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 *
Affiliation(s):
Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad, Pakistan
https://doi.org/10.21833/ijaas.2017.09.018
Full Text - PDF XML
Abstract:
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 (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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:
https://doi.org/10.21833/ijaas.2017.09.018
Citation:
Akram W and Imran M (2017). Pakistan stock exchange prediction using RIDOR classifier. International Journal of Advanced and Applied Sciences, 4(9): 130-137
http://www.science-gate.com/IJAAS/V4I9/Akram.html
References:
- Asadi S, Hadavandi E, Mehmanpazir F, and Nakhostin MM (2012). Hybridization of evolutionary Levenberg–Marquardt neural networks and data pre-processing for stock market prediction. Knowledge-Based Systems, 35: 245-258. https://doi.org/10.1016/j.knosys.2012.05.003
- Babu CN and Reddy BE (2014). A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data. Applied Soft Computing, 23: 27-38. https://doi.org/10.1016/j.asoc.2014.05.028
- Chang PC (2012). A novel model by evolving partially connected neural network for stock price trend forecasting. Expert Systems with Applications, 39(1): 611-620. https://doi.org/10.1016/j.eswa.2011.07.051
- Choudhury S, Ghosh S, Bhattacharya A, Fernandes KJ, and Tiwari MK (2014). A real time clustering and SVM based price-volatility prediction for optimal trading strategy. Neurocomputing, 131: 419-426. https://doi.org/10.1016/j.neucom.2013.10.002
- Dai W, Wu JY, and Lu CJ (2012).Combining nonlinear independent component analysis and neural network for the prediction of Asian stock market indexes. Expert Systems with Applications, 39(4): 4444-4452. https://doi.org/10.1016/j.eswa.2011.09.145
- de Oliveira FA, Nobre CN, and Zárate LE (2013). Applying artificial neural networks to prediction of stock price and improvement of the directional prediction index–case study of PETR4, Petrobras, Brazil. Expert Systems with Applications, 40(18): 7596-7606. https://doi.org/10.1016/j.eswa.2013.06.071
- Devasena CL, Sumathi T, Gomathi VV, and Hemalatha M (2011). Effectiveness evaluation of rule based classifiers for the classification of iris data set. Bonfring International Journal of Man Machine Interface, 1(5): 05-09.
- Farid DM, Al-Mamun MA, Manderick B, and Nowe A (2016). An adaptive rule-based classifier for mining big biological data. Expert Systems with Applications, 64: 305-316. https://doi.org/10.1016/j.eswa.2016.08.008
- Kara Y, Boyacioglu MA, and Baykan ÖK (2011). Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul stock exchange. Expert Systems with Applications, 38(5): 5311-5319. https://doi.org/10.1016/j.eswa.2010.10.027
- Karimi F, Dastgir M, and Shariati M (2014).Index prediction in Tehran stock exchange using hybrid model of artificial neural networks and genetic algorithms. International Journal of Academic Research in Accounting, Finance and Management Sciences, 4(1): 352-357.
- Koklu M, Kahramanli H, and Allahverdi N (2015). Applications of rule based classification techniques for thoracic surgery. In the MakeLearn and TIIM Joint International Conference on Managing Intellectual Capital and Innovation for Sustainable and Inclusive Society: Managing Intellectual Capital and Innovation, ToKnowPress, Bari, Italy: 1991-1998.
- Laboissiere LA, Fernandes RA, and Lage GG (2015). Maximum and minimum stock price forecasting of Brazilian power distribution companies based on artificial neural networks. Applied Soft Computing, 35: 66-74. https://doi.org/10.1016/j.asoc.2015.06.005
- Lakshmi C (2014). Proficiency comparison of zeror, ridor and partclassifiers for intelligent heart disease prediction. International Journal of Advance in Computer Science and Technology, 3(11): 12-18.
- Li X, Xie H, Wang R, Cai Y, Cao J, Wang F, and Deng X (2016). Empirical analysis: stock market prediction via extreme learning machine. Neural Computing and Applications, 27(1): 67-78. https://doi.org/10.1007/s00521-014-1550-z
- Patel J, Shah S, Thakkar P, and Kotecha K (2015a). Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Systems with Applications, 42(1): 259-268. https://doi.org/10.1016/j.eswa.2014.07.040
- Patel J, Shah S, Thakkar P, and Kotecha K (2015b). Predicting stock market index using fusion of machine learning techniques. Expert Systems with Applications, 42(4): 2162-2172. https://doi.org/10.1016/j.eswa.2014.10.031
- Qin B, Xia Y, Prabhakar S, and Tu Y (2009).A rule-based classification algorithm for uncertain data. In the IEEE 25th International Conference on Data Engineering, IEEE, Shanghai, China: 1633-1640. https://doi.org/10.1109/ICDE.2009.164
- Qiu M, Song Y, and Akagi F (2016). Application of artificial neural network for the prediction of stock market returns: The case of the Japanese stock market. Chaos, Solitonsand Fractals, 85: 1-7. https://doi.org/10.1016/j.chaos.2016.01.004
- Shriwas J and Farzana S (2014) Using text mining and rule based technique for prediction of stock market price. International Journal of Emerging Technology and Advanced Engineering, 4(1): 245-250.
- Shriwas J and Sharma SD (2014). Stock price prediction using hybrid approach of rule based algorithm and financial news. International Journal of Computer Technology and Applications, 5(1): 205-211.
- Ticknor JL (2013). A Bayesian regularized artificial neural network for stock market forecasting. Expert Systems with Applications, 40(14): 5501-5506. https://doi.org/10.1016/j.eswa.2013.04.013
- Veeralakshmi V and Ramyachitra D (2015) Ripple down rule learner (RIDOR) classifier for IRIS Dataset. Issues, 1(1): 79-85.
- Wang JJ, Wang JZ, Zhang ZG, and Guo SP (2012). Stock index forecasting based on a hybrid model. Omega, 40(6): 758-766. https://doi.org/10.1016/j.omega.2011.07.008