ADMIRAL: A data mining based financial trading system

Gil Rachlin, Mark Last, Dima Alberg, Abraham Kandel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

30 Scopus citations

Abstract

This paper presents a novel framework for predicting stock trends and making financial trading decisions based on a combination of Data and Text Mining techniques. The prediction models of the proposed system are based on the textual content of time-stamped web documents in addition to traditional numerical time series data, which is also available from the Web. The financial trading system based on the model predictions (ADMIRAL) is using three different trading strategies. In this paper, the ADMIRAL system is simulated and evaluated on real-world series of news stories and stocks data using the C4.5 Decision Tree Induction Algorithm. The main performance measures are the predictive accuracy of the induced models and, more importantly, the profitability of each trading strategy using these predictions.

Original languageEnglish
Title of host publicationProceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007
Pages720-725
Number of pages6
DOIs
StatePublished - 2007
Event1st IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007 - Honolulu, HI, United States
Duration: 1 Apr 20075 Apr 2007

Publication series

NameProceedings of the 2007 IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007

Conference

Conference1st IEEE Symposium on Computational Intelligence and Data Mining, CIDM 2007
Country/TerritoryUnited States
CityHonolulu, HI
Period1/04/075/04/07

Keywords

  • data mining
  • decision trees
  • financial data processing
  • Internet
  • stock markets
  • text analysis

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing
  • Software
  • Theoretical Computer Science

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