Multiresolution Signal Processing of Financial Market Objects

Abstract

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 decompositions calibrated to market. We illustrate our approach via seven use cases.