SVM-Based Regression Prediction Analysis: Predicting Shanghai Composite Index Opening Prices

Support Vector Machines (SVM) can be applied to both classification and regression prediction tasks. This case study demonstrates SVM implementation for regression analysis to predict stock market indices. Effective prediction of major indices provides crucial insights for observing overall market trends, making Shanghai Composite Index forecasting particularly valuable. Using daily opening prices from 1990.12.20 to 2009.08.19, the SVM regression model achieved impressive results: Mean Squared Error (MSE) = 1.95029e-005 and R-squared coefficient R = 99.9345%, indicating highly accurate fitting. Key implementation involves using SVM regression algorithms (like SVR) with appropriate kernel functions and parameter optimization.

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.