble Model to Economic Time Series Prediction. Indian Journal of Science and Technology, 14 (31), 2557–2566. DOI: 10.17485/IJST/v14i31.1204. Busari G. A. Kwak N. W. Lim D. H. 2021 . An Application of AdaBoost-GRU Ensemble Model to Economic Time Series Prediction . Indian Journal of Science and Technology , 14 ( 31 ), 2557 2566 . 10.17485/IJST/v14i31.1204 . Open DOISearch in Google Scholar

Cao, J. and Sun, X. 2024. Analysis of the Difference in Stock Price Between A-shares and American Stocks in Machine Learning. SHS Web of Conferences, 181, 02011. DOI: 10.1051/shsconf/202418102011. Cao J. Sun X. 2024 . Analysis of the Difference in Stock Price Between A-shares and American Stocks in Machine Learning . SHS Web of Conferences , 181 , 02011 . 10.1051/shsconf/202418102011 . Open DOISearch in Google Scholar

Cao, J. and Wang, J. 2019. Stock Price Forecasting Model Based on Modified Convolution Neural Network and Financial Time Series Analysis. International Journal of Communication Systems, 32 (12), e3987. DOI: 10.1002/dac.3987. Cao J. Wang J. 2019 . Stock Price Forecasting Model Based on Modified Convolution Neural Network and Financial Time Series Analysis . International Journal of Communication Systems , 32 ( 12 ), e3987 . 10.1002/dac.3987 . Open DOISearch in Google Scholar

Cao, L. and Tay, F. E. H. 2001. Financial Forecasting Using Support Vector Machines. Neural Computing and Applications, 10 (2), 184–192. DOI: 10.1007/s005210170010. Cao L. Tay F. E. H. 2001 . Financial Forecasting Using Support Vector Machines . Neural Computing and Applications , 10 ( 2 ), 184 192 . 10.1007/s005210170010 . Open DOISearch in Google Scholar

Cao, W. and He, T. 2019. Predictability of Financial Crisis via Pair Coupling of Commodity Market and Stock Market. Journal of Finance and Accounting, 7 (1), 9–16. DOI: 10.11648/j.jfa.20190701.12. Cao W. He T. 2019 . Predictability of Financial Crisis via Pair Coupling of Commodity Market and Stock Market . Journal of Finance and Accounting , 7 ( 1 ), 9 16 . 10.11648/j.jfa.20190701.12 . Open DOISearch in Google Scholar

Casas, C. A. 2012. Parallelization of Artificial Neural Network Training Algorithms: A Financial Forecasting Application. In 2012 IEEE Conference on Computational Intelligence for Financial Engineering & Economics, 337–342. DOI: 10.1109/CIFEr.2012.6327811. Casas C. A. 2012 . Parallelization of Artificial Neural Network Training Algorithms: A Financial Forecasting Application . In 2012 IEEE Conference on Computational Intelligence for Financial Engineering & Economics , 337 342 . 10.1109/CIFEr.2012.6327811 . Open DOISearch in Google Scholar

Chang, V., Li, T. and Zeng, Z. 2019. Towards an Improved Adaboost Algorithmic Method for Computational Financial Analysis. Journal of Parallel and Distributed Computing, 134, 219–232. DOI: 10.1016/j.jpdc.2019.07.014. Chang V. Li T. Zeng Z. 2019 . Towards an Improved Adaboost Algorithmic Method for Computational Financial Analysis . Journal of Parallel and Distributed Computing , 134 , 219 232 . 10.1016/j.jpdc.2019.07.014 . Open DOISearch in Google Scholar

Chen, H., Didisheim, A. and Scheidegger, S. 2021a. Deep Structural Estimation: With an Application to Option Pricing. ArXiv:2102.09209. DOI: 10.48550/arXiv.2102.09209. Chen H. Didisheim A. Scheidegger S. 2021a . Deep Structural Estimation: With an Application to Option Pricing . ArXiv:2102.09209. 10.48550/arXiv.2102.09209 . Open DOISearch in Google Scholar

Chen, J. M., Zovko, M., Šimurina, N. and Zovko, V. 2021b. Fear in a Handful of Dust: The Epidemiological, Environmental, and Economic Drivers of Death by PM22.5 Pollution. International Journal of Environmental Research and Public Health, 18 (16), 8688. DOI: 10.3390/ijerph18168688. Chen J. M. Zovko M. Šimurina N. Zovko V. 2021b . Fear in a Handful of Dust: The Epidemiological, Environmental, and Economic Drivers of Death by PM2.5 Pollution . International Journal of Environmental Research and Public Health , 18 ( 16 ), 8688 . 10.3390/ijerph18168688 . Open DOISearch in Google Scholar

Chen, M.-Y. 2011. Bankruptcy Prediction in Firms with Statistical and Intelligent Techniques and a Comparison of Evolutionary Computation Approaches. Computers & Mathematics with Applications, 62 (12), 4514–4524. DOI: 10.1016/j.camwa.2011.10.030. Chen M.-Y. 2011 . Bankruptcy Prediction in Firms with Statistical and Intelligent Techniques and a Comparison of Evolutionary Computation Approaches . Computers & Mathematics with Applications , 62 ( 12 ), 4514 4524 . 10.1016/j.camwa.2011.10.030 . Open DOISearch in Google Scholar

Chen, S., Goo, Y.-J. J. and Shen, Z.-D. 2014. A Hybrid Approach of Stepwise Regression, Logistic Regression, Support Vector Machine, and Decision Tree for Forecasting Fraudulent Financial Statements. The Scientific World Journal, 2014 (1), 968712. DOI: 10.1155/2014/968712. Chen S. Goo Y.-J. J. Shen Z.-D. 2014 . A Hybrid Approach of Stepwise Regression, Logistic Regression, Support Vector Machine, and Decision Tree for Forecasting Fraudulent Financial Statements . The Scientific World Journal , 2014 ( 1 ), 968712 . 10.1155/2014/968712 . Open DOISearch in Google Scholar

Chen, T. and Guestrin, C. 2016. XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. DOI: 10.1145/2939672.2939785. Chen T. Guestrin C. 2016 . XGBoost: A Scalable Tree Boosting System . In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , 785 794 . 10.1145/2939672.2939785 . Open DOISearch in Google Scholar

Chen, Z. 2024. Stock Price Prediction with Denoising Autoencoder and Transformers. Highlights in Science, Engineering and Technology, 85, 803–810. DOI: 10.54097/1skct023. Chen Z. 2024 . Stock Price Prediction with Denoising Autoencoder and Transformers . Highlights in Science, Engineering and Technology , 85 , 803 810 . 10.54097/1skct023 . Open DOISearch in Google Scholar

Cheng, L.-C., Lu, W.-T. and Yeo, B. 2023. Predicting Abnormal Trading Behavior from Internet Rumor Propagation: A Machine Learning Approach. Financial Innovation, 9, 3. DOI: 10.1186/s40854-022-00423-9. Cheng L.-C. Lu W.-T. Yeo B. 2023 . Predicting Abnormal Trading Behavior from Internet Rumor Propagation: A Machine Learning Approach . Financial Innovation , 9 , 3 . 10.1186/s40854-022-00423-9 . Open DOISearch in Google Scholar

Choi, I., Yun, W. and Kim, W. C. 2022. Improving Data Efficiency for Analyzing Global Exchange Rate Fluctuations Based on Nonlinear Causal Network-Based Clustering. Annals of Operations Research. DOI: 10.1007/s10479-022-05101-8. Choi I. Yun W. Kim W. C. 2022 . Improving Data Efficiency for Analyzing Global Exchange Rate Fluctuations Based on Nonlinear Causal Network-Based Clustering . Annals of Operations Research . 10.1007/s10479-022-05101-8 . Open DOISearch in Google Scholar

Chow, J. C. K. 2018. Analysis of Financial Credit Risk Using Machine Learning. ArXiv:1802.05326. DOI: 10.48550/arXiv.1802.05326. Chow J. C. K. 2018 . Analysis of Financial Credit Risk Using Machine Learning . ArXiv:1802.05326. 10.48550/arXiv.1802.05326 . Open DOISearch in Google Scholar

