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Predicting Closing Stock Price using Artificial Neural Network and Adaptive Neuro Fuzzy Inference System (ANFIS): The Case of the Dhaka Stock Exchange

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2015
Authors:
Mustain Billah, Sajjad Waheed, Abu Hanifa
10.5120/ijca2015906952

Mustain Billah, Sajjad Waheed and Abu Hanifa. Article: Predicting Closing Stock Price using Artificial Neural Network and Adaptive Neuro Fuzzy Inference System (ANFIS): The Case of the Dhaka Stock Exchange. International Journal of Computer Applications 129(11):1-5, November 2015. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Mustain Billah and Sajjad Waheed and Abu Hanifa},
	title = {Article: Predicting Closing Stock Price using Artificial Neural Network and Adaptive Neuro Fuzzy Inference System (ANFIS): The Case of the Dhaka Stock Exchange},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {129},
	number = {11},
	pages = {1-5},
	month = {November},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

Stock market prediction plays a vital rule in taking financial decisions. Various factors affecting the stock market makes stock prediction somewhat complex and difficult. Different data mining techniques such as Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS) etc are being widely used for predicting stock prices of different stock exchange cases. But there is no good work on stock prediction using ANN and ANFIS for Bangladesh Stock Markets. The goal of this paper is to find out an efficient soft computing technique for Dhaka Stock Exchange (DSE) closing data prediction. In this paper, ANN and ANFIS have been applied on different companies previous data such as opening price, highest price, lowest price, total share traded. The day end closing price of stock is the outcome of ANN and ANFIS model. Our experiment shows that, ANFIS is more effective and efficient technique to predict Dhaka Stock exchange (DSE) data.

References

  1. Reza Gharoie Ahangar, Mahmood Yahyazadehfar, and Hassan Pournaghshband. The comparison of methods artificial neural network with linear regression using specific variables for prediction stock price in tehran stock exchange.
  2. Doron Avramov. Stock return predictability and model uncertainty. 64(3):423–458.
  3. Asst Birgul Egeli. Stock market prediction using artificial neural networks. 22:171–185.
  4. Johan Bollen, Huina Mao, and Xiaojun Zeng. Twitter mood predicts the stock market. 2(1):1–8.
  5. Melek Acar Boyacioglu and Derya Avci. An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: the case of the istanbul stock exchange. 37(12):7908–7912.
  6. Abdur R. Chowdhury. Statistical properties of daily returns from the dhaka stock exchange. pages 61–76.
  7. David Enke, Manfred Grauer, and Nijat Mehdiyev. Stock market prediction with multiple regression, fuzzy type-2 clustering and neural networks. 6:201–206.
  8. Eugene F. Fama and Kenneth R. French. Common risk factors in the returns on stocks and bonds. 33(1):3–56.
  9. Farhad Soleimanian Gharehchopogh, Tahmineh Haddadi Bonab, and Seyyed Reza Khaze. ALinear REGRESSION APPROACH TO PREDICTION OF STOCK MARKET TRADING VOLUME: ACase STUDY. 4.
  10. Martin T. Hagan, Howard B. Demuth, Mark H. Beale, and others. Neural network design. Pws Pub. Boston.
  11. Tsung-Jung Hsieh, Hsiao-Fen Hsiao, and Wei-Chang Yeh. Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm. 11(2):2510–2525.
  12. Jyh-Shing Roger Jang. ANFIS: adaptive-network-based fuzzy inference system. 23(3):665–685.
  13. Jyh-Shing Roger Jang. Input selection for ANFIS learning. In Proceedings of the fifth IEEE international conference on fuzzy systems, volume 2, pages 1493–1499. Citeseer.
  14. Gurbinder Kaur, Joydip Dhar, and Rangan K. Guha. Stock market forecasting using ANFIS with OWA operator. 12(2):102–114.
  15. Gurbinder Kaur, Joydip Dhar, and Rangan K. Guha. STOCK MARKET PREDICTION FROM SECTORAL INDICES USING AN ADAPTIVE NETWORK BASED FUZZY INFERENCE SYSTEM. 4(2):74.
  16. Bart Kosko. Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence/book and disk.
  17. Jibendu Kumar Mantri, P. Gahan, and Braja B. Nayak. Artificial neural networksan application to stock market volatility. page 179.
  18. David G. McMillan. Stock return, dividend growth and consumption growth predictability across markets and time: Implications for stock price movement. 35:90–101.
  19. M. Hashem Pesaran and Allan Timmermann. Predictability of stock returns: Robustness and economic significance. 50(4):1201–1228.
  20. Han Yan, Zhihong Zou, and Huiwen Wang. Adaptive neuro fuzzy inference system for classification of water quality status. 22(12):1891–1896.

Keywords

Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (Anfis), Stock prediction, DSE, Grameenphone