Algorithms for Learning Graphs in Financial Markets Article Swipe

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José Vinícius de Miranda Cardoso , Jiaxi Ying , Daniel P. Palomar · YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2012.15410

In the past two decades, the field of applied finance has tremendously benefited from graph theory. As a result, novel methods ranging from asset network estimation to hierarchical asset selection and portfolio allocation are now part of practitioners' toolboxes. In this paper, we investigate the fundamental problem of learning undirected graphical models under Laplacian structural constraints from the point of view of financial market times series data. In particular, we present natural justifications, supported by empirical evidence, for the usage of the Laplacian matrix as a model for the precision matrix of financial assets, while also establishing a direct link that reveals how Laplacian constraints are coupled to meaningful physical interpretations related to the market index factor and to conditional correlations between stocks. Those interpretations lead to a set of guidelines that practitioners should be aware of when estimating graphs in financial markets. In addition, we design numerical algorithms based on the alternating direction method of multipliers to learn undirected, weighted graphs that take into account stylized facts that are intrinsic to financial data such as heavy tails and modularity. We illustrate how to leverage the learned graphs into practical scenarios such as stock time series clustering and foreign exchange network estimation. The proposed graph learning algorithms outperform the state-of-the-art methods in an extensive set of practical experiments. Furthermore, we obtain theoretical and empirical convergence results for the proposed algorithms. Along with the developed methodologies for graph learning in financial markets, we release an R package, called fingraph, accommodating the code and data to obtain all the experimental results.

Type

preprint

Language

en

Landing Page

http://arxiv.org/abs/2012.15410

PDF

https://arxiv.org/pdf/2012.15410

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green

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10

OpenAlex ID

https://openalex.org/W4287546840

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OpenAlex ID

https://openalex.org/W4287546840

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DOI

https://doi.org/10.48550/arxiv.2012.15410

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Title

Algorithms for Learning Graphs in Financial Markets

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Type

preprint

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Language

en

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Publication year

2020

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Publication date

2020-12-31

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Authors

José Vinícius de Miranda Cardoso, Jiaxi Ying, Daniel P. Palomar

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https://arxiv.org/abs/2012.15410

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PDF URL

https://arxiv.org/pdf/2012.15410

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Open access

Yes

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OA status

green

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OA URL

https://arxiv.org/pdf/2012.15410

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Concepts

Computer science, Stylized fact, Financial market, Financial networks, Machine learning, Leverage (statistics), Finance, Artificial intelligence, Stock market, Theoretical computer science, Econometrics, Mathematics, Economics, Systemic risk, Macroeconomics, Biology, Horse, Paleontology, Financial crisis

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0

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Related works (count)

10

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