Modeling the Financial Market with Multiple Prices

Abstract

An effective financial market trading decision is usually dependent on superior forecasting. Forex market as the largest financial market is chosen in this study. The main objective of this paper is to explore the forecasting performance of the proposed multiple-price model which integrates close, low and high price information, by using Artificial Neural Network (ANN). The architecture of the network and the related algorithms are described. The effects due to different choices of preprocessing methods, combinations of input variables and different time intervals of forecasting are examined. By using the best multiple-price model, trading strategies with high and low prices are developed as well. The results have shown that in terms of both absolute values and trends of the prices, forecasting accuracy has improved compared with single price model. This is especially so for low and high prices whose directional accuracies are much higher. The trading performance is also proven to have much better total return than buy & hold strategy, and trading with high price has the best overall performance considering both return and risk.