Year: 2025
Type: article
Abstract: As the global economy becomes more interconnected and financial markets face increasing risks, stock price volatility has also risen. For the numerous stock traders and investors in our country, conducting scientific and effective stock price prediction and analysis is crucial. This process enables the formulation of sound investment strategies and the achievement of higher returns. Additionally, the rapid advancements in machine learning in recent years have made stock price factor analysis using neural networks increasingly feasible.This paper reviews key studies from the past few decades and provides a summary of the research methods and models used in stock price factor analysis. It also explores two primary research approaches: horizontal research and vertical research. Furthermore, the paper compares long-term and short-term investment strategies, highlighting the neural network models most suitable for each scenario.
Author: Ziyue Zhao
Institution: Beijing University of Technology
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4410907850