Introduction to financial forecasting

Abstract

This paper provides a brief introduction to forecasting in financial markets with emphasis on commodity futures and foreign exchange. We describe the basic approaches to forecasting, and discuss the noisy nature of financial data. Using neural networks as a learning paradigm, we describe different techniques for choosing the inputs, outputs, and error function. We also describe the learning from hints technique that augments the standard learning from examples method. We demonstrate the use of hints in foreign-exchange trading of the U.S. Dollar versus the British Pound, the German Mark, the Japanese Yen, and the Swiss Franc, over a period of 32 months. The paper does not assume a background in financial markets.