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<p>For regulators, investors, and risk managers, the prediction of sector returns in financial markets remains a complex task. The main challenge lies in the accurate representation of the tricky interdependencies within sectors and the nonlinear dynamics that are manipulated by economic disruptions, market attitude, and structural transformations. This study conducts a comparative analysis of two major modeling paradigms for predicting sector-level returns in the context of an emerging market. The results reveal that LSTM models demonstrate superior accuracy in capturing non-linear short-term fluctuations, particularly during periods of market stress. In addition to offering insights into the sectoral behavior of an emerging economy during crisis and recovery phases, the research outlines several methodological and practical implications for portfolio management, stress-testing, and real-time economic monitoring. Future extensions include validation on other markets, integration of exogenous macroeconomic variables, and deployment of real-time adaptive forecasting systems.</p>

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Predicting under uncertainty to shape the future: a comparative analysis of econometric and deep learning models for smarter sectoral investment decisions

  • 期刊论文

  • 发表日期：

    2026年7月16日

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  • Hanane Allioui,
  • Mohamed Beraich,
  • Azzeddine Allioui

摘要

For regulators, investors, and risk managers, the prediction of sector returns in financial markets remains a complex task. The main challenge lies in the accurate representation of the tricky interdependencies within sectors and the nonlinear dynamics that are manipulated by economic disruptions, market attitude, and structural transformations. This study conducts a comparative analysis of two major modeling paradigms for predicting sector-level returns in the context of an emerging market. The results reveal that LSTM models demonstrate superior accuracy in capturing non-linear short-term fluctuations, particularly during periods of market stress. In addition to offering insights into the sectoral behavior of an emerging economy during crisis and recovery phases, the research outlines several methodological and practical implications for portfolio management, stress-testing, and real-time economic monitoring. Future extensions include validation on other markets, integration of exogenous macroeconomic variables, and deployment of real-time adaptive forecasting systems.