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Abstracts Mathematics
WMR prediction using recurrent neural networks on FX limit order book data
by Steven (author) Kortekaas
| Institution: | Delft University of Technology |
|---|---|
| Department: | |
| Degree: | |
| Year: | 2022 |
| Keywords: | Recurrent Neural Network; Long Short-term Memory; Foreign Exchange; Limit order book; WMR Fix |
| Posted: | 3/25/2025 |
| Record ID: | 2291700 |
| Full text PDF: | http://resolver.tudelft.nl/uuid:89f4ea74-0632-4563-a0d5-ced6363d6ded |
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. Applied Mathematics | Financial Engineering
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