Факультет інформатики

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Browsing Факультет інформатики by Author "Voitishyn, Mykyta"

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    Hybrid AI Model for Financial Market Prediction
    (2025) Voitishyn, Mykyta; Kuzmenko, Dmytro
    Financial time series modeling is increasingly complex due to volatility, unexpected breakouts, and the impact of external factors, such as macroeconomic indicators, investor sentiment, company fundamentals, and extreme shocks, like geopolitical events or market manipulations. This paper introduces a hybrid artificial intelligence framework that integrates traditional statistical methods, machine learning models, and Bayesian neural networks (BNNs) to improve predictive performance and uncertainty quantification in financial forecasting. The model leverages a variety of engineered features, including rolling statistics, technical indicators, anomaly scores, interpolated macroeconomic data, and transformer-based sentiment scores. A complete ablation study compares various architectures, including ARIMA, SARIMA, MLR, SNN, and BNN, across multiple prediction windows (1, 3, 5 days) and feature combinations. Results show that while linear models yield the lowest MSE for short-term predictions, they fail to capture non-linear dependencies and uncertainty. In contrast, BNNs offer more reliable mid-term predictions by estimating predictive distributions. The best BNN configuration (Normal distribution, constant variation, TanH activation, 1 hidden layer) achieved an MSE of 0.00022, confirming the advantage of uncertainty-adjusted modeling. Sentiment analysis and anomaly detection were especially impactful when combined with macroeconomic indicators, improving signal reliability and behavioral insight. Our findings highlight the importance of integrating diverse data sources and accounting for predictive uncertainty in financial applications. Additionally, the experiments revealed that compact network architectures often outperform deeper ones when paired with engineered features. All experiments were systematically tracked to ensure reproducibility and facilitate future model benchmarking.