SVM-Based Regression for Opening Price Prediction

Model Objective: Develop a regression model using Support Vector Machines (SVM) to predict daily opening prices of the SSE Composite Index through regression fitting. Model Assumption: The daily opening price of the SSE Composite Index is assumed to correlate with the previous day's opening price, highest value, lowest value, closing price, trading volume, and trading amount. These six indicators serve as independent variables, while the current day's opening price functions as the dependent variable. Implementation involves feature engineering to normalize these financial indicators and employing SVM regression algorithms (such as SVR) with parameter optimization for accurate time-series forecasting.