Financial Markets Analysis by Probabilistic Fuzzy Modelling

Jan van den Berg, Willem-Max van den Bergh and Uzay Kaymak ()

ERIM Report Series Research in Management from Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam

Abstract: For successful trading in financial markets, it is important to develop financial models where one can identify different states of the market for modifying one???s actions. In this paper, we propose to use probabilistic fuzzy systems for this purpose. We concentrate on Takagi???Sugeno (TS) probabilistic fuzzy systems that combine interpretability of fuzzy systems with the statistical properties of probabilistic systems. We start by recapitulating the general architecture of TS probabilistic fuzzy rule-based systems and summarize the corresponding reasoning schemes. We mention how probabilities can be estimated from a given data set and how a probability distribution can be approximated by a fuzzy histogram. We apply our methodology for financial time series analysis and demonstrate how a probabilistic TS fuzzy system can be identified, assuming that a linguistic term set is given. We illustrate the interpretability of such a system by inspecting the rule bases of our models.

Keywords: data-driven design; fuzzy reasoning; fuzzy rule base; probabilistic fuzzy systems; time series analysis (search for similar items in EconPapers)
JEL-codes: C45 M M11 R4 (search for similar items in EconPapers)
Date: 2003-04-29
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Citations: View citations in EconPapers (3)

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