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Regime-Dependent Sectoral Information Transmission in S&P 500 Forecasting

Author

Listed:
  • László Vancsura

    (Department of Marketing and Supply Chain Analysis, Institute of Agricultural and Food Economics, Hungarian University of Agriculture and Life Sciences, 7400 Kaposvár, Hungary)

  • Tibor Tatay

    (Department of Statistics, Finances and Controlling, Széchenyi István University, 9026 Győr, Hungary)

  • Tivadar Zakár

    (Doctoral School of Management and Business Administration, John von Neumann University, 1117 Budapest, Hungary)

Abstract

Understanding which segments of the economy drive aggregate stock market movements is central to risk management. This study traces how the economic drivers of the Standard & Poor’s 500 Index (S&P 500) changed across two episodes: the 2020 COVID-19 shock and the 2025 technology-led period, using eleven sector indices and nine deep learning architectures. During COVID-19, forecasting power concentrated in Consumer Discretionary, Health Care, and Industrials before reorganizing sharply around Information Technology, consistent with a disruptive break. In 2025, Information Technology and market momentum dominated throughout, with no comparable reorganization, consistent with a gradual adjustment rather than a disruptive shift. This distinction, invisible from accuracy metrics alone (Gated Recurrent Unit: Mean Absolute Percentage Error = 3.41% and 2.16%), shows that information-concentration diagnostics can complement forecast-accuracy and risk-monitoring frameworks.

Suggested Citation

  • László Vancsura & Tibor Tatay & Tivadar Zakár, 2026. "Regime-Dependent Sectoral Information Transmission in S&P 500 Forecasting," Forecasting, MDPI, vol. 8(4), pages 1-56, August.
    • Handle: RePEc:gam:jforec:v:8:y:2026:i:4:p:75-:d:2016984
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