Clements, M. P., Franses, P. H. and Swanson, N. R. 2004. Forecasting Economic and Financial Time-Series with Non-Linear Models. International Journal of Forecasting, 20 (2), 169–183. DOI: 10.1016/j.ijforecast.2003.10.004. Clements M. P. Franses P. H. Swanson N. R. 2004 . Forecasting Economic and Financial Time-Series with Non-Linear Models . International Journal of Forecasting , 20 ( 2 ), 169 183 . 10.1016/j.ijforecast.2003.10.004 . Open DOISearch in Google Scholar

Das, J. D., Thulasiram, R. K., Henry, C. and Thavaneswaran, A. 2024. Encoder–Decoder Based LSTM and GRU Architectures for Stocks and Cryptocurrency Prediction. Journal of Risk and Financial Management, 17 (5), 200. DOI: 10.3390/jrfm17050200. Das J. D. Thulasiram R. K. Henry C. Thavaneswaran A. 2024 . Encoder–Decoder Based LSTM and GRU Architectures for Stocks and Cryptocurrency Prediction . Journal of Risk and Financial Management , 17 ( 5 ), 200 . 10.3390/jrfm17050200 . Open DOISearch in Google Scholar

Davis, R. A. and Nielsen, M. S. 2020. Modeling of Time Series using Random Forests: Theoretical Developments. ArXiv:2008.02479. DOI: 10.48550/arXiv.2008.02479. Davis R. A. Nielsen M. S. 2020 . Modeling of Time Series using Random Forests: Theoretical Developments . ArXiv:2008.02479. 10.48550/arXiv.2008.02479 . Open DOISearch in Google Scholar

Deng, S., Huang, X., Wang, J., Qin, Z., Fu, Z., Wang, A. and Yang, T. 2021. A Decision Support System for Trading in Apple Futures Market Using Predictions Fusion. IEEE Access, 9, 1271–1285. DOI: 10.1109/access.2020.3047138. Deng S. Huang X. Wang J. Qin Z. Fu Z. Wang A. Yang T. 2021 . A Decision Support System for Trading in Apple Futures Market Using Predictions Fusion . IEEE Access , 9 , 1271 1285 . 10.1109/access.2020.3047138 . Open DOISearch in Google Scholar

Deshpande, V. 2023. Implementation of Long Short-Term Memory (LSTM) Networks for Stock Price Prediction. Research Journal of Computer Systems and Engineering, 4 (2), 60–72. DOI: 10.52710/rjcse.74. Deshpande V. 2023 . Implementation of Long Short-Term Memory (LSTM) Networks for Stock Price Prediction . Research Journal of Computer Systems and Engineering , 4 ( 2 ), 60 72 . 10.52710/rjcse.74 . Open DOISearch in Google Scholar

Dey, P., Hossain, E., Hossain, M. I., Chowdhury, M. A., Alam, M. S., Hossain, M. S. and Andersson, K. 2021. Comparative Analysis of Recurrent Neural Networks in Stock Price Prediction for Different Frequency Domains. Algorithms, 14 (8), 251. DOI: 10.3390/a14080251. Dey P. Hossain E. Hossain M. I. Chowdhury M. A. Alam M. S. Hossain M. S. Andersson K. 2021 . Comparative Analysis of Recurrent Neural Networks in Stock Price Prediction for Different Frequency Domains . Algorithms , 14 ( 8 ), 251 . 10.3390/a14080251 . Open DOISearch in Google Scholar

Dietterich, T. G. 2000. Ensemble Methods in Machine Learning. In Proceedings of the First International Workshop on Multiple Classifier Systems. Lecture Notes in Computer Science, 1857, 1–15. DOI: 10.1007/3-540-45014-9 1. Dietterich T. G. 2000 . Ensemble Methods in Machine Learning . In Proceedings of the First International Workshop on Multiple Classifier Systems. Lecture Notes in Computer Science , 1857 , 1 15 . 10.1007/3-540-45014-9 1 . Open DOISearch in Google Scholar

Diqi, M., Ordiyasa, I. W. and Hamzah, H. 2024. Enhancing Stock Price Prediction Using Stacked Long Short-Term Memory. IT Journal Research & Development, 8 (2), 164–174. DOI: 10.25299/itjrd.2023.13486. Diqi M. Ordiyasa I. W. Hamzah H. 2024 . Enhancing Stock Price Prediction Using Stacked Long Short-Term Memory . IT Journal Research & Development , 8 ( 2 ), 164 174 . 10.25299/itjrd.2023.13486 . Open DOISearch in Google Scholar

Dorogush, A. V., Ershov, V. and Gulin, A. 2018. CatBoost: Gradient Boosting with Categorical Features Support. ArXiv:1810.11363. DOI: 10.48550/arXiv.1810.11363. Dorogush A. V. Ershov V. Gulin A. 2018 . CatBoost: Gradient Boosting with Categorical Features Support . ArXiv:1810.11363. 10.48550/arXiv.1810.11363 . Open DOISearch in Google Scholar

Du, H., Lv, L., Wang, H. and Guo, A. 2024. A Novel Method for Detecting Credit Card Fraud Problems. PLOS One, 19 (3), e0294537. DOI: 10.1371/journal.pone.0294537. Du H. Lv L. Wang H. Guo A. 2024 . A Novel Method for Detecting Credit Card Fraud Problems . PLOS One , 19 ( 3 ), e0294537 . 10.1371/journal.pone.0294537 . Open DOISearch in Google Scholar

Du, S., Hao, D. and Li, X. 2022. Research on Stock Forecasting Based on Random Forest. In 2022 IEEE 2nd International Conference on Data Science and Computer Application, 301–305. DOI: 10.1109/ICDSCA56264.2022.9987903. Du S. Hao D. Li X. 2022 . Research on Stock Forecasting Based on Random Forest . In 2022 IEEE 2nd International Conference on Data Science and Computer Application , 301 305 . 10.1109/ICDSCA56264.2022.9987903 . Open DOISearch in Google Scholar

Faber, T. and Finkenrath, M. 2021. Load Forecasting in District Heating Systems Using Stacked Ensembles of Machine Learning Algorithms. In Proceedings of the 14th International Renewable Energy Storage Conference 2020. DOI: 10.2991/ahe.k.210202.001. Faber T. Finkenrath M. 2021 . Load Forecasting in District Heating Systems Using Stacked Ensembles of Machine Learning Algorithms . In Proceedings of the 14th International Renewable Energy Storage Conference 2020 . 10.2991/ahe.k.210202.001 . Open DOISearch in Google Scholar

Fadzil, M. A. M., Razali, A. A., Zabiri, H. and Hussin, A. H. C. 2024. Investigative Analysis of Automatic Mode Detection for a Lubricant Base Oil Production Plant Using PCA and Machine-Learning Models. ACS Omega, 9 (3), 3525–3540. DOI: 10.1021/acsomega.3c07331. Fadzil M. A. M. Razali A. A. Zabiri H. Hussin A. H. C. 2024 . Investigative Analysis of Automatic Mode Detection for a Lubricant Base Oil Production Plant Using PCA and Machine-Learning Models . ACS Omega , 9 ( 3 ), 3525 3540 . 10.1021/acsomega.3c07331 . Open DOISearch in Google Scholar

Fatima, S. S. W. and Rahimi, A. 2024. A Review of Time-Series Forecasting Algorithms for Industrial Manufacturing Systems. Machines, 12 (6), 380. DOI: 10.3390/machines12060380. Fatima S. S. W. Rahimi A. 2024 . A Review of Time-Series Forecasting Algorithms for Industrial Manufacturing Systems . Machines , 12 ( 6 ), 380 . 10.3390/machines12060380 . Open DOISearch in Google Scholar

Firouzjaee, J. and Khalilian, P. 2024. The Interpretability of LSTM Models for Predicting Oil Company Stocks: Impact of Correlated Features. International Journal of Energy Research, 1–18. DOI: 10.1155/2024/5526692. Firouzjaee J. Khalilian P. 2024 . The Interpretability of LSTM Models for Predicting Oil Company Stocks: Impact of Correlated Features . International Journal of Energy Research , 1 18 . 10.1155/2024/5526692 . Open DOISearch in Google Scholar

Fischer, T. and Krauss, C. 2018. Deep Learning with Long Short-Term Memory Networks for Financial Market Predictions. European Journal of Operational Research, 270 (2), 654–669. DOI: 10.1016/j.ejor.2017.11.054. Fischer T. Krauss C. 2018 . Deep Learning with Long Short-Term Memory Networks for Financial Market Predictions . European Journal of Operational Research , 270 ( 2 ), 654 669 . 10.1016/j.ejor.2017.11.054 . Open DOISearch in Google Scholar

