SVM-Based Regression for Opening Price Prediction

In this study, we aim to construct a regression model using Support Vector Machines (SVM) to perform regression fitting on the daily opening prices of the SSE Composite Index. The model operates under the hypothesis that the daily opening price correlates with the previous day's opening price, highest value, lowest value, closing price, trading volume, and trading amount. Consequently, these six historical indicators are treated as independent variables for predicting the current day's opening price as the dependent variable. From an implementation perspective, the workflow would involve: 1. Data preprocessing: Normalizing financial indicators to mitigate scale differences using techniques like Min-Max scaling or Z-score standardization. 2. Feature selection: Utilizing correlation analysis or recursive feature elimination to validate the relevance of the six predefined variables. 3. Model training: Applying SVM regression (e.g., via scikit-learn's SVR class) with kernel functions (e.g., RBF or linear) to capture non-linear relationships, coupled with cross-validation for hyperparameter tuning (C, gamma, epsilon). 4. Performance evaluation: Assessing prediction accuracy through metrics like Mean Absolute Error (MAE) or Root Mean Squared Error (RMSE) on time-series split test sets. We believe this model will enhance the understanding and forecasting of SSE Composite Index opening trends, providing investors with more reliable decision-support tools for financial analysis.