Applications of Li Filters and Neural Networks in Stock System State Estimation and Profit-Loss Trend Prediction

This article explores the application of Li filters and neural networks in stock system state estimation and profit-loss trend prediction. The Li filter, a widely-used signal processing technique, enables estimation of stock prices and market trends through mathematical modeling. It operates by analyzing historical data to forecast future price movements, typically implemented using state-space equations and Kalman filter variants for real-time data smoothing. Neural networks, computational models simulating biological nervous systems, are employed for profit-loss trend estimation in stock systems. These networks learn from extensive historical data and relevant factors (e.g., trading volume, technical indicators) to predict profitability patterns, often using multilayer perceptron (MLP) architectures with backpropagation training algorithms. The integration of these technologies provides investors with enhanced market insight, serving as decision-making references to improve investment success rates and profitability through algorithmic trend analysis and predictive modeling.