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

Open Access
|Sep 2025

References

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. Breiman, L. 2001. Random Forests. Machine Learning, 45, 5–32. DOI: 10.1023/A:1010933404324.
  20. 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.
  21. 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.
  22. 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.
  23. 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.
  24. 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.
  25. 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.
  26. 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.
  27. 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.
  28. 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.
  29. 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.
  30. 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.
  31. 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.
  32. Chen, Z. 2024. Stock Price Prediction with Denoising Autoencoder and Transformers. Highlights in Science, Engineering and Technology, 85, 803–810. DOI: 10.54097/1skct023.
  33. 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.
  34. 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.
  35. Chow, J. C. K. 2018. Analysis of Financial Credit Risk Using Machine Learning. ArXiv:1802.05326. DOI: 10.48550/arXiv.1802.05326.
  36. 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.
  37. 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.
  38. 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.
  39. 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.
  40. 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.
  41. 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.
  42. 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.
  43. 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.
  44. 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.
  45. 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.
  46. 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.
  47. 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.
  48. 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.
  49. 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.
  50. 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.
  51. 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.
  52. 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.
  53. Gohil, J. and Shah, M. 2022. Application of Big Data in Petroleum Streams. 1st ed. CRC Press. DOI: 10.1201/9781003185710.
  54. 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.
  55. 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.
  56. 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.
  57. 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.
  58. 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.
  59. 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.
  60. 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.
  61. 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.
  62. 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.
  63. Hochreiter, S. and Schmidhuber, J. 1997. Long Short-Term Memory. Neural Computation, 9 (8), 1735–1780. DOI: 10.1162/neco.1997.9.8.1735.
  64. 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.
  65. 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.
  66. 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.
  67. 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.
  68. Jan, C.-L. 2021. Financial Information Asymmetry: Using Deep Learning Algorithms to Predict Financial Distress. Symmetry, 13 (3), 443. DOI: 10.3390/sym13030443.
  69. 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.
  70. 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.
  71. 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.
  72. Jordan, M. I. and Mitchell, T. M. 2015. Machine Learning: Trends, Perspectives, and Prospects. Science, 349 (6245), 255–260. DOI: 10.1126/science.aaa8415.
  73. 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.
  74. 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.
  75. 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.
  76. 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.
  77. 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.
  78. 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.
  79. 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.
  80. 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.
  81. Li, N. 2024b. Literature Review: Machine Learning in Stock Predictions. Highlights in Business Economics and Management, 24, 853–859. DOI: 10.54097/81x6z947.
  82. 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.
  83. 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.
  84. Liaw, A. and Wiener, M. 2002. Classification and Regression by randomForest. R News, 2/3, 18–22.
  85. 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.
  86. 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.
  87. 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.
  88. 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.
  89. 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.
  90. 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.
  91. 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.
  92. 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.
  93. 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.
  94. 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.
  95. 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.
  96. 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.
  97. 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.
  98. 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.
  99. 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.
  100. 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.
  101. 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.
  102. 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.
  103. 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.
  104. 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.
  105. 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.
  106. 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.
  107. 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.
  108. 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.
  109. 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.
  110. 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.
  111. 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.
  112. 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.
  113. 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.
  114. 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.
  115. 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.
  116. 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.
  117. 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.
  118. 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.
  119. 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.
  120. 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.
  121. 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.
  122. 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.
  123. 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.
  124. 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.
  125. 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.
  126. 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.
  127. 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.
  128. 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.
  129. 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.
  130. 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.
  131. 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.
  132. 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.
  133. 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.
  134. 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.
  135. 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.
  136. 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.
  137. 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.
  138. 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.
  139. 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.
  140. 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.
  141. 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.
DOI: https://doi.org/10.2478/revecp-2025-0005 | Journal eISSN: 1804-1663 | Journal ISSN: 1213-2446
Language: English
Page range: 66 - 90
Published on: Sep 8, 2025
Published by: Mendel University in Brno
In partnership with: Paradigm Publishing Services
Publication frequency: 2 times per year

© 2025 Cansu Ergenç, Rafet Aktaş, published by Mendel University in Brno
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