Modeling of EURO STOXX 50 index price returns based on industrial production surprises: basic and machine learning approach

Ligita Gasparėnienė (), Rita Remeikienė (), Aleksejus Sosidko (), Vigita Vėbraitė () and Evaldas Raistenskis ()
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Ligita Gasparėnienė: Vilnius University, Lithuania
Rita Remeikienė: Vilnius University, Lithuania
Aleksejus Sosidko: Mykolas Romeris University, Lithuania
Vigita Vėbraitė: Vilnius University, Lithuania
Evaldas Raistenskis: Vilnius University, Lithuania

Entrepreneurship and Sustainability Issues, 2020, vol. 8, issue 2, 1305-1320

Abstract: There are a big number of researches which analyzing stock price returns. Some of them is based on fundamental analysis theory. Meanwhile other studies are based on efficient market hypotheses and financial behavior theories. However, there is not enough researches combining the characteristics of these theories into one. Such kind of researches in the scientific literature is usually referred to macroeconomic news, announcements, surprises, expectations studies. These studies examine not only the actual but also the predictive values of macroeconomic indicators announcements, normalizing them and thus creating absolutely a new surprise indicator. Purpose of this paper is modeling EURO STOXX 50 index price returns based on Industrial production surprise indicator. Empirical part shows that the best models for explaining EURO STOXX 50 index price returns was obtained at the 40 and 42 in different surprise indicator scenario. The coefficient of determination was obtained respectively 24.70% and 21,80%. Meanwhile applying machine learning method of artificial intelligence, a much more accurate models were obtained. The coefficient of determination respectively was 33,22% and 26,60%.

Keywords: surprises; price; return; EURO STOXX 50; industrial production (search for similar items in EconPapers)
JEL-codes: L16 P22 (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:ssi:jouesi:v:8:y:2020:i:2:p:1305-1320

DOI: 10.9770/jesi.2020.8.2(77)

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