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Forecasting System for Trading Rules in Stock Market using Bi-Clustering Method


Author(s):

Prakhash Raj KS , Arasu Engineering College, Kumbakonam, Tamil Nadu, India.; Rajavignesh R, Arasu Engineering College, Kumbakonam, Tamil Nadu, India.; Arun K, Arasu Engineering College, Kumbakonam, Tamil Nadu, India.

Keywords:

Trading systems, bi-clustering, k-NN algorithm, genetic programming

Abstract:

Analysis of trading rules and trading days are complex for the investors. Trading system influenced by a lot of factors such as, National policies, Economic environment, Supply – demand relationships. The mined patterns are regarded as trading rules and can be classified as three trading actions (buy, sell, and no-action signals). From that an average performance was compared with the conventional trading systems. This research paper proposes the use of bi-clustering mining to discover the trading pattern. It innovatively proposes the use of bi-clustering mining to discover an effective technical trading pattern that contain a combination of indicators from historical financial data series. A modified k- Nearest Neighborhood (k-NN) method is applied to the classification of trading days in the testing period. Experimental result demonstrates.


Other Details:

Manuscript Id :IJSTEV3I6011
Published in :Volume : 3, Issue : 6
Publication Date: 01/01/2017
Page(s): 11-18
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