MACHINE LEARNING ALGORITHMS FOR PREDICTIVE ANALYTICS IN FINANCIAL MARKETS
DOI:
https://doi.org/10.53555/eijms.v11i1.78Keywords:
Machine Learning, Financial Forecasting, Synthetic Data, Algorithmic Trading, Deep LearningAbstract
A research examines machine learning algorithm applications for financial data prediction in markets by using synthetic data to minimize reliance on traditional datasets. The study develops a hybrid framework that combines deep learning and reinforcement learning models to enhance forecasting precision. GANs and VAEs work together to generate synthetic financial data through advanced network architectures. The prepared data is improved through logarithmic returns and technical indicator techniques. Transformer-based models deliver better performance than LSTM networks because they decrease prediction errors by 12% compared to LSTM networks. At the same time Deep Q-Networks (DQNs) operating under the reinforcement learning framework generate 23% superior cumulative returns compared to typical trading methods. Research outcomes indicate that artificial intelligence-driven models can improve market predictions and company decision-making functions. The research develops a new synthetic data method that boosts financial prediction while delivering essential information for automated traders and financial establishments.
References
F. A. Gers, J. Schmidhuber, and F. Cummins, "Learning to Forget: Continual Prediction with LSTM," Neural Computation, vol. 12, no. 10, pp. 2451-2471, 2000.
R. Cont, "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, vol. 1, no. 2, pp. 223-236, 2001.
G. E. P. Box and G. M. Jenkins, Time Series Analysis: Forecasting and Control, Holden-Day, 1976.
Y. Bengio, A. Courville, and P. Vincent, "Representation Learning: A Review and New Perspectives," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1798-1828, 2013.
M. Dixon, D. Klabjan, and J. H. Bang, "Classification-Based Financial Markets Prediction Using Deep Neural Networks," Algorithmic Finance, vol. 6, no. 3-4, pp. 67-77, 2017.
Goodfellow et al., "Generative Adversarial Networks," Advances in Neural Information Processing Systems (NeurIPS), 2014.
J. B. Heaton, N. G. Polson, and J. H. Witte, "Deep Learning in Finance," Annual Review of Financial Economics, vol. 8, no. 1, pp. 63-78, 2016.
T. Fischer and C. Krauss, "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, vol. 270, no. 2, pp. 654-669, 2018.
Radford et al., "Learning Transferable Visual Models From Natural Language Supervision," International Conference on Machine Learning (ICML), 2021.
D. Silver et al., "Mastering the game of Go with deep neural networks and tree search," Nature, vol. 529, no. 7587, pp. 484-489, 2016.
Sahu, S. K., Mokhade, A., & Bokde, N. D. (2023). An overview of machine learning, deep learning, and reinforcement learning-based techniques in quantitative finance: recent progress and challenges. Applied Sciences, 13(3), 1956.
S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997.
Hossain, E., Hossain, M. S., Zander, P. O., & Andersson, K. (2022). Machine learning with Belief Rule-Based Expert Systems to predict stock price movements. Expert Systems with Applications, 206, 117706.
M. Mnih et al., "Human-level control through deep reinforcement learning," Nature, vol. 518, no. 7540, pp. 529-533, 2015.
Lee, J., Stevens, N., & Han, S. C. (2025). Large Language Models in Finance (FinLLMs). Neural Computing and Applications, 1-15.
Perera, H., & Costa, L. (2023, July 28). PERSONALITY CLASSIFICATION OF TEXT THROUGH MACHINE LEARNING AND DEEP LEARNING: A REVIEW (2023). International Journal for Research in Advanced Computer Science and Engineering, 9(4), 6–12. https://doi.org/10.53555/cse.v9i4.2266
C. Chatfield, Time-Series Forecasting, Chapman & Hall/CRC, 2020.
J. Huang, Y. Guo, and X. Chen, "XGBoost-based feature selection and prediction model for stock price movement," Applied Soft Computing, vol. 134, p. 109967, 2023.
Mozaffari, L. (2024). Stock Market Time Series Forecasting using Transformer Models (Master's thesis, Oslo Metropolitan University).
S. Arjovsky, M. Dumoulin, and Y. Bengio, "Wasserstein Generative Adversarial Networks," International Conference on Machine Learning (ICML), 2017.
Vuletić, M., Prenzel, F., & Cucuringu, M. (2024). Fin-gan: Forecasting and classifying financial time series via generative adversarial networks. Quantitative Finance, 24(2), 175-199.
Li, C., Welling, M., Zhu, J., & Zhang, B. (2018). Graphical generative adversarial networks. Advances in neural information processing systems, 31.
Wan, Z., Zhang, Y., & He, H. (2017, November). Variational autoencoder based synthetic data generation for imbalanced learning. In 2017 IEEE symposium series on computational intelligence (SSCI) (pp. 1-7). IEEE.
Maeda, I., DeGraw, D., Kitano, M., Matsushima, H., Sakaji, H., Izumi, K., & Kato, A. (2020). Deep reinforcement learning in agent based financial market simulation. Journal of Risk and Financial Management, 13(4), 71.
