Open AccessModelling of S&P 500 Index Price Based on U.S. Economic Indicators: Machine Learning Approach
Author(s) -
Ligita Gasparėnienė,
Rita Remeikienė,
Aleksejus Sosidko,
Вігіта Вебрайте
Publication year - 2021
Publication title -
inžinerinė ekonomika
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.303
H-Index - 29
eISSN - 2029-5839
pISSN - 1392-2785
DOI - 10.5755/j01.ee.32.4.27985
Subject(s) - multicollinearity , index (typography) , random forest , econometrics , machine learning , computer science , statistical model , artificial intelligence , linear regression , regression analysis , model selection , stock market index , feature selection , statistical learning , economic indicator , decision tree , statistics , stock market , mathematics , economics , paleontology , macroeconomics , horse , world wide web , biology
In order to forecast stock prices based on economic indicators, many studies have been conducted using well-known statistical methods. Meanwhile, since ~2010 as the power of computers improved, new methods of machine learning began to be used. It would be interesting to know how those algorithms using a variety of mathematical and statistical methods, are able to predict the stock market. The purpose of this article is to model the monthly price of the S&P 500 index based on U.S. economic indicators using statistical, machine learning, deep learning approaches and finally compare metrics of those models. After the selection of indicators according to the data visualization, multicollinearity tests, statistical significance tests, 3 out of 27 indicators remained. The main finding of the research is that the authors improved the baseline statistical linear regression model by 19 percent using a ML Random Forest algorithm. In this way, model achieved accuracy 97.68% of prediction S&P 500 index.