Stock Prediction Based on Machine and Deep Learning

Amidst the ongoing evolution of capital markets and accelerated progress in digital technologies, forecasting equity market trends has emerged as a pivotal investigation domain within computational finance and intelligent systems research. This study systematically examines two predominant predictive methodologies: algorithmic learning techniques and neural network architectures. Initially, this paper establish the contextual relevance and academic value of financial market forecasting while delineating fundamental principles governing critical processes like information pattern recognition, predictive framework development, and performance validation. Regarding machine learning applications, the analysis explores conventional computational models including Decision Tree ensembles and Kernel-based classification systems in market prediction scenarios. The investigation further expands to neural network implementations, highlighting temporal pattern recognition capabilities in Long Short-Term Memory architectures and spatial feature extraction advantages in Hierarchical Neural Structures. The concluding section addresses current methodological constraints and potential advancements through data refinement techniques, algorithmic enhancement strategies, and hybrid forecasting approaches. This research endeavors to construct an integrative analytical paradigm for academic investigators, proposing multidimensional examination frameworks and implementation methodologies for developing innovative, reliable, and transparent financial forecasting systems while enhancing synergy between conceptual breakthroughs and real-world deployment.