Harry Hedman: Performance Evaluation of Artificial Neural Networks in the Foreign Exchange Market

This thesis examines the performance of artificial neural networks in the foreign exchange market. The thesis is restricted to comprise two types of network architectures: feedforward and probabilistic neural networks, respectively. The networks' capabilities are evaluated in a trading simulation, where predictions of exchange rate log-returns are backtested using historical data. All G10 currency pairs are considered, 45 in total. The results presented indicate that although several networks generate substantial returns, the average performance is rather modest. The foreign exchange market indeed appears efficient.