Multiresolution Signal Processing of Financial Market Objects

2022/10/28 by Boier, Ioana · 1 citation
#Computational Finance (q-fin.CP) #FOS: Computer and information sciences #FOS: Economics and business #FOS: Electrical engineering #Machine Learning (cs.LG) #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2210.15934

Abstract

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.

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