Some Efficient Stock Price Trend Prediction Based on Multi-category Textual Information and Support Vector Machines

For a selected portfolio of large-cap blue-chip stocks in China A-share market, this study selects and quantifies three categories of textual information with comparatively notably low average daily volume: responses from Secretaries of the Boards of Listed Companies (RSB), Comments by Internet Influencers on Listed Companies (CII), and Official Press Releases of High-level Meetings of the Communist Party of China and Central Government (R-M-P&G). Then, for every category of textual information, the predictive role of a Gaussian Kernel Learning-Support Vector Machine(GKL-SVM) model, employing the information independently, is explored and assessed on stock (price) trends. Based on a comparative analysis of A-share market transaction data for this investment portfolio over the past three years, the GKL-SVM model, individually utilizing any type of textual information, demonstrates some enhancement in predicting short-term trends of Daily Closing (DC) stock prices compared to the coin-toss benchmark. These improvements further enable the corresponding Intraday Decision-making Short-term (IDS) trading strategy to achieve positive average daily returns in a simulated trading environment. Furthermore, by innovatively adapting the Multi-Kernel Learning (MKL) applied in Support Vector Machine (SVM) models into a Multi-Gauss-Kernel Learning (MGKL) framework consisting of the three GKs employed, respectively, for the above three different categories of textual information, i.e. RSB, CII, and R-M-P&G, the corresponding MGKL-SVM model is constructed for DC stock (price) trend prediction. This novel modeling approach integrating multiple categories of textual information not only significantly reduces the volume of textual data required and the computational complexity for intelligent analysis but also achieves a 10[Formula: see text] percentage point improvement in prediction accuracy compared to the three single-category (text-processing) GKL-SVM models (termed RSB, CII, and R-M-P&G (category) GKL-SVM models). The simulated trading results further demonstrate that the IDS trading strategy generates superior average daily returns relative to its single-category GKL-SVM counterparts. Finally, applying the MGKL-SVM model to Weekly Closing (WC) stock (price) trend prediction achieves significantly higher accuracy than daily prediction, indicating its potential practical value for medium-to-long-term value investing.