Forecasting Directional Change Uncertainty Using Probabilistic Fuzzy Systems

Directional change (DC) representations of stock and exchange rate prices have been proposed as a new method for describing and forecasting intra-day price movements. In the DC approach, a time series price curve is transformed into an intrinsic time curve which records price changes that exceed a threshold level and identify upwards and downwards price changes. These price changes are shown to be relevant for creating investment strategies. In this paper we propose the use of a multi-output Probabilistic Fuzzy System (PFS) to forecast upwards and downwards price changes in the intrinsic time curve. The use of PFS allows to model the stochastic uncertainty of the price changes, while maintaining a linguistic description of the system. The proposed model forecasts upwards and downward movements depending on current market conditions. We apply the proposed method to 5-minute intraday data and report the accuracy of the model in estimating and forecasting price changes. In addition, we illustrate how the uncertainty in these forecasts can potentially be used to define DC-based investment strategies. We report the risk-return features of these strategies and compare them with a conventional moving window strategy and an existing DC investment strategy.