Financial markets behave more like noisy radio signals than statistical distributions, allowing engineering tools to predict price movements.
Most traders use statistical models that assume market movements are a series of random events. This framework replaces those models with Butterworth filters and physical damping rules typically used in signal processing. By treating the stock market as a physical object with momentum and friction, the system filters out the noise of daily trading. This approach provides a much more stable way to allocate assets than traditional portfolio theory. The results suggest that the random walk of prices is actually an engineering problem with solvable variables. This shift allows investors to apply the rigors of electrical engineering to the chaos of global finance.
Signal Processing Architectures for Systematic Asset Allocation: A Cross-Disciplinary Framework
SSRN · 6721258
This paper develops a cross-disciplinary framework for systematic asset allocation by combining ideas from signal processing, information theory, condensed matter physics, and quantitative finance. The central claim is that financial time series are better understood as noisy physical signals than as purely statistical objects. Once this shift in framing is made, several practical implications follow. First, trend extraction should be treated as a filtering problem rather than a moving-average p