A Modelling of S&P 500 Index Price Based on U.S. Economic Indicators: Machine Learning Approach


  • Ligita Gasparėnienė Vilnius University, Lithuania
  • Rita Remeikiene Vilnius University, Lithuania
  • Aleksejus Sosidko Mykolas Romeris University, Lithuania
  • Vigita Vėbraitė Vilnius University, Lithuania




S&P 500 Index, economic indicators, machine learning, deep learning, fundamental analysis, stock


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.

Author Biographies

Ligita Gasparėnienė, Vilnius University, Lithuania

Ligita Gasparėnienė is the Head of the Research and Innovation Department, Vilnius University; the leader of the national project “Welfare society.” Research interests: shadow economy, digital shadow economy, corruption, money laundering, investments in gold, monetary policy. ORCID ID: https://orcid.org/0000-002-5535-6552

Rita Remeikiene, Vilnius University, Lithuania

Rita Remeikienė is Dr. in Social Sciences, a project leader at the Research and Innovation Department, Vilnius University, the senior researcher of the national project “Welfare society.” Research interests: shadow economy, digital shadow economy, corruption, money laundering, self-employment. ORCID ID: https://orcid.org/0000-0002-3369-485X

Aleksejus Sosidko, Mykolas Romeris University, Lithuania

Aleksejus Sosidko is a PhD student of the Faculty of Business and Economics, Mykolas Romeris University. Research interests: Fundamental analysis, stock market analysis, machine learning. ORCID ID: https://orcid.org/0000-0002-0438-668X

Vigita Vėbraitė, Vilnius University, Lithuania

Vigita Vėbraitė is Dr., an associate professor at Vilnius University Faculty of Law (Department of Private Law).  Research interests: private law, civil procedure, electronic justice, alternative dispute resolution. ORCID ID: https://orcid.org/0000-0003-4351-061X

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