The Application of Artificial Intelligence in Financial Prediction

Abstract

This paper explores the application of artificial intelligence (AI) in financial forecasting and its associated challenges. AI leverages technologies such as natural language processing (NLP) and long short-term memory (LSTM) networks, and probabilistic models to process complex data, overcoming the limitations of traditional models and improving prediction accuracy (15%-20%). However, AI in financial forecasting still faces issues such as data noise (e.g., 35% signal distortion in social media), cross-market adaptability (e.g., differences between A-shares and U.S. stocks), real-time delays (e.g., 2-second lags), and model transparency. The study also examines AI's specific applications in technical indicators, sentiment analysis, and policy assessment, proposing solutions such as knowledge graph verification, dynamic parameter adjustment, and edge computing. The results showed that hybrid AI models (e.g., Transformer+LSTM-GARCH) achieve up to 91.2% accuracy in policy classification and reduce latency to 500 milliseconds, while sentiment analysis combined with technical indicators enhances high-frequency trading returns by 38.7% annually. However, challenges such as social media noise (35% distortion) and cross-market adaptation gaps persist. To address these, we recommend integrating knowledge graphs for data validation (reducing noise by 20%), adopting dynamic parameter tuning for market-specific adjustments, and employing edge computing to minimize latency. In conclusion, AI demonstrates significant advantages in financial forecasting, with accuracy improvements of 12%-20% over traditional methods. Its successful implementation requires balancing technological innovation (e.g., field-programmable gate array [FPGA] acceleration), cost efficiency (e.g., open-source tools for retail investors), and regulatory compliance (e.g., SHAP/LIME for transparency). Future work should focus on adaptive frameworks for black swan events and cost-effective solutions for broader market adoption.