WMR prediction using recurrent neural networks on FX limit order book data

Master thesis (2022)

Authors

S.B. Kortekaas Electrical Engineering, Mathematics and Computer Science

Contributors

A. Papapantoleon Applied Probability - EEMCS (supervisor 1)
F. Fang Numerical Analysis - EEMCS (supervisor 2)
T. Methorst (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Steven Kortekaas
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Published Date

03-06-2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

This thesis investigates the application of machine learning models on foreign exchange data around the WM/R 4pm Closing Spot Rate (colloquially known as the WMR Fix). Due to the nature of the market dynamics around the WMR Fix, inefficiencies can occur and therefore some predictability might be expected. We aim to find these inefficiencies. This is done by applying machine learning models, specifically recurrent neural networks, on limit order book data of foreign exchange (FX). The focus will be on the Euro - US dollar exchange rate.