Gajamannage, K. and Park, Y. 2022. Real-time Forecasting of Time Series in Financial Markets Using Sequentially Trained Many-to-one LSTMs. ArXiv:2205.04678. DOI: 10.48550/arxiv.2205.04678. Gajamannage K. Park Y. 2022 . Real-time Forecasting of Time Series in Financial Markets Using Sequentially Trained Many-to-one LSTMs . ArXiv:2205.04678. 10.48550/arxiv.2205.04678 . Open DOISearch in Google Scholar

Gohil, J. and Shah, M. 2022. Application of Big Data in Petroleum Streams. 1st ed. CRC Press. DOI: 10.1201/9781003185710. Gohil J. Shah M. 2022 . Application of Big Data in Petroleum Streams . 1st ed. CRC Press . 10.1201/9781003185710 . Open DOISearch in Google Scholar

Gong, X., Guan, K. and Chen, Q. 2022. The Role of Textual Analysis in Oil Futures Price Forecasting Based on Machine Learning Approach. Journal of Futures Markets, 42 (10), 1987–2017. DOI: 10.1002/fut.22367. Gong X. Guan K. Chen Q. 2022 . The Role of Textual Analysis in Oil Futures Price Forecasting Based on Machine Learning Approach . Journal of Futures Markets , 42 ( 10 ), 1987 2017 . 10.1002/fut.22367 . Open DOISearch in Google Scholar

Gradojevic, N. and Yang, J. 2006. Non‐linear, Non‐parametric, Non‐fundamental Exchange Rate Forecasting. Journal of Forecasting, 25 (4), 227–245. DOI: 10.1002/for.986. Gradojevic N. Yang J. 2006 . Non‐linear, Non‐parametric, Non‐fundamental Exchange Rate Forecasting . Journal of Forecasting , 25 ( 4 ), 227 245 . 10.1002/for.986 . Open DOISearch in Google Scholar

Gupta, R., Modise, M. P. and Uwilingiye, J. 2016. Out-of-Sample Equity Premium Predictability in South Africa: Evidence from a Large Number of Predictors. Emerging Markets Finance and Trade, 52 (8), 1935–1955. DOI: 10.1080/1540496x.2015.1058075. Gupta R. Modise M. P. Uwilingiye J. 2016 . Out-of-Sample Equity Premium Predictability in South Africa: Evidence from a Large Number of Predictors . Emerging Markets Finance and Trade , 52 ( 8 ), 1935 1955 . 10.1080/1540496x.2015.1058075 . Open DOISearch in Google Scholar

Hahn, W. J., Dyer, J. and Brandão, L. E. 2007. Using Decision Analysis to Solve Real Option Valuation Problems: Building a Generalized Approach. In Hydrocarbon Economics and Evaluation Symposium. DOI: 10.2118/108066-MS. Hahn W. J. Dyer J. Brandão L. E. 2007 . Using Decision Analysis to Solve Real Option Valuation Problems: Building a Generalized Approach . In Hydrocarbon Economics and Evaluation Symposium . 10.2118/108066-MS . Open DOISearch in Google Scholar

Hao, J., Feng, Q. Q., Li, J. and Sun, X. 2023. A Bi‐Level Ensemble Learning Approach to Complex Time Series Forecasting: Taking Exchange Rates as an Example. Journal of Forecasting, 42 (6), 1385–1406. DOI: 10.1002/for.2971. Hao J. Feng Q. Q. Li J. Sun X. 2023 . A Bi‐Level Ensemble Learning Approach to Complex Time Series Forecasting: Taking Exchange Rates as an Example . Journal of Forecasting , 42 ( 6 ), 1385 1406 . 10.1002/for.2971 . Open DOISearch in Google Scholar

Henrique, B. M., Sobreiro, V. A. and Kimura, H. 2023. Practical Machine Learning: Forecasting Daily Financial Markets Directions. Expert Systems with Applications, 233 (2), 120840. DOI: 10.1016/j.eswa.2023.120840. Henrique B. M. Sobreiro V. A. Kimura H. 2023 . Practical Machine Learning: Forecasting Daily Financial Markets Directions . Expert Systems with Applications , 233 ( 2 ), 120840 . 10.1016/j.eswa.2023.120840 . Open DOISearch in Google Scholar

Heo, J. and Yang, J. J. 2014. Bankruptcy Forecasting Model Using AdaBoost: A Focus on Construction Companies. Journal of Intelligence and Information Systems, 20 (1), 35–48. DOI: 10.13088/jiis.2014.20.1.035. Heo J. Yang J. J. 2014 . Bankruptcy Forecasting Model Using AdaBoost: A Focus on Construction Companies . Journal of Intelligence and Information Systems , 20 ( 1 ), 35 48 . 10.13088/jiis.2014.20.1.035 . Open DOISearch in Google Scholar

Ho, M. K., Darman, H. and Musa, S. 2021. Stock Price Prediction Using ARIMA, Neural Network and LSTM Models. Journal of Physics: Conference Series, 1988 (1), 012041. DOI: 10.1088/1742-6596/1988/1/012041. Ho M. K. Darman H. Musa S. 2021 . Stock Price Prediction Using ARIMA, Neural Network and LSTM Models . Journal of Physics: Conference Series , 1988 ( 1 ), 012041 . 10.1088/1742-6596/1988/1/012041 . Open DOISearch in Google Scholar

Höbarth, L. L. 2006. Modeling the Relationship Between Financial Indicators and Company Performance. An Empirical Study for USListed Companies. Doctoral thesis. Institute for Statistics and Mathematics. DOI: 10.57938/f5f31af3-19a7-488e-9497-e8998438e049. Höbarth L. L. 2006 . Modeling the Relationship Between Financial Indicators and Company Performance. An Empirical Study for USListed Companies . Doctoral thesis. Institute for Statistics and Mathematics . 10.57938/f5f31af3-19a7-488e-9497-e8998438e049 . Open DOISearch in Google Scholar

Hochreiter, S. and Schmidhuber, J. 1997. Long Short-Term Memory. Neural Computation, 9 (8), 1735–1780. DOI: 10.1162/neco.1997.9.8.1735. Hochreiter S. Schmidhuber J. 1997 . Long Short-Term Memory . Neural Computation , 9 ( 8 ), 1735 1780 . 10.1162/neco.1997.9.8.1735 . Open DOISearch in Google Scholar

Hoque, K. E. and Aljamaan, H. 2021. Impact of Hyperparameter Tuning on Machine Learning Models in Stock Price Forecasting. IEEE Access, 9, 163815–163830. DOI: 10.1109/access.2021.3134138. Hoque K. E. Aljamaan H. 2021 . Impact of Hyperparameter Tuning on Machine Learning Models in Stock Price Forecasting . IEEE Access , 9 , 163815 163830 . 10.1109/access.2021.3134138 . Open DOISearch in Google Scholar

Hossain, M. F., Islam, S., Chakraborty, P. and Majumder, A. K. 2020. Predicting Daily Closing Prices of Selected Shares of Dhaka Stock Exchange (DSE) Using Support Vector Machines. Internet of Things and Cloud Computing, 8 (4), 46–51. DOI: 10.11648/j.iotcc.20200804.12. Hossain M. F. Islam S. Chakraborty P. Majumder A. K. 2020 . Predicting Daily Closing Prices of Selected Shares of Dhaka Stock Exchange (DSE) Using Support Vector Machines . Internet of Things and Cloud Computing , 8 ( 4 ), 46 51 . 10.11648/j.iotcc.20200804.12 . Open DOISearch in Google Scholar

Iqbal, R., Doctor, F., More, B., Mahmud, S. and Yousuf, U. 2020. Big Data Analytics: Computational Intelligence Techniques and Application Areas. Technological Forecasting and Social Change, 153, 119253. DOI: 10.1016/j.techfore.2018.03.024. Iqbal R. Doctor F. More B. Mahmud S. Yousuf U. 2020 . Big Data Analytics: Computational Intelligence Techniques and Application Areas . Technological Forecasting and Social Change , 153 , 119253 . 10.1016/j.techfore.2018.03.024 . Open DOISearch in Google Scholar

