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

Learning Embedded Representation of the Stock Correlation Matrix using Graph Machine Learning