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Learning Undirected Graphs in Financial Markets

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  • Jos'e Vin'icius de Miranda Cardoso
  • Daniel P. Palomar

Abstract

We investigate the problem of learning undirected graphical models under Laplacian structural constraints from the point of view of financial market data. We show that Laplacian constraints have meaningful physical interpretations related to the market index factor and to the conditional correlations between stocks. Those interpretations lead to a set of guidelines that users should be aware of when estimating graphs in financial markets. In addition, we propose algorithms to learn undirected graphs that account for stylized facts and tasks intrinsic to financial data such as non-stationarity and stock clustering.

Suggested Citation

  • Jos'e Vin'icius de Miranda Cardoso & Daniel P. Palomar, 2020. "Learning Undirected Graphs in Financial Markets," Papers 2005.09958, arXiv.org, revised Nov 2020.
    • Handle: RePEc:arx:papers:2005.09958
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    File URL: http://arxiv.org/pdf/2005.09958
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    Cited by:

    1. Jaydip Sen & Rajdeep Sen & Abhishek Dutta, 2021. "Machine Learning in Finance-Emerging Trends and Challenges," Papers 2110.11999, arXiv.org.

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