TITLE:
Forecasting S&P 500 Stock Index Using Statistical Learning Models
AUTHORS:
Chongda Liu, Jihua Wang, Di Xiao, Qi Liang
KEYWORDS:
Statistical Learning Models, S&P 500 Index, Feature Selection, SVM, RBF Kernel
JOURNAL NAME:
Open Journal of Statistics,
Vol.6 No.6,
December
7,
2016
ABSTRACT: Forecasting the movement of stock market is
a long-time attractive topic. This paper implements different statistical
learning models to predict the movement of S&P 500 index. The S&P 500
index is influenced by other important financial indexes across the world such
as commodity price and financial technical indicators. This paper systematically
investigated four supervised learning models, including Logistic Regression,
Gaussian Discriminant Analysis (GDA), Naive Bayes and Support Vector Machine
(SVM) in the forecast of S&P 500 index. After several experiments of
optimization in features and models, especially the SVM kernel selection and
feature selection for different models, this paper concludes that a SVM model
with a Radial Basis Function (RBF) kernel can achieve an accuracy rate of
62.51% for the future market trend of the S&P 500 index.