金融市场中的无向图学习
Learning Undirected Graphs in Financial Markets
摘要：
我们从金融市场数据的角度研究了拉普拉斯结构约束下无向图模型的学习问题。我们发现，拉普拉斯约束有意义的物理解释相关的市场指数因子和股票之间的条件相关性。这些解释导致了一套准则，用户在估计金融市场中的图形时应该知道。此外，我们还提出了学习无向图的算法，这些无向图可以解释金融数据固有的程式化事实和任务，如非平稳性和股票聚类。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
