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Classification and Preprocessing in the Stock Data

  • Przemysław JuszczukEmail author
  • Jan Kozak
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 303)

Abstract

In this paper we deal with the problem of assigning classes to the given market situation. We consider approach in which every market situation can be connected with one of the following decision classes: BUY, SELL or WAIT. Each of two classes: BUY and SELL can be assigned only on the basis of significant rises or drops of the given instrument. In all remaining cases WAIT class is assigned. Such approach allows to be independent of indicator values which nowadays are considered to have the significant prediction power. To achieve the goal we selected various stock instruments and with the use of the preprocessing and data discretization we generated decision tables for every considered datasets.

Furthermore, decision trees is built on the basis of generated decision tables. Decision trees are used in the process of classification of newly generated stock data. Presented approach is tested with the use of two independent sets: training set – used to built classifiers – decision classes, and test set – used to estimate accuracy of the generated decision trees. Finally we refer results to other approach in which forex data were used.

Keywords

Stock data Machine learning Decision trees Data classification 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Knowledge Engineering, Faculty of Informatics and CommunicationUniversity of EconomicsKatowicePoland

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