Novel Data-Driven Dynamic Network Science Application in Algorithmic Trading

One of the recent developments in computational finance has been the rise in using graph-based approaches to analyse stock market dynamics systematically. In algo trading literature, stocks for pairs trading and multiple trading are commonly selected by using Engle Granger and Johansen co-integration tests. Identifying all pairs eligible for trading in large datasets, entails a significant computational burden. In this paper, price correlation-based dynamic networks and differenced price series-based correlation networks and their importance ranks are used to pre-select pairs for trading. Algorithmic trading profits using commonly used co-integration methods are compared with the profits made by proposed correlation-based financial networks. Unlike the existing work, the novelty of the paper is that it uses data-driven correlation-based financial network approach to propose a stock selection method for pairs trading. In this paper, profit per transaction is used to compare the trading strategies. The superiority of the financial network stock selection method is discussed as well in some detail.