  Understanding non-linear relationships among financial instruments has
various applications in investment processes ranging from risk management,
portfolio construction and trading strategies. Here, we focus on
interconnectedness among stocks based on their correlation matrix which we
represent as a network with the nodes representing individual stocks and the
weighted links between pairs of nodes representing the corresponding pair-wise
correlation coefficients. The traditional network science techniques, which are
extensively utilized in financial literature, require handcrafted features such
as centrality measures to understand such correlation networks. However,
manually enlisting all such handcrafted features may quickly turn out to be a
daunting task. Instead, we propose a new approach for studying nuances and
relationships within the correlation network in an algorithmic way using a
graph machine learning algorithm called Node2Vec. In particular, the algorithm
compresses the network into a lower dimensional continuous space, called an
embedding, where pairs of nodes that are identified as similar by the algorithm
are placed closer to each other. By using log returns of S&P 500 stock data, we
show that our proposed algorithm can learn such an embedding from its
correlation network. We define various domain specific quantitative (and
objective) and qualitative metrics that are inspired by metrics used in the
field of Natural Language Processing (NLP) to evaluate the embeddings in order
to identify the optimal one. Further, we discuss various applications of the
embeddings in investment management.
