On testing efficiency of karachi stock exchange using computational intelligence
Faculty / School
Faculty of Computer Sciences (FCS)
Department
Department of Computer Science
Was this content written or created while at IBA?
Yes
Document Type
Conference Paper
Publication Date
11-17-2009
Conference Name
2009 International Conference on Information and Financial Engineering
Conference Location
Singapore
Conference Dates
17-20 April 2009
ISBN/ISSN
70449424352 (Scopus)
First Page
32
Last Page
36
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Keywords
Computational intelligence, Efficient market hypothesis, Forecasting, Karachi stock exchange, Neural networks, Particle swarm optimization
Abstract / Description
This paper tests the efficiency of the Karachi stock market. The efficient market hypothesis suggests that the current price of an asset reflects all information that can be obtained from historical data. According to the proponents of this hypothesis, the best strategy in the absence of any predictive ability is to buy and hold. The paper compares this buy and hold strategy against a computational intelligence based trading strategy which predicts the price of an asset. The prediction is then used to identify the buying and selling points of the asset. The strategy is based on neural networks whose weights are optimized through particle swarm optimization. Both buy and hold and computational intelligence based strategies are tested on KSE100 index values for the period June 2004 to April 2007. The results show that the computational intelligence based strategy out performs the buy and hold strategy.
DOI
https://doi.org/10.1109/ICIFE.2009.31
Recommended Citation
Haider, S., & Nishat, M. (2009). On testing efficiency of karachi stock exchange using computational intelligence., 32-36. https://doi.org/10.1109/ICIFE.2009.31
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