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Filtering market signals: dynamic asset allocation with momentum and hidden mean reversion

Author

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  • Sühan Altay
  • Katia Colaneri
  • Zehra Eksi-Altay
  • Eva Flonner

Abstract

We study dynamic asset allocation when returns display short-run momentum yet revert to a hidden long-run mean. We extend the two-factor specification of [Koijen, R.S., Rodriguez, J.C. and Sbuelz, A., Momentum and mean reversion in strategic asset allocation. Manage. Sci., 2009, 55, 1199–1213.] into a partially observable linear-Gaussian economy. We estimate the unobservable drift of the return process with a Kalman–Bucy filter and exploit the separation principle to characterize the optimal portfolio and its value under partial information. The optimal weight splits into a myopic momentum bet, an intertemporal hedge, and an information-hedging component that scales with the filter's conditional error variance. Closed-form expressions for the indifference value of information show that the premium for perfect drift observability rises with the noise-to-signal ratio. A simulation study enables us to interpret our theoretical results, and a real data application reveals that the partial-information strategy behaves more smoothly, yet still outperforms a naïve benchmark.

Suggested Citation

  • Sühan Altay & Katia Colaneri & Zehra Eksi-Altay & Eva Flonner, 2026. "Filtering market signals: dynamic asset allocation with momentum and hidden mean reversion," Quantitative Finance, Taylor & Francis Journals, vol. 26(4), pages 593-613, April.
    • Handle: RePEc:taf:quantf:v:26:y:2026:i:4:p:593-613
    DOI: 10.1080/14697688.2026.2627261
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