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Paying attention to returns: Forecasting returns as nonlinear AR time-varying drifts using multihead attention and Singular Spectrum Analysis

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  • Wáng, Adriel

Abstract

Many believe that informational efficient markets make predicting returns impossible, but methods like Moving Averages (MA) and Exponential Smoothing show it is possible. This fact opens the door for applying signal processing techniques to the theory of financial mathematics. Here, we use Singular Spectrum Analysis (SSA) to predict price movements driven by Lévy Stable Stochastic Differential Equations. In a study of 4-hour cryptocurrency returns, we show that SSA improves the forecast accuracy of OHLC/4 data. Using Bitcoin (BTC), we show how a multihead attention model can predict prices up to 24 h (6 steps) ahead, such that attention and spectral filtering work well together.

Suggested Citation

  • Wáng, Adriel, 2025. "Paying attention to returns: Forecasting returns as nonlinear AR time-varying drifts using multihead attention and Singular Spectrum Analysis," Chaos, Solitons & Fractals, Elsevier, vol. 199(P3).
    • Handle: RePEc:eee:chsofr:v:199:y:2025:i:p3:s0960077925008069
    DOI: 10.1016/j.chaos.2025.116793
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    References listed on IDEAS

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