Sign prediction and volatility dynamics with hybrid neurofuzzy approaches.

Abstract

Reliable forecasting techniques for financial applications are important for investors either to make profit by trading or hedge against potential market risks. In this paper the efficiency of a trading strategy based on the utilization of a neurofuzzy model is investigated, in order to predict the direction of the market in case of FTSE100 and New York stock exchange returns. Moreover, it is demonstrated that the incorporation of the estimates of the conditional volatility changes, according to the theory of Bekaert and Wu (2000), strongly enhances the predictability of the neurofuzzy model, as it provides valid information for a potential turning point on the next trading day. The total return of the proposed volatility-based neurofuzzy model including transaction costs is consistently superior to that of a Markov-switching model, a feedforward neural network as well as of a buy & hold strategy. The findings can be justified by invoking either the "volatility feedback" theory or the existence of portfolio insurance schemes in the equity markets and are also consistent with the view that volatility dependence produces sign dependence. Thus, a trading strategy based on the proposed neurofuzzy model might allow investors to earn higher returns than the passive portfolio management strategy.