Learning Undirected Graphs in Financial Markets
This work addresses graph learning for financial analysts, but it is incremental as it applies existing Laplacian constraints to a specific domain.
The paper tackled the problem of learning undirected graphical models with Laplacian constraints from financial market data, showing that these constraints relate to market index factors and conditional correlations between stocks, and proposed algorithms to handle non-stationarity and stock clustering.
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.