Application of Support Vector Machine Regression in Stock Market Prediction

The application of Support Vector Machine Regression (SVR) in stock market prediction is extensively utilized in financial analytics. Beyond academic research documented in papers, numerous simulation curves validate its effectiveness and accuracy in practical scenarios. This machine learning method analyzes historical market data to forecast stock price movements, assisting investors in making more informed decisions. The implementation typically involves feature selection from time-series data, kernel function optimization (such as RBF or polynomial kernels), and parameter tuning using techniques like grid search or cross-validation. Key functions include data normalization, support vector identification, and margin optimization for regression tasks. Additionally, SVR demonstrates strong performance in other prediction domains such as weather forecasting and sales prediction, with advantages including high accuracy, stability against overfitting, and robust handling of non-linear relationships, providing reliable references for decision-makers across various industries.