Islam, S., Sikder, M. S., Hossain, M. F. and Chakraborty, P. 2021. Predicting the Daily Closing Price of Selected Shares on the Dhaka Stock Exchange Using Machine Learning Techniques. SN Business & Economics, 1 (4), 58. DOI: 10.1007/s43546-021-00065-6. Islam S. Sikder M. S. Hossain M. F. Chakraborty P. 2021 . Predicting the Daily Closing Price of Selected Shares on the Dhaka Stock Exchange Using Machine Learning Techniques . SN Business & Economics , 1 ( 4 ), 58 . 10.1007/s43546-021-00065-6 . Open DOISearch in Google Scholar

Jan, C.-L. 2021. Financial Information Asymmetry: Using Deep Learning Algorithms to Predict Financial Distress. Symmetry, 13 (3), 443. DOI: 10.3390/sym13030443. Jan C.-L. 2021 . Financial Information Asymmetry: Using Deep Learning Algorithms to Predict Financial Distress . Symmetry , 13 ( 3 ), 443 . 10.3390/sym13030443 . Open DOISearch in Google Scholar

Jannink, J. W. and Bos, C. F. M. 2005. Probabilistic Discharge Forecasting for Improved Asset Investment Decision Support. In SPE Europec/EAGE Annual Conference. DOI: 10.2118/94116-MS. Jannink J. W. Bos C. F. M. 2005 . Probabilistic Discharge Forecasting for Improved Asset Investment Decision Support . In SPE Europec/EAGE Annual Conference . 10.2118/94116-MS . Open DOISearch in Google Scholar

Johari, S. N. M., Farid, F. H. M., Nasrudin, N. A. E. B., Bistamam, N. S. L. and Shuhaili, N. S. S. M. 2018. Predicting Stock Market Index Using Hybrid Intelligence Model. International Journal of Engineering and Technology, 7 (3.15), 36–39. DOI: 10.14419/ijet.v7i3.15.17403. Johari S. N. M. Farid F. H. M. Nasrudin N. A. E. B. Bistamam N. S. L. Shuhaili N. S. S. M. 2018 . Predicting Stock Market Index Using Hybrid Intelligence Model . International Journal of Engineering and Technology , 7 ( 3.15 ), 36 39 . 10.14419/ijet.v7i3.15.17403 . Open DOISearch in Google Scholar

Jones, S. 2017. Corporate Bankruptcy Prediction: A High Dimensional Analysis. Review of Accounting Studies, 22 (3), 1366–1422. DOI: 10.1007/s11142-017-9407-1. Jones S. 2017 . Corporate Bankruptcy Prediction: A High Dimensional Analysis . Review of Accounting Studies , 22 ( 3 ), 1366 1422 . 10.1007/s11142-017-9407-1 . Open DOISearch in Google Scholar

Jordan, M. I. and Mitchell, T. M. 2015. Machine Learning: Trends, Perspectives, and Prospects. Science, 349 (6245), 255–260. DOI: 10.1126/science.aaa8415. Jordan M. I. Mitchell T. M. 2015 . Machine Learning: Trends, Perspectives, and Prospects . Science , 349 ( 6245 ), 255 260 . 10.1126/science.aaa8415 . Open DOISearch in Google Scholar

Jordan, S. J., Vivian, A. and Wohar, M. E. 2017. Forecasting Market Returns: Bagging or Combining? International Journal of Forecasting, 33 (1), 102–120. DOI: 10.1016/j.ijforecast.2016.07.003. Jordan S. J. Vivian A. Wohar M. E. 2017 . Forecasting Market Returns: Bagging or Combining? International Journal of Forecasting , 33 ( 1 ), 102 120 . 10.1016/j.ijforecast.2016.07.003 . Open DOISearch in Google Scholar

Kambale, W. V., Salem, M., Benarbia, T., Al Machot, F. and Kyamakya, K. (2024). Comprehensive Sensitivity Analysis Framework for Transfer Learning Performance Assessment for Time Series Forecasting: Basic Concepts and Selected Case Studies. Symmetry, 16 (2), 241. DOI: 10.3390/sym16020241. Kambale W. V. Salem M. Benarbia T. Al Machot F. Kyamakya K. ( 2024 ). Comprehensive Sensitivity Analysis Framework for Transfer Learning Performance Assessment for Time Series Forecasting: Basic Concepts and Selected Case Studies . Symmetry , 16 ( 2 ), 241 . 10.3390/sym16020241 . Open DOISearch in Google Scholar

Karminsky, A. M. and Burekhin, R. N. 2019. Comparative Analysis of Methods for Forecasting Bankruptcies of Russian Construction Companies. Business Informatics, 13 (3), 52–66. DOI: 10.17323/1998-0663.2019.3.52.66. Karminsky A. M. Burekhin R. N. 2019 . Comparative Analysis of Methods for Forecasting Bankruptcies of Russian Construction Companies . Business Informatics , 13 ( 3 ), 52 66 . 10.17323/1998-0663.2019.3.52.66 . Open DOISearch in Google Scholar

Kim, T. and Kim, H. Y. 2019. Forecasting Stock Prices with a Feature Fusion LSTM-CNN Model Using Different Representations of the Same Data. PLOS One, 14 (2), e0212320. DOI: 10.1371/journal.pone.0212320. Kim T. Kim H. Y. 2019 . Forecasting Stock Prices with a Feature Fusion LSTM-CNN Model Using Different Representations of the Same Data . PLOS One , 14 ( 2 ), e0212320 . 10.1371/journal.pone.0212320 . Open DOISearch in Google Scholar

Koller, T., Goedhart, M. and Wessels, D. 2010. Valuation: Measuring and Managing the Value of Companies. 5th ed. John Wiley & Sons. ISBN 978-0-470-88996-1. Koller T. Goedhart M. Wessels D. 2010 . Valuation: Measuring and Managing the Value of Companies . 5th ed. John Wiley & Sons . ISBN 978-0-470-88996-1 . Search in Google Scholar

Lee, T.-H., Ullah, A. and Wang, R. 2019. Bootstrap Aggregating and Random Forest. In Fuleky, P. (ed.). Macroeconomic Forecasting in the Era of Big Data: Theory and Practice, pp. 389–429. DOI: 10.1007/978-3-030-31150-6 13. Lee T.-H. Ullah A. Wang R. 2019 . Bootstrap Aggregating and Random Forest . In Fuleky P. (ed.). Macroeconomic Forecasting in the Era of Big Data: Theory and Practice , pp. 389 429 . 10.1007/978-3-030-31150-6 13 . Open DOISearch in Google Scholar

Li, C., Chan, Y., Kazmi, S. H. A. and Fu, H. 2015. Financial Fraud Detection Model: Based on Random Forest. International Journal of Economics and Finance, 7 (7), 178–188. DOI: 10.5539/ijef.v7n7p178. Li C. Chan Y. Kazmi S. H. A. Fu H. 2015 . Financial Fraud Detection Model: Based on Random Forest . International Journal of Economics and Finance , 7 ( 7 ), 178 188 . 10.5539/ijef.v7n7p178 . Open DOISearch in Google Scholar

Li, H. 2024a. Optimizing Stock Price Prediction: Exploring LSTM Architectural Parameters in Financial Forecasting. Highlights in Science Engineering and Technology, 85, 1095–1100. DOI: 10.54097/40px3f62. Li H. 2024a . Optimizing Stock Price Prediction: Exploring LSTM Architectural Parameters in Financial Forecasting . Highlights in Science Engineering and Technology , 85 , 1095 1100 . 10.54097/40px3f62 . Open DOISearch in Google Scholar

Li, N. 2024b. Literature Review: Machine Learning in Stock Predictions. Highlights in Business Economics and Management, 24, 853–859. DOI: 10.54097/81x6z947. Li N. 2024b . Literature Review: Machine Learning in Stock Predictions . Highlights in Business Economics and Management , 24 , 853 859 . 10.54097/81x6z947 . Open DOISearch in Google Scholar

Li, Q., Tan, J., Wang, J. and Chen, H. 2020. A Multimodal Event-Driven LSTM Model for Stock Prediction Using Online News. IEEE Transactions on Knowledge and Data Engineering, 33 (10), 3323–3337. DOI: 10.1109/TKDE.2020.2968894. Li Q. Tan J. Wang J. Chen H. 2020 . A Multimodal Event-Driven LSTM Model for Stock Prediction Using Online News . IEEE Transactions on Knowledge and Data Engineering , 33 ( 10 ), 3323 3337 . 10.1109/TKDE.2020.2968894 . Open DOISearch in Google Scholar

