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Accounting vs technical information: what matters more for stock return predictability?

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  • Cakici, Nusret
  • Zaremba, Adam

Abstract

We employ machine learning models to determine what matters more for stock return predictability: technical data or accounting information. Technical data holds an advantage—it consistently yields more accurate forecasts and higher portfolio returns. This superiority is not limited to the U.S. market but extends to major developed markets worldwide, at times showing even stronger effects. Furthermore, it remains remarkably robust across firm sizes and time periods. However, its edge is most pronounced at short horizons and comes at the cost of higher turnover. Accounting signals, while weaker overall, perform better over longer horizons and support lower-cost implementation. Finally, technical strategies excel in volatile, hard-to-value contexts, whereas accounting-based models fare better when valuation uncertainty is low.

Suggested Citation

  • Cakici, Nusret & Zaremba, Adam, 2025. "Accounting vs technical information: what matters more for stock return predictability?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 104(C).
  • Handle: RePEc:eee:intfin:v:104:y:2025:i:c:s1042443125000976
    DOI: 10.1016/j.intfin.2025.102207
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    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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