Fuzzy rule-based modeling for interval-valued data: An application to high and low stock prices forecasting

Abstract

Financial interval time series (ITS) describe the evolution of the highest and lowest prices of an asset throughout time. The difference of these prices, the range, is a measure of volatility. Therefore, their accurate forecasts play an important role in many applications such as risk management, derivatives pricing, and portfolio selection, as well as supplement the information by the time series of the closing price values. This chapter proposes an interval fuzzy rule-based model (iFRB) for ITS forecasting. iFRB is a fuzzy rule-based model with affine consequents which provide a nonlinear approach that naturally processes interval-valued data. It is suggested as empirical application the prediction of the main index of the Brazilian stock market, the IBOVESPA. Interval forecasts are compared against traditional univariate and multivariate time series benchmark models and with an interval multilayer perceptron neural network in terms of traditional accuracy metrics, statistical tests, and quality measures for interval-valued data. The results indicate that iFRB method appears as a promising alternative for interval-valued financial time series forecasting.