Forecasting Bank Stock Price With Function Principal Component Analysis

Over the past decade,with the rapid development of science and technology,FDA plays a more and more important role in modern scientific rescarch.In the big data time,the observed data will display an obvious functional feature.Most of them are smooth curves or continous functions.The traditional data analysis method is not applicable for this kind of functional data and it has its limitations to build models.A new solution to data analysis is introduced-Functional Data Analysis(FDA),which consider the observed functional data as a whole,not a series of numbers and with less assumptions.It is substantial of functional data analysis.FDA selects a basis function system to linearly expand sample data and transform it into a smooth fitting function curve which can be analyzed in the context of function data.In the financial markets,the data can be regarded as a continuous function data.This thesis collectes ten banks shares of closeing and opening price data.According to the functional data analysis method to explore the functional theory of principal component analysis.Based functional data principal component bulid linear prediction model and nonlinear prediction model.In addition,the closing price of ten bank shares was predicted,and a good prediction effect was obtained.