Li, R., Ma, M. and Tang, N. 2023. Stock Price Prediction Based on Decision Trees, CNN and LSTM. In Proceedings of the 4th International Conference on Economic Management and Model Engineering. DOI: 10.4108/eai.18-11-2022.2327160. Li R. Ma M. Tang N. 2023 . Stock Price Prediction Based on Decision Trees, CNN and LSTM . In Proceedings of the 4th International Conference on Economic Management and Model Engineering . 10.4108/eai.18-11-2022.2327160 . Open DOISearch in Google Scholar

Liaw, A. and Wiener, M. 2002. Classification and Regression by randomForest. R News, 2/3, 18–22. Liaw A. Wiener M. 2002 . Classification and Regression by randomForest . R News , 2/3 , 18 22 . Search in Google Scholar

Lin, T.-C. 2012. Decision-Based Filter Based on SVM and Evidence Theory for Image Noise Removal. Neural Computing and Applications, 21 (4), 695–703. DOI: 10.1007/s00521-011-0648-9. Lin T.-C. 2012 . Decision-Based Filter Based on SVM and Evidence Theory for Image Noise Removal . Neural Computing and Applications , 21 ( 4 ), 695 703 . 10.1007/s00521-011-0648-9 . Open DOISearch in Google Scholar

Liu, W., Liu, S., Hassan, S. G., Cao, Y., Xu, L., Feng, D., Cao, L., Chen, W., Chen, Y., Guo, J., Liu, T. and Zhang, H. 2023. A Novel Hybrid Model to Predict Dissolved Oxygen for Efficient Water Quality in Intensive Aquaculture. IEEE Access, 11, 29162–29174. DOI: 10.1109/ACCESS.2023.3260089. Liu W. Liu S. Hassan S. G. Cao Y. Xu L. Feng D. Cao L. Chen W. Chen Y. Guo J. Liu T. Zhang H. 2023 . A Novel Hybrid Model to Predict Dissolved Oxygen for Efficient Water Quality in Intensive Aquaculture . IEEE Access , 11 , 29162 29174 . 10.1109/ACCESS.2023.3260089 . Open DOISearch in Google Scholar

Liu, W., Zhang, Y. and Liu, Y. 2022. Attentionbased BiLSTM Model for Stock Price Prediction. In Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition, pp. 257–263. DOI: 10.1145/3573942.3574019. Liu W. Zhang Y. Liu Y. 2022 . Attentionbased BiLSTM Model for Stock Price Prediction . In Proceedings of the 2022 5th International Conference on Artificial Intelligence and Pattern Recognition , pp. 257 263 . 10.1145/3573942.3574019 . Open DOISearch in Google Scholar

Liu, Y., Qin, Z., Li, P. and Wan, T. 2017. Stock Volatility Prediction Using Recurrent Neural Networks with Sentiment Analysis. In Benferhat, S., Tabia, K. and Ali, M. (eds.). Advances in Artificial Intelligence: From Theory to Practice, pp. 192–201. Lecture Notes in Computer Science, 10350. DOI: 10.1007/978-3-319-60042-0 22. Liu Y. Qin Z. Li P. Wan T. 2017 . Stock Volatility Prediction Using Recurrent Neural Networks with Sentiment Analysis . In Benferhat S. Tabia K. Ali M. (eds.). Advances in Artificial Intelligence: From Theory to Practice , pp. 192 201 . Lecture Notes in Computer Science, 10350. 10.1007/978-3-319-60042-0 22 . Open DOISearch in Google Scholar

Lu, J., Ding, Y. and Li, Z. 2023. Optimizing Financial Engineering Time Indicator Using Bionics Computation Algorithm and Neural Network. In Fifth International Conference on Artificial Intelligence and Computer Science. DOI: 10.1117/12.3009279. Lu J. Ding Y. Li Z. 2023 . Optimizing Financial Engineering Time Indicator Using Bionics Computation Algorithm and Neural Network . In Fifth International Conference on Artificial Intelligence and Computer Science . 10.1117/12.3009279 . Open DOISearch in Google Scholar

Lundberg, S. M., Erion, G. G. and Lee, S.-I. 2019. Consistent Individualized Feature Attribution for Tree Ensembles. ArXiv:1802.03888. DOI: 10.48550/arXiv.1802.03888. Lundberg S. M. Erion G. G. Lee S.-I. 2019 . Consistent Individualized Feature Attribution for Tree Ensembles . ArXiv:1802.03888. 10.48550/arXiv.1802.03888 . Open DOISearch in Google Scholar

Luo, T. 2018. Research on Decision-Making of Complex Venture Capital Based on Financial Big Data Platform. Complexity, 1–12. DOI: 10.1155/2018/5170281. Luo T. 2018 . Research on Decision-Making of Complex Venture Capital Based on Financial Big Data Platform . Complexity , 1 12 . 10.1155/2018/5170281 . Open DOISearch in Google Scholar

Margarat, G. S., Kumar, C. S., Rajan, S. and Raj, M. B. 2023. Forecasting Wind Energy Production Using Machine Learning Techniques. E3S Web of Conferences, 387 (2), 01007. DOI: 10.1051/e3sconf/202338701007. Margarat G. S. Kumar C. S. Rajan S. Raj M. B. 2023 . Forecasting Wind Energy Production Using Machine Learning Techniques . E3S Web of Conferences , 387 ( 2 ), 01007 . 10.1051/e3sconf/202338701007 . Open DOISearch in Google Scholar

Mode, G. R. and Hoque, K. A. 2020. Adversarial Examples in Deep Learning for Multivariate Time Series Regression. ArXiv:2009.11911. DOI: 10.48550/arxiv.2009.11911. Mode G. R. Hoque K. A. 2020 . Adversarial Examples in Deep Learning for Multivariate Time Series Regression . ArXiv:2009.11911. 10.48550/arxiv.2009.11911 . Open DOISearch in Google Scholar

Moon, K.-S. and Kim, H. 2019. Performance of Deep Learning in Prediction of Stock Market Volatility. Economic Computation and Economic Cybernetics Studies and Research, 53 (2), 77–92. DOI: 10.24818/18423264/53.2.19.05. Moon K.-S. Kim H. 2019 . Performance of Deep Learning in Prediction of Stock Market Volatility . Economic Computation and Economic Cybernetics Studies and Research , 53 ( 2 ), 77 92 . 10.24818/18423264/53.2.19.05 . Open DOISearch in Google Scholar

Nabi, R. M., Saeed, S. A. M. and Harron, H. 2020. A Novel Approach for Stock Price Prediction Using Gradient Boosting Machine with Feature Engineering (GBM-wFE). Kurdistan Journal of Applied Research, 5 (1), 28–48. DOI: 10.24017/science.2020.1.3. Nabi R. M. Saeed S. A. M. Harron H. 2020 . A Novel Approach for Stock Price Prediction Using Gradient Boosting Machine with Feature Engineering (GBM-wFE) . Kurdistan Journal of Applied Research , 5 ( 1 ), 28 48 . 10.24017/science.2020.1.3 . Open DOISearch in Google Scholar

Nosratabadi, S., Mosavi, A., Duan, P., Ghamisi, P., Filip, F., Band, S. S., Reuter, U., Gama, J. and Gandomi, A. H. 2020. Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods. Mathematics, 8 (10), 1799. DOI: 10.3390/math8101799. Nosratabadi S. Mosavi A. Duan P. Ghamisi P. Filip F. Band S. S. Reuter U. Gama J. Gandomi A. H. 2020 . Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods . Mathematics , 8 ( 10 ), 1799 . 10.3390/math8101799 . Open DOISearch in Google Scholar

Park, M., Lee, M. L. and Lee, J. 2019. Predicting Stock Market Indices Using Classification Tools. Asian Economic and Financial Review, 9 (2), 243–256. DOI: 10.18488/journal.aefr.2019.92.243.256. Park M. Lee M. L. Lee J. 2019 . Predicting Stock Market Indices Using Classification Tools . Asian Economic and Financial Review , 9 ( 2 ), 243 256 . 10.18488/journal.aefr.2019.92.243.256 . Open DOISearch in Google Scholar

