| Title |
Modelling of S&P 500 index price based on U.S. economic indicators: machine learning approach / |
| Authors |
Gasparėnienė, Ligita ; Remeikienė, Rita ; Sosidko, Aleksejus ; Vėbraitė, Vigita |
| DOI |
10.5755/j01.ee.32.4.27985 |
| Full Text |
|
| Is Part of |
Inžinerinė ekonomika = Engineering economics.. Kaunas : Kaunas University of Technology. 2021, vol. 32, no. 4, p. 362-375.. ISSN 1392-2785. eISSN 2029-5839 |
| Keywords [eng] |
S&P 500 Index ; economic indicators ; machine learning ; deep learning ; fundamental analysis ; stock |
| Abstract [eng] |
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. |
| Published |
Kaunas : Kaunas University of Technology |
| Type |
Journal article |
| Language |
English |
| Publication date |
2021 |
| CC license |
|
