International Journal of Advanced and Applied Sciences

Int. j. adv. appl. sci.

EISSN: 2313-3724

Print ISSN: 2313-626X

Volume 4, Issue 9  (September 2017), Pages:  130-137

Title: Pakistan stock exchange prediction using RIDOR classifier

Author(s):  Wasim Akram, Muhammad Imran *


Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad, Pakistan

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Now-a-days stock market prediction is important activity and interesting topic for professional analyst. The stock market is biggest investment platform, however investment in stock market need accurate and complete information. The accurate and fast-prediction of stock market attracted the investor for profitable output. The stock market prediction is complex task because uncertainty involves in the market movement up/down. Mostly the machine learning techniques (MLT) are used for accurate prediction of stock market, because of its capability of partitioning; extract hidden information form raw data, monitors the fluctuation rate of stock market, suitable for nonlinear data etc. This research work is about to review the strength and weakness of existing stock market prediction techniques. This research work proposed a Ripple-down-rule-learner (RIDOR) classifier based technique. The RIDOR rule base classifier generates default value and work like if-else statement for uncertainties. The other contribution is a prepared data-set using technical indicator to predict stock market trend. The output of the propose model is outperformed as compare to the exiting techniques. 

© 2017 The Authors. Published by IASE.

This is an open access article under the CC BY-NC-ND license (

Keywords: RIDOR, Technical indicators, Stock market, Machine learning techniques, Data preparation

Article History: Received 24 April 2017, Received in revised form 2 August 2017, Accepted 5 August 2017

Digital Object Identifier:


Akram W and Imran M (2017). Pakistan stock exchange prediction using RIDOR classifier. International Journal of Advanced and Applied Sciences, 4(9): 130-137


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