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Vector DNF for Datasets Classifications: Application to the Financial Timing Decision Problem
Abstract
Traditional classification approaches consider a dataset formed by an archive of observations classified as positive or negative according to a binary classification rule. In this paper we consider the Financial Timing Decision Problem which is the problem of deciding the time when it is profitable for the investor to buy shares or to sell shares or to wait in the stock exchange market. The decision is based on classifying a dataset of observations, represented by a vector containing the values of some financial numerical attributes, according to a ternary classification rule. We propose a new technique based on partially defined vector Boolean functions. We test our technique on different time series of the Mibtel stock exchange market in Italy, and we show that it provides a high classification accuracy as well as wide applicability for other classification problems where a classification in three or more classes is needed.
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