Abstract

Markets play a critical role in economics of the world and the distribution of wealth. Predicting them can help with preventing crashes and avoiding severe losses, or making significant profits. But such prediction is not easy due to the very complex nature of markets and the wide variety of the influence factors involved. Technical analysts or chartists rely on historical chart data to predict patterns based on previous behaviours of graphs. This approach is fairly straightforward and has also been automated to a great extent. There are computer programs or predictor robots that use the technical approach and facilitate buy or sell decisions. However, market behaviour obviously is more than repetition of old patterns and many of the events in the outside world have constant impacts on it. These external pieces of information can vary from political events to economic statistics. Fundamental analysts are those with a knowledge and understanding of the world events on market behaviour. This requires knowledge of politics, micro and macroeconomics to say the least, and hence, there are far fewer of such analysts. However, the very successful analysts like Warren Buffet have repeatedly emphasized on consideration of fundamental data in prediction calculations. Nevertheless, proper fundamental analysis remains to be a challenge and even a bigger challenge when it comes to its automation. There are very few research efforts and approaches which look into possibilities of automation of fundamental analysis. Hence, this work initiated a novel approach on fundamental data manipulation for identification of relationships between market behaviour and external information. This work made an effort to apply the afore in the foreign exchange market by observing the USD/GBP currency pair. In this research, an approach was devised and proposed for integration of fundamental data into automatic prediction. In this approach 3 main sources for fundamental data were identified. From these sources, data was extracted, organized and then fed into a proposed neural network during 6 experiments. The experiments put the possible relationships between the identified fundamental data and the price movements of the chosen currency pair (USD/GBP) to test. The test results indentified the datasets with plausible relationships with the market behaviour. The observed positive output of 3 different sets of data-input proved the proposed methodology to be of considerable value for market prediction.

Key words: Foreign exchange market prediction, stock market prediction, neural networks, fundamental analysis, market behaviour.