Patel, V., Kumar, A. and Yadav, D. 2023. Machine Learning Techniques for Predicting Stock Closing Prices. In 2023 3rd International Conference on Pervasive Computing and Social Networking, pp. 447–452. DOI: 10.1109/ICPCSN58827.2023.00079. Patel V. Kumar A. Yadav D. 2023 . Machine Learning Techniques for Predicting Stock Closing Prices . In 2023 3rd International Conference on Pervasive Computing and Social Networking , pp. 447 452 . 10.1109/ICPCSN58827.2023.00079 . Open DOISearch in Google Scholar

Pedchenko, N., Strilec, V., Kolisnyk, G. M., Dykha, M. and Frolov, S. 2018. Business Angels as an Alternative to Financial Support at the Early Stages of Small Businesses’ Life Cycle. Investment Management and Financial Innovations, 15 (1), 166–179. DOI: 10.21511/imfi.15(1).2018.15. Pedchenko N. Strilec V. Kolisnyk G. M. Dykha M. Frolov S. 2018 . Business Angels as an Alternative to Financial Support at the Early Stages of Small Businesses’ Life Cycle . Investment Management and Financial Innovations , 15 ( 1 ), 166 179 . 10.21511/imfi.15(1).2018.15 . Open DOISearch in Google Scholar

Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V. and Gulin, A. 2017. CatBoost: Unbiased Boosting with Categorical Features. ArXiv:1706.09516. DOI: 10.48550/arxiv.1706.09516. Prokhorenkova L. Gusev G. Vorobev A. Dorogush A. V. Gulin A. 2017 . CatBoost: Unbiased Boosting with Categorical Features . ArXiv:1706.09516. 10.48550/arxiv.1706.09516 . Open DOISearch in Google Scholar

Qi, J., Huang, S., Hu, J., Ni, W. and Chen, H. 2022. Stock Price Prediction in Chinese Stock Markets Based on CNN-GRU-attention Model. In International Symposium on Artificial Intelligence and Robotics 2022, 12508, pp. 182–189. DOI: 10.1117/12.2663261. Qi J. Huang S. Hu J. Ni W. Chen H. 2022 . Stock Price Prediction in Chinese Stock Markets Based on CNN-GRU-attention Model . In International Symposium on Artificial Intelligence and Robotics 2022 , 12508 , pp. 182 189 . 10.1117/12.2663261 . Open DOISearch in Google Scholar

Qin, Q., Wang, Q.-G., Li, J. and Ge, S. S. 2013. Linear and Nonlinear Trading Models with Gradient Boosted Random Forests and Application to Singapore Stock Market. Journal of Intelligent Learning Systems and Applications, 5 (1), 1–10. DOI: 10.4236/jilsa.2013.51001. Qin Q. Wang Q.-G. Li J. Ge S. S. 2013 . Linear and Nonlinear Trading Models with Gradient Boosted Random Forests and Application to Singapore Stock Market . Journal of Intelligent Learning Systems and Applications , 5 ( 1 ), 1 10 . 10.4236/jilsa.2013.51001 . Open DOISearch in Google Scholar

Qiu, J., Wang, B. and Zhou, C. 2020. Forecasting Stock Prices with Long-Short Term Memory Neural Network Based on Attention Mechanism. PLOS One, 15 (1), e0227222. DOI: 10.1371/journal.pone.0227222. Qiu J. Wang B. Zhou C. 2020 . Forecasting Stock Prices with Long-Short Term Memory Neural Network Based on Attention Mechanism . PLOS One , 15 ( 1 ), e0227222 . 10.1371/journal.pone.0227222 . Open DOISearch in Google Scholar

Raut, S. and Shrivas, A. 2024. Analysis & Stock Price Prediction and Forecasting Using Different LSTM Models. International Journal of Scientific Research in Engineering and Management, 8 (4), 1–5. DOI: 10.55041/ijsrem30115. Raut S. Shrivas A. 2024 . Analysis & Stock Price Prediction and Forecasting Using Different LSTM Models . International Journal of Scientific Research in Engineering and Management , 8 ( 4 ), 1 5 . 10.55041/ijsrem30115 . Open DOISearch in Google Scholar

Rawnaq, E., Esmatyar, B., Hamanaka, A., Tasaoka, T. and Shimada, H. 2024. A Comparative Study of Two Tree-Based Models for Predicting Flyrock Velocity at Open Pit Bench Mining. Open Journal of Applied Sciences, 14 (2), 267–287. DOI: 10.4236/ojapps.2024.142019. Rawnaq E. Esmatyar B. Hamanaka A. Tasaoka T. Shimada H. 2024 . A Comparative Study of Two Tree-Based Models for Predicting Flyrock Velocity at Open Pit Bench Mining . Open Journal of Applied Sciences , 14 ( 2 ), 267 287 . 10.4236/ojapps.2024.142019 . Open DOISearch in Google Scholar

Raza, A., Javed, M., Fayad, A. and Khan, A. Y. 2023. Advanced Deep Learning-Based Predictive Modelling for Analyzing Trends and Performance Metrics in Stock Market. Journal of Accounting and Finance in Emerging Economies, 9 (3), 277–294. DOI: 10.26710/jafee.v9i3.2739. Raza A. Javed M. Fayad A. Khan A. Y. 2023 . Advanced Deep Learning-Based Predictive Modelling for Analyzing Trends and Performance Metrics in Stock Market . Journal of Accounting and Finance in Emerging Economies , 9 ( 3 ), 277 294 . 10.26710/jafee.v9i3.2739 . Open DOISearch in Google Scholar

Reddy, D. J., Donald, A. D., Ramana, K. S., Lakshmi, K. S. and Divya, P. C. S. 2020. Cross Entropy Based Long Short Term Memory Recurrent Neural Network Model for Analyzing the Time Series on Stock Market Price. International Journal of Intelligent Engineering & Systems, 13 (2), 259–266. DOI: 10.22266/ijies2020.0430.25. Reddy D. J. Donald A. D. Ramana K. S. Lakshmi K. S. Divya P. C. S. 2020 . Cross Entropy Based Long Short Term Memory Recurrent Neural Network Model for Analyzing the Time Series on Stock Market Price . International Journal of Intelligent Engineering & Systems , 13 ( 2 ), 259 266 . 10.22266/ijies2020.0430.25 . Open DOISearch in Google Scholar

Reddy, V. M., Naveen, D. N., Sundhar, D. N. and Victoria, K. L. 2024. Deep Insights: Revolutionizing Stock Market Predictions with Machine Learning and Deep Learning Techniques. In 2024 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, pp. 1–6. DOI: 10.1109/RAEEUCCI61380.2024.10547777. Reddy V. M. Naveen D. N. Sundhar D. N. Victoria K. L. 2024 . Deep Insights: Revolutionizing Stock Market Predictions with Machine Learning and Deep Learning Techniques . In 2024 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence , pp. 1 6 . 10.1109/RAEEUCCI61380.2024.10547777 . Open DOISearch in Google Scholar

Reel, P. S., Reel, S., Pearson, E., Trucco, E. and Jefferson, E. 2021. Using Machine Learning Approaches for Multi-Omics Data Analysis: A Review. Biotechnology Advances, 49, 107739. DOI: 10.1016/j.biotechadv.2021.107739. Reel P. S. Reel S. Pearson E. Trucco E. Jefferson E. 2021 . Using Machine Learning Approaches for Multi-Omics Data Analysis: A Review . Biotechnology Advances , 49 , 107739 . 10.1016/j.biotechadv.2021.107739 . Open DOISearch in Google Scholar

Refenes, A.-P. N., Burgess, A. N. and Bentz, Y. 1997. Neural Networks in Financial Engineering: A Study in Methodology. IEEE Transactions on Neural Networks, 8 (6), 1222–1267. DOI: 10.1109/72.641449. Refenes A.-P. N. Burgess A. N. Bentz Y. 1997 . Neural Networks in Financial Engineering: A Study in Methodology . IEEE Transactions on Neural Networks , 8 ( 6 ), 1222 1267 . 10.1109/72.641449 . Open DOISearch in Google Scholar

