Hierarchical Fuzzy Case Based Reasoning with Multi-criteria Decision Making for Financial Applications

  • Shanu Sushmita
  • Santanu Chaudhury
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4815)


This paper presents a framework for using a case-based reasoning system for stock analysis in financial market. The unique aspect of this paper is the use of a hierarchical structure for case representation. The system further incorporates a multi-criteria decision-making algorithm which furnishes the most suitable solution with respect to the current market scenario. Two important aspects of financial market are addressed in this paper: stock evaluation and investment planning. CBR and multi-criteria when used in conjunction offer an effective tool for evaluating goodness of a particular stock based on certain factors. The system also suggests a suitable investment plan based on the current assets of a particular investor. Stock evaluation maps to a flat case structure, but investment planning offers a scenario more suited for structuring the case into successive detailed layers of information related to different facets. This naturally leads to a hierarchical case structure.


Similarity Score Mutual Fund Saving Account Investment Plan Case Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Kim, K.-J.: Toward Global Optimization of Case-Based Reasoning Systems for Financial Forecasting. Applied Intelligence 21, 239–249 (2004)zbMATHCrossRefGoogle Scholar
  2. 2.
    Kyong Joo Oh, T.Y.K.: Financial market monitoring by case-based reasoning. Expert Systems with Applications 32 (2007)Google Scholar
  3. 3.
    Chun, Y.J.P.S.H.: Dynamic adaptive ensemble case-based reasoning: application to stock market prediction. Expert Systems with Applications 28, 435–443 (2005)CrossRefGoogle Scholar
  4. 4.
    Ying Wei, Y.W., Li, F., Li, F.: Case-Based Reasoning: An Intelligent Approach Applied for Financial Crises Warning. Springer, Heidelberg (2003)Google Scholar
  5. 5.
    Smyth, P.C.B., Keane, M.T.: Hierarchical case-based reasoning integrating case-based and decompositional problem-solving techniques for plant-control software design. Knowledge and Data Engineering  (2001)Google Scholar
  6. 6.
    Tang, Y.: Time series extrapolation using hierarchical case-based reasoning. Signal Processing, Pattern Recognition, and Applications (2006)Google Scholar
  7. 7.
    Chi-I Hsua, P.-L.H., Chiub, C.: Predicting information systems outsourcing success using a hierarchical design of case-based reasoning  (2004)Google Scholar
  8. 8.
    Singh, T., Goswami, S.C.P.S.: Distributed fuzzy case based reasoning. Elsevier:Journal of Applied Soft Computing 4, 323–343 (2004)Google Scholar
  9. 9.
    Roy, B.: The outranking approach and the foundations of ELECTRE methods ch in book: Readings in Multiple Criteria Decision Aid. In: Costa, C.A.B.e (ed.) Springer, Berlin (1990)Google Scholar
  10. 10.
    Pedro, J.S., Burstien, F.: A framework for case-based fuzzy multi-criteria decision support for tropical cyclone forecasting. In: Proceedings of the 36th Hawaii International Conference on System Sciences(HICSS), IEEE, Los Alamitos (2002)Google Scholar
  11. 11.
    Brans, J.P., Mareschal, B.: The PROMETHEE methods for MCDM; The PROMALC GAIA and BANKADVISER softwarech in book: Readings in Multiple Criteria Decision Aid. In: Costa, C.A.B.e. (ed.) Springer, Berlin (1990)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Shanu Sushmita
    • 1
  • Santanu Chaudhury
    • 1
  1. 1.Electrical Engineering Department, Indian Institute of Technology Delhi, New Delhi, 110016India

Personalised recommendations