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An entropy-based class of moving averages

Andreas Kull

Journal of Investment Strategies2024https://doi.org/10.21314/jois.2024.004article
AJG 1
Weight
0.30

What the paper says

This paper discusses the application of information-theoretic concepts to the backward filtering of time series using moving averages. We identify moving averages as time-dependent expectation values derived from maximum entropy probability kernels that are subject to relevant constraints. Constraining the width of the kernel results in the simple moving average, while constraining the typical timescale yields the exponential moving average. With a martingale constraint, we derive a moving average corresponding to a risk-neutral valuation scheme for financial time series. By expanding this framework to generalized forms of entropy, we introduce a broad family of maximum-entropy-based moving averages.

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https://doi.org/https://doi.org/10.21314/jois.2024.004

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@article{andreas2024,
  title        = {{An entropy-based class of moving averages}},
  author       = {Andreas Kull},
  journal      = {Journal of Investment Strategies},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.21314/jois.2024.004},
}

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An entropy-based class of moving averages

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Evidence weight

0.30

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.00 × 0.4 = 0.00
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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