Stock Price Prediction Based on Standardized Price–Volume Charts and a Convolutional Neural Network
Main article
Abstract
Against the backdrop of accelerating the development of new quality productive forces and tightening capital market regulation, accurately assessing firms’ earnings quality is crucial for high-quality regional development. This study uses a sample of 569 A-share listed firms in the Greater Bay Area from 2020 to 2024, measures the substance of business operations based on a theoretical profit-health assessment model, and examines structural features using ANOVA and nonparametric tests. The results show, first, that firms’ profit health in the Greater Bay Area displays a robust “olive-shaped” pattern—large in the middle and small at both ends—yet top-tier firms with exceptionally high quality remain scarce. Second, overall differences across cities are not significant, supporting the synergy of regional integration, while core cities exhibit a more intense internal selection mechanism. Third, there is significant structural divergence across industries (p < 0.01), forming a “new-strong, old-weak” pattern: information technology and materials—both aligned with new quality productive forces—show clear advantages, whereas traditional sectors such as real estate and finance face substantial balance-sheet repair pressure. The study indicates that the model effectively identifies structural risks and provides empirical evidence for regulators to implement differentiated supervision and to promote the transition from old to new growth drivers.
