چکیده (انگلیسی):

Forex market is the most liquid financial market and the largest market in the world. Forex market has been
analysed using two isolated approaches, technical analysis and fundamental analysis. Technical analysis
attempts to predict the movement of prices by studying the historical data of the market whereas
fundamental analysis concerns essentially with the overall state of the economy. Relying on one kind of
analysis limits the quality of trading decisions therefore traders usually gain insight into the market by
analysing many factors which may influence the market state and the price movement. This process has
become increasingly challenging due to the vast and variant number of prices’ determinants and the rapid
changes in the market dynamics. This study proposes an event-driven business intelligence approach to
respond immediately to any change in the market status by generating trading signals based on different
analyses. Targeting the value associated with the data as it arrives, different models are built to capture and
process the data of three currencies against US dollar in different frequency as well as the data of nine US
macroeconomic indicators. The time-series data for both technical and fundamental indicators are modelled
using artificial neural network while a knowledge base model is implemented to integrate the signals
generated by time-series models. The experimental results show a remarkable improvement in the quality of
trading signals using real-time consideration of different analyses.