Pair trading is a market-neutral strategy which is based on the use of standard, well-known statistical tests applied to time series of price to identify suitable pairs of stock. This article studies the potential benefits of using additional qualitative information for this type of trade. Here we use an ontology to represent and structure information extracted from financial SEC reports in a way that is optimised for search. These mandatory reports are originally published as XML using a schema that has varied over the years. The XML format itself is not easy to query, e.g. projections to fields or their composition are hard to find even when using an XML store. Our ontology-based approach provides uniformity of representation, which is further enhanced by the strife to use common vocabulary wherever possible. The ontology is then used to identify links between companies, by finding common senior employees or major shareholders. This is also potentially useful information to identify suitable pairs of stock. We show that the ontology increases the probability of selecting cointegrated pairs of stock from the data, with no negative effect on the survival time of such pairs when compared to random ones.
|Title of host publication||The 2020 IEEE Symposium Series on Computational Intelligence|
|Subtitle of host publication||IEEE Symposium on Computational Intelligence for Financial Engineeting and Economics (CIFEr 2020)|
|Number of pages||8|
|Publication status||Published - 1 Dec 2020|
- Pair trading
- SEC financial reports