A Synergistic Forecasting Model for High-Frequency Foreign Exchange Data: Statistical Significance, Economic Significance and Trading Strategies

In this thesis, we develop a synergistic forecasting model using the information fusion approach. By using high frequency (one-minute) foreign exchange (FX) data, the model fuses two standalone models, namely the technical analysis structural model and the intra-market model. Subsequently, the outputs are fed into a unique modified extended Kalman filter whose functional parameters are estimated dynamically by using an artificial neural network. The synergistic model is tested on four currency pairs (EURUSD, EURGBP, NDZUSD, and USDJPY) that dominate the FX market. In terms of forecasting performance, both root mean squared error and correct directional change performance results show that the synergistic model statistically outperforms and is superior to each of the both standalone models as well as to the benchmark random walk model. This thesis also presents the economic significance of trading system based on the synergistic forecasting model by developing automated simple trend-following and adaptive trading systems strategies, considering the market microstructures of transaction costs. The results for economic significance support the possibility of profiting from these predictions which are positive for both trading strategies, but the adaptive trading system gain higher return than simple trend following trading. Keywords: foreign exchange, Kalman filter, forecasting, high-frequency data, technical analysis indicators, automated trading, statistical significance, economic significance.