Development of an Intelligent Software Platform for Forecasting Market Trends and Stock Prices Based on the Analysis of Big Financial Data and News Feeds
Year: 2026
Type: conference-paper
Abstract: Currently, there is a fundamental problem of information noise and high data dimensionality in financial markets. Traders and automated systems face the problem of the non-stationary nature of stock prices, which makes traditional methods of market analysis and forecasting ineffective. This study presents the development of an intelligent software platform for short-horizon stock price movement forecasting based on financial indicators and news sentiment analysis. The system integrates feature engineering, dimensionality reduction via Principal Component Analysis (PCA), and machine learning classification models. A multi-horizon evaluation (1-day, 3day, and 5-day ahead prediction) was conducted to assess time-scale sensitivity. Experimental results indicate that predictive performance remains close to random classification levels (ROC-AUC$\approx 0.47 - 0.51$) across all horizons. These findings highlight the inherent stochastic nature of short-term financial markets and confirm the difficulty of extracting stable directional signals from publicly available information.
Institution: Astana IT University
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7168242866