Predicting Financial Trader Participation in Fixing Risk Mitigation Cycles : A Machine Learning Approach

Abstract: Financial markets have been crucial in driving capital investments across the world. Anessential piece of these markets is the presence of risk takers, or market speculators, who will hold financial portfolios in hopes of profit. Portfolios with cash flowsgenerated from floating interest rate derivatives will often be subjected to fixing risk, also called second-order basis risk, stemming from a discrepancy in time with the hedge and the original position. Using data from a fixing risk mitigation service, named RESET, this thesis aims todeepen the understanding of accumulation of fixing risk on the the USD dollar market for 3-month interest rate swaps. This is done by modeling customer behavior using machine learning methods. Macroeconomic factors such as market volatility and the January effect amongst others were incorporated as variables into the set. The two models explored are logistic regression and neural networks, the first one chosen for interoperability and the latter for its generality. Neither of the two models could accurately predict customer behavior, with a balanced accuracy short of 70 percent. The strongest influence of the final prediction turned out to be previous behavior, the January effect and how many of their financial positions the customer previously put into the service.