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

Author

Listed:
  • 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)

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%.

Suggested Citation

  • Ligita Gasparėnienė & Rita Remeikienė & Aleksejus Sosidko & Vigita Vėbraitė & Evaldas Raistenskis, 2020. "Modeling of EURO STOXX 50 index price returns based on industrial production surprises: basic and machine learning approach," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 8(2), pages 1305-1320, December.
    • Handle: RePEc:ssi:jouesi:v:8:y:2020:i:2:p:1305-1320
    DOI: 10.9770/jesi.2020.8.2(77)
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    References listed on IDEAS

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    2. Kari Harju & Syed Mujahid Hussain, 2011. "Intraday Seasonalities and Macroeconomic News Announcements," European Financial Management, European Financial Management Association, vol. 17(2), pages 367-390, March.
    3. Pasquariello, Paolo, 2014. "Prospect Theory and market quality," Journal of Economic Theory, Elsevier, vol. 149(C), pages 276-310.
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    5. Cakan Esin & Rangan Gupta, 2017. "Does the US. macroeconomic news make the South African stock market riskier?," Journal of Developing Areas, Tennessee State University, College of Business, vol. 51(4), pages 17-27, October-D.
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    7. Riadh El ABED & Amna ZARDOUB, 2019. "Exploring the nexus between macroeconomic variables and stock market returns in Germany: An ARDL Co-integration approach," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(2(619), S), pages 139-148, Summer.
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    More about this item

    Keywords

    surprises; price; return; EURO STOXX 50; industrial production;
    All these keywords.

    JEL classification:

    • L16 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Industrial Organization and Macroeconomics; Macroeconomic Industrial Structure
    • P22 - Political Economy and Comparative Economic Systems - - Socialist and Transition Economies - - - Prices

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