By the same authors

Ontology Graph Embeddings and ILP for Financial Forecasting

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Title of host publicationInductive Logic Programming, Proceedings of the 30th International Conference
DateAccepted/In press - 20 Sep 2021
Number of pages14
PublisherSpringer
Original languageEnglish

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NameLNAI

Abstract

There is a history of hybrid machine learning approaches where the result of an unsupervised learning algorithm is used to provide data annotation from which ILP can learn in the usual supervised manner. Here we consider the task of predicting the property of cointegration between the time series of stock price of two companies, which can be used to implement a robust pair-trading strategy that can remain profitable regardless of the overall direction in which the market evolves. We start with an original FinTech ontology of relations between companies and their managers, which we have previously extracted from SEC reports, quarterly filings that are mandatory for all US companies. When combined with stock price time series, these relations have been shown to help find pairs of companies suitable to pair trading. Here we use node2vec embeddings to produce clusters of companies and managers, which are then used as background predicates in addition to the relations linking companies and staff present in the ontology, and the values of the target predicate for a given time period. Progol is used to learn from this mixture of predicates combining numerical with structural relations of the entities represented in the data set to reveal rules with and predictive power.

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© 2021 Springer Nature Switzerland AG.

    Research areas

  • Ontology, financial forecasting, SEC financial reports, Inductive Logic Programming (ILP), unsupervised learning, graph embedding

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