Roberts, P. W. and Dowling, G. R. 2002. Corporate Reputation and Sustained Superior Financial Performance. Strategic Management Journal, 23 (12), 1077–1093. DOI: 10.1002/smj.274. Roberts P. W. Dowling G. R. 2002 . Corporate Reputation and Sustained Superior Financial Performance . Strategic Management Journal , 23 ( 12 ), 1077 1093 . 10.1002/smj.274 . Open DOISearch in Google Scholar

Ryll, L. and Seidens, S. 2019. Evaluating the Performance of Machine Learning Algorithms in Financial Market Forecasting: A Comprehensive Survey. ArXiv:1906.07786. DOI: 10.48550/arxiv.1906.07786. Ryll L. Seidens S. 2019 . Evaluating the Performance of Machine Learning Algorithms in Financial Market Forecasting: A Comprehensive Survey . ArXiv:1906.07786. 10.48550/arxiv.1906.07786 . Open DOISearch in Google Scholar

Selmi, N., Chaabene, S. and Hachicha, N. 2015. Forecasting Returns on a Stock Market Using Artificial Neural Networks and Garch Family Models: Evidence of Stock Market S&P 500. Decision Science Letters, 4 (2), 203–210. DOI: 10.5267/j.dsl.2014.12.002. Selmi N. Chaabene S. Hachicha N. 2015 . Forecasting Returns on a Stock Market Using Artificial Neural Networks and Garch Family Models: Evidence of Stock Market S&P 500 . Decision Science Letters , 4 ( 2 ), 203 210 . 10.5267/j.dsl.2014.12.002 . Open DOISearch in Google Scholar

Shen, Z., Wan, Q. and Leatham, D. J. 2021. Bitcoin Return Volatility Forecasting: A Comparative Study between Garch and RNN. Journal of Risk and Financial Management, 14 (7), 337. DOI: 10.3390/jrfm14070337. Shen Z. Wan Q. Leatham D. J. 2021 . Bitcoin Return Volatility Forecasting: A Comparative Study between Garch and RNN . Journal of Risk and Financial Management , 14 ( 7 ), 337 . 10.3390/jrfm14070337 . Open DOISearch in Google Scholar

Shende, S. D., Singh, A. S., Shah, S. S., Shinde, M. M., More, S. R. and Ainapure, B. 2022. Stocks Price Prediction by Fundamental Analysis Using Machine Learning Algorithms. In 2022 5th International Conference on Contemporary Computing and Informatics, pp. 1515–1522. DOI: 10.1109/IC3I56241.2022.10072563. Shende S. D. Singh A. S. Shah S. S. Shinde M. M. More S. R. Ainapure B. 2022 . Stocks Price Prediction by Fundamental Analysis Using Machine Learning Algorithms . In 2022 5th International Conference on Contemporary Computing and Informatics , pp. 1515 1522 . 10.1109/IC3I56241.2022.10072563 . Open DOISearch in Google Scholar

Singh, S., Ahmad, M., Bhattacharya, A. and Azhagiri, M. 2019. Predicting Stock Market Trends using Hybrid SVM Model and LSTM with Sentiment Determination using Natural Language Processing. International Journal of Engineering and Advanced Technology, 9 (1), 2870–2875. DOI: 10.35940/ijeat.a1106.109119. Singh S. Ahmad M. Bhattacharya A. Azhagiri M. 2019 . Predicting Stock Market Trends using Hybrid SVM Model and LSTM with Sentiment Determination using Natural Language Processing . International Journal of Engineering and Advanced Technology , 9 ( 1 ), 2870 2875 . 10.35940/ijeat.a1106.109119 . Open DOISearch in Google Scholar

Sun, J. 2012. Integration of Random Sample Selection, Support Vector Machines and Ensembles for Financial Risk Forecasting with an Empirical Analysis on the Necessity of Feature Selection. Intelligent Systems in Accounting Finance and Management, 19 (4), 229–246. DOI: 10.1002/isaf.1331. Sun J. 2012 . Integration of Random Sample Selection, Support Vector Machines and Ensembles for Financial Risk Forecasting with an Empirical Analysis on the Necessity of Feature Selection . Intelligent Systems in Accounting Finance and Management , 19 ( 4 ), 229 246 . 10.1002/isaf.1331 . Open DOISearch in Google Scholar

Sun, Y. and Tian, L. 2023. Research on Stock Prediction Based on LSTM and CatBoost Algorithm. In Proceedings of the 2nd International Conference on Bigdata Blockchain and Economy Management. DOI: 10.4108/eai.19-5-2023.2334326. Sun Y. Tian L. 2023 . Research on Stock Prediction Based on LSTM and CatBoost Algorithm . In Proceedings of the 2nd International Conference on Bigdata Blockchain and Economy Management . 10.4108/eai.19-5-2023.2334326 . Open DOISearch in Google Scholar

Tanuwijaya, J. and Hansun, S. 2019. LQ45 Stock Index Prediction Using k-Nearest Neighbors Regression. International Journal of Recent Technology and Engineering, 8 (3), 2388–2391. DOI: 10.35940/ijrte.c4663.098319. Tanuwijaya J. Hansun S. 2019 . LQ45 Stock Index Prediction Using k-Nearest Neighbors Regression . International Journal of Recent Technology and Engineering , 8 ( 3 ), 2388 2391 . 10.35940/ijrte.c4663.098319 . Open DOISearch in Google Scholar

Tsai, C.-F., Lin, Y.-C., Yen, D. C. and Chen, Y.-M. 2011. Predicting Stock Returns by Classifier Ensembles. Applied Soft Computing, 11 (2), 2452–2459. DOI: 10.1016/j.asoc.2010.10.001. Tsai C.-F. Lin Y.-C. Yen D. C. Chen Y.-M. 2011 . Predicting Stock Returns by Classifier Ensembles . Applied Soft Computing , 11 ( 2 ), 2452 2459 . 10.1016/j.asoc.2010.10.001 . Open DOISearch in Google Scholar

Upadhyay, N. K., Singh, V., Singh, S. and Khanna, P. 2023. Enhancing Stock Market Predictability: A Comparative Analysis of RNN and LSTM Models for Retail Investors. Journal of Management and Service Science, 3 (1), 1–9. DOI: 10.54060/jmss.v3i1.42. 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 . Journal of Management and Service Science , 3 ( 1 ), 1 9 . 10.54060/jmss.v3i1.42 . Open DOISearch in Google Scholar

Verma, S. A., Thampi, G. T. and Rao, M. 2017. Inter-Comparison of Artificial Neural Network Algorithms for Time Series Forecasting: Predicting Indian Financial Markets. International Journal of Computer Applications, 162 (2), 1–13. DOI: 10.5120/ijca2017913249. Verma S. A. Thampi G. T. Rao M. 2017 . Inter-Comparison of Artificial Neural Network Algorithms for Time Series Forecasting: Predicting Indian Financial Markets . International Journal of Computer Applications , 162 ( 2 ), 1 13 . 10.5120/ijca2017913249 . Open DOISearch in Google Scholar

Viswanathan, T. and Stephen, M. 2021. Does Machine Learning Algorithms Improve Forecasting Accuracy? Predicting Stock Market Index Using Ensemble Model. In Advances in Distributed Computing and Machine Learning, pp. 511–519. Lecture Notes in Networks and Systems, 127. DOI: 10.1007/978-981-15-4218-3 50. Viswanathan T. Stephen M. 2021 . Does Machine Learning Algorithms Improve Forecasting Accuracy? Predicting Stock Market Index Using Ensemble Model . In Advances in Distributed Computing and Machine Learning , pp. 511 519 . Lecture Notes in Networks and Systems, 127. 10.1007/978-981-15-4218-3 50 . Open DOISearch in Google Scholar

Vochozka, M., Vrbka, J. and Šuleř, P. 2020. Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM. Sustainability, 12 (18), 7529. DOI: 10.3390/su12187529. Vochozka M. Vrbka J. Šuleř P. 2020 . Bankruptcy or Success? The Effective Prediction of a Company’s Financial Development Using LSTM . Sustainability , 12 ( 18 ), 7529 . 10.3390/su12187529 . Open DOISearch in Google Scholar

Wang, J., Rong, W., Zhang, Z. and Mei, D. 2022. Credit Debt Default Risk Assessment Based on the XGBoost Algorithm: An Empirical Study from China. Wireless Communications and Mobile Computing, 2022. DOI: 10.1155/2022/8005493. Wang J. Rong W. Zhang Z. Mei D. 2022 . Credit Debt Default Risk Assessment Based on the XGBoost Algorithm: An Empirical Study from China . Wireless Communications and Mobile Computing , 2022. 10.1155/2022/8005493 . Open DOISearch in Google Scholar

