机器学习在金融研究中的应用探究OA

As financial markets become increasingly complex and big data technolo-gies are applied more deeply,traditional financial econometric methods,which rely on linear assumptions and focus mainly on structured data,have gradually revealed limitations in supporting precise decision-making.Machine learning,with its ability to autonomously extract data patterns and handle massive amounts of both struc-tured and unstructured data,has emerged as a pivotal force breaking through these limitations.It has enabled transformative advancements across domains such as fi-nancial market forecasting,risk assessment and management,robo-advisory sys-tems,and text mining,evolving from technical applications to paradigm shifts.Building on a systematic review of machine learning applications in finance and alig-ning with fundamental research needs and technological trends,this paper outlines the core principles and innovative directions for integrating machine learning into financial research.It analyzes key challenges at the data,modeling,market,and reg-ulatory levels in current implementations and proposes actionable solutions with both theoretical rigor and practical feasibility.The study aims to provide a forward-looking yet practical framework for deepening financial technology integration,highlighting the inevitable evolution of financial research toward data-driven,preci-sion-oriented,and intelligent approaches powered by machine learning.