Multiresolution Signal Processing of Financial Market Objects
This addresses the limitations of mainstream quantitative models in finance, which rely on linear correlations and fixed scales, though it appears incremental as it builds on existing neural network and multiresolution techniques.
The paper tackles the problem of analyzing complex financial market data by combining neural networks with multiscale decomposition to capture non-linear and causal structures, demonstrating the approach through seven use cases.
Multiresolution analysis has applications across many disciplines in the study of complex systems and their dynamics. Financial markets are among the most complex entities in our environment, yet mainstream quantitative models operate at predetermined scale, rely on linear correlation measures, and struggle to recognize non-linear or causal structures. In this paper, we combine neural networks known to capture non-linear associations with a multiscale decomposition to facilitate a better understanding of financial market data substructures. Quantization keeps our decompositions calibrated to market at every scale. We illustrate our approach in the context of seven use cases.