Wang, W. and Wu, Y. 2023. Risk Analysis of the Chinese Financial Market with the Application of a Novel Hybrid Volatility Prediction Model. Mathematics, 11 (18), 3937. DOI: 10.3390/math11183937. Wang W. Wu Y. 2023 . Risk Analysis of the Chinese Financial Market with the Application of a Novel Hybrid Volatility Prediction Model . Mathematics , 11 ( 18 ), 3937 . 10.3390/math11183937 . Open DOISearch in Google Scholar

Wang, Z. 2024. Stock Price Prediction Using LSTM Neural Networks: Techniques and Applications. Applied and Computational Engineering, 86 (1), 275–281. DOI: 10.54254/2755-2721/86/20241605. Wang Z. 2024 . Stock Price Prediction Using LSTM Neural Networks: Techniques and Applications . Applied and Computational Engineering , 86 ( 1 ), 275 281 . 10.54254/2755-2721/86/20241605 . Open DOISearch in Google Scholar

Widiputra, H., Mailangkay, A. and Gautama, E. 2021. Multivariate CNN-LSTM Model for Multiple Parallel Financial Time-Series Prediction. Complexity, 2021, 1–14. DOI: 10.1155/2021/9903518. Widiputra H. Mailangkay A. Gautama E. 2021 . Multivariate CNN-LSTM Model for Multiple Parallel Financial Time-Series Prediction . Complexity , 2021 , 1 14 . 10.1155/2021/9903518 . Open DOISearch in Google Scholar

Wu, Y. and Gao, J. 2018. AdaBoost-Based Long Short-Term Memory Ensemble Learning Approach for Financial Time Series Forecasting. Current Science, 115 (1), 159. DOI: 10.18520/cs/v115/i1/159-165. Wu Y. Gao J. 2018 . AdaBoost-Based Long Short-Term Memory Ensemble Learning Approach for Financial Time Series Forecasting . Current Science , 115 ( 1 ), 159 . 10.18520/cs/v115/i1/159-165 . Open DOISearch in Google Scholar

Xia, Y. and Chen, J. 2017. Traffic Flow Forecasting Method Based on Gradient Boosting Decision Tree. In Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology. DOI: 10.2991/fmsmt-17.2017.87. Xia Y. Chen J. 2017 . Traffic Flow Forecasting Method Based on Gradient Boosting Decision Tree . In Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology . 10.2991/fmsmt-17.2017.87 . Open DOISearch in Google Scholar

Xiuguo, W. and Shengyong, D. 2022. An Analysis on Financial Statement Fraud Detection for Chinese Listed Companies Using Deep Learning. IEEE Access, 10, 22516–22532. DOI: 10.1109/ACCESS.2022.3153478. Xiuguo W. Shengyong D. 2022 . An Analysis on Financial Statement Fraud Detection for Chinese Listed Companies Using Deep Learning . IEEE Access , 10 , 22516 22532 . 10.1109/ACCESS.2022.3153478 . Open DOISearch in Google Scholar

Yadav, G. and Vasuja, R. 2019. Analysis of Time Series Prediction Using Recurrent Neural Networks. International Journal of Computer Applications, 182 (48), 34–40. DOI: 10.5120/ijca2019918732. Yadav G. Vasuja R. 2019 . Analysis of Time Series Prediction Using Recurrent Neural Networks . International Journal of Computer Applications , 182 ( 48 ), 34 40 . 10.5120/ijca2019918732 . Open DOISearch in Google Scholar

Yan, J., Liao, J.-J. and Shih, C.-H. 2015. Multi-Agent Hybrid Mechanism for Financial Risk Management. Journal of Industrial Engineering and Management, 8 (2), 435–452. DOI: 10.3926/jiem.1313. Yan J. Liao J.-J. Shih C.-H. 2015 . Multi-Agent Hybrid Mechanism for Financial Risk Management . Journal of Industrial Engineering and Management , 8 ( 2 ), 435 452 . 10.3926/jiem.1313 . Open DOISearch in Google Scholar

Yang, M. 2023. Predicting the Direction of Stock Price Movement with Machine Learning Algorithms. Advances in Economics, Management and Political Sciences, 52 (1), 283–291. DOI: 10.54254/2754-1169/52/20230758. Yang M. 2023 . Predicting the Direction of Stock Price Movement with Machine Learning Algorithms . Advances in Economics, Management and Political Sciences , 52 ( 1 ), 283 291 . 10.54254/2754-1169/52/20230758 . Open DOISearch in Google Scholar

Yao, X. and Yang, X. 2024. Forecasting Crude Oil Futures Using an Ensemble Model Including Investor Sentiment and Attention. Kybernetes, 53 (12), 6114–6138. DOI: 10.1108/K-03-2023-0364. Yao X. Yang X. 2024 . Forecasting Crude Oil Futures Using an Ensemble Model Including Investor Sentiment and Attention . Kybernetes , 53 ( 12 ), 6114 6138 . 10.1108/K-03-2023-0364 . Open DOISearch in Google Scholar

Yaprakdal, F. and Bal, F. 2022. Comparison of Robust Machine-Learning and Deep-Learning Models for Midterm Electrical Load Forecasting. European Journal of Technique, 12 (2), 102–107. DOI: 10.36222/ejt.1201977. Yaprakdal F. Bal F. 2022 . Comparison of Robust Machine-Learning and Deep-Learning Models for Midterm Electrical Load Forecasting . European Journal of Technique , 12 ( 2 ), 102 107 . 10.36222/ejt.1201977 . Open DOISearch in Google Scholar

Zahrah, H. H., Sa’adah, S. and Rismala, R. 2021. The Foreign Exchange Rate Prediction Using Long-Short Term Memory. International Journal on Information and Communication Technology, 6 (2), 94–105. DOI: 10.21108/ijoict.2020.62.538. Zahrah H. H. Sa’adah S. Rismala R. 2021 . The Foreign Exchange Rate Prediction Using Long-Short Term Memory . International Journal on Information and Communication Technology , 6 ( 2 ), 94 105 . 10.21108/ijoict.2020.62.538 . Open DOISearch in Google Scholar

Zhang, X. 2022. A Model Combining LightGBM and Neural Network for High-Frequency Realized Volatility Forecasting. In Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development. DOI: 10.2991/aebmr.k.220307.473. Zhang X. 2022 . A Model Combining LightGBM and Neural Network for High-Frequency Realized Volatility Forecasting . In Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development . 10.2991/aebmr.k.220307.473 . Open DOISearch in Google Scholar

Zhou, L. and Lai, K. K. 2016. AdaBoost Models for Corporate Bankruptcy Prediction with Missing Data. Computational Economics, 50 (1), 69–94. DOI: 10.1007/s10614-016-9581-4. Zhou L. Lai K. K. 2016 . AdaBoost Models for Corporate Bankruptcy Prediction with Missing Data . Computational Economics , 50 ( 1 ), 69 94 . 10.1007/s10614-016-9581-4 . Open DOISearch in Google Scholar

Zhou, Z., Song, Z., Ren, T. and Yu, L. 2023. Two-Stage Portfolio Optimization Integrating Optimal Sharp Ratio Measure and Ensemble Learning. IEEE Access, 11, 1654–1670. DOI: 10.1109/access.2022.3232281. Zhou Z. Song Z. Ren T. Yu L. 2023 . Two-Stage Portfolio Optimization Integrating Optimal Sharp Ratio Measure and Ensemble Learning . IEEE Access , 11 , 1654 1670 . 10.1109/access.2022.3232281 . Open DOISearch in Google Scholar

Zhu, C., Beatty, T., Zhao, Q., Si, W. and Chen, Q. 2023. Leveraging Genetic Data for Predicting Consumer Choices of Alcoholic Products. China Agricultural Economic Review, 15 (4), 685–707. DOI: 10.1108/caer-09-2022-0214. Zhu C. Beatty T. Zhao Q. Si W. Chen Q. 2023 . Leveraging Genetic Data for Predicting Consumer Choices of Alcoholic Products . China Agricultural Economic Review , 15 ( 4 ), 685 707 . 10.1108/caer-09-2022-0214 . Open DOISearch in Google Scholar

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