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• W1490444017 abstract "The present study deals with heterogeneous learning rules in speculative markets where heuristic strategies reflect the rules-of-thumb of boundedly rational investors. The major challenge for chartists is the development of new models that would enhance forecasting ability particularly for time series with dynamic time- varying, nonlinear features. This paper introduces fuzzy learning rules with the incorporation of beliefs, preferences and idiosyncratic behavioral patterns for decision-making and trading under uncertainty. The efficiency of a technical trading strategy based on a neurofuzzy model is investigated, in order to predict the direction of the market for NASDAQ Composite, NIKKEI255 and FTSE100. Moreover, it is demonstrated that the incorporation of the estimates of the conditional changes strongly enhances predictability, 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 a markov-switching model, a recurrent neural network as well as to the buy & hold strategy for all indices. 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 dependence produces sign dependence. Overall what leads to optimal prediction is the dynamic update of the expectations and preferences of the heuristic learning rules combined with the adaptive calibration of the degrees-of-belief that match agent’s fads." @default.
• W1490444017 created "2016-06-24" @default.
• W1490444017 creator A5038937667 @default.
• W1490444017 date "2009-01-01" @default.
• W1490444017 modified "2023-09-27" @default.
• W1490444017 title "Boundedly rational learning and heterogeneous trading strategies with hybrid neuro-fuzzy models" @default.
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• W1490444017 hasPublicationYear "2009" @default.
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