Development of an evolutionary fuzzy expert system for estimating future behavior of stock price
The stock market has always been an attractive area for researchers since no method has been found yet to predict the stock price behavior precisely. Due to its high rate of uncertainty and volatility, it carries a higher risk than any other investment area, thus the stock price behavior is difficul...
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| Veröffentlicht in: | Journal of industrial engineering international Jg. 13; H. 1; S. 29 - 46 |
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Springer
01.03.2017
Springer Berlin Heidelberg Islamic Azad University, South Tehran Branch |
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| ISSN: | 2251-712X, 1735-5702, 2251-712X |
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| Abstract | The stock market has always been an attractive area for researchers since no method has been found yet to predict the stock price behavior precisely. Due to its high rate of uncertainty and volatility, it carries a higher risk than any other investment area, thus the stock price behavior is difficult to simulation. This paper presents a ''data mining-based evolutionary fuzzy expert system'' (DEFES) approach to estimate the behavior of stock price. This tool is developed in seven-stage architecture. Data mining is used in three stages to reduce the complexity of the whole data space. The first stage, noise filtering, is used to make our raw data clean and smooth. Variable selection is second stage; we use stepwise regression analysis to choose the key variables been considered in the model. In the third stage, K-means is used to divide the data into subpopulations to decrease the effects of noise and rebate complexity of the patterns. At next stage, extraction of Mamdani type fuzzy rule-based system will be carried out for each cluster by means of genetic algorithm and evolutionary strategy. In the fifth stage, we use binary genetic algorithm to rule filtering to remove the redundant rules in order to solve over learning phenomenon. In the sixth stage, we utilize the genetic tuning process to slightly adjust the shape of the membership functions. Last stage is the testing performance of tool and adjusts parameters. This is the first study on using an approximate fuzzy rule base system and evolutionary strategy with the ability of extracting the whole knowledge base of fuzzy expert system for stock price forecasting problems. The superiority and applicability of DEFES are shown for International Business Machines Corporation and compared the outcome with the results of the other methods. Results with MAPE metric and Wilcoxon signed ranks test indicate that DEFES provides more accuracy and outperforms all previous methods, so it can be considered as a superior tool for stock price forecasting problems. |
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| AbstractList | The stock market has always been an attractive area for researchers since no method has been found yet to predict the stock price behavior precisely. Due to its high rate of uncertainty and volatility, it carries a higher risk than any other investment area, thus the stock price behavior is difficult to simulation. This paper presents a "data mining-based evolutionary fuzzy expert system" (DEFES) approach to estimate the behavior of stock price. This tool is developed in seven-stage architecture. Data mining is used in three stages to reduce the complexity of the whole data space. The first stage, noise filtering, is used to make our raw data clean and smooth. Variable selection is second stage; we use stepwise regression analysis to choose the key variables been considered in the model. In the third stage, K-means is used to divide the data into sub-populations to decrease the effects of noise and rebate complexity of the patterns. At next stage, extraction of Mamdani type fuzzy rule-based system will be carried out for each cluster by means of genetic algorithm and evolutionary strategy. In the fifth stage, we use binary genetic algorithm to rule filtering to remove the redundant rules in order to solve over learning phenomenon. In the sixth stage, we utilize the genetic tuning process to slightly adjust the shape of the membership functions. Last stage is the testing performance of tool and adjusts parameters. This is the first study on using an approximate fuzzy rule base system and evolutionary strategy with the ability of extracting the whole knowledge base of fuzzy expert system for stock price forecasting problems. The superiority and applicability of DEFES are shown for International Business Machines Corporation and compared the outcome with the results of the other methods. Results with MAPE metric and Wilcoxon signed ranks test indicate that DEFES provides more accuracy and outperforms all previous methods, so it can be considered as a superior tool for stock price forecasting problems. The stock market has always been an attractive area for researchers since no method has been found yet to predict the stock price behavior precisely. Due to its high rate of uncertainty and volatility, it carries a higher risk than any other investment area, thus the stock price behavior is difficult to simulation. This paper presents a “data mining-based evolutionary fuzzy expert system” (DEFES) approach to estimate the behavior of stock price. This tool is developed in seven-stage architecture. Data mining is used in three stages to reduce the complexity of the whole data space. The first stage, noise filtering, is used to make our raw data clean and smooth. Variable selection is second stage; we use stepwise regression analysis to choose the key variables been considered in the model. In the third stage, K -means is used to divide the data into sub-populations to decrease the effects of noise and rebate complexity of the patterns. At next stage, extraction of Mamdani type fuzzy rule-based system will be carried out for each cluster by means of genetic algorithm and evolutionary strategy. In the fifth stage, we use binary genetic algorithm to rule filtering to remove the redundant rules in order to solve over learning phenomenon. In the sixth stage, we utilize the genetic tuning process to slightly adjust the shape of the membership functions. Last stage is the testing performance of tool and adjusts parameters. This is the first study on using an approximate fuzzy rule base system and evolutionary strategy with the ability of extracting the whole knowledge base of fuzzy expert system for stock price forecasting problems. The superiority and applicability of DEFES are shown for International Business Machines Corporation and compared the outcome with the results of the other methods. Results with MAPE metric and Wilcoxon signed ranks test indicate that DEFES provides more accuracy and outperforms all previous methods, so it can be considered as a superior tool for stock price forecasting problems. The stock market has always been an attractive area for researchers since no method has been found yet to predict the stock price behavior precisely. Due to its high rate of uncertainty and volatility, it carries a higher risk than any other investment area, thus the stock price behavior is difficult to simulation. This paper presents a ''data mining-based evolutionary fuzzy expert system'' (DEFES) approach to estimate the behavior of stock price. This tool is developed in seven-stage architecture. Data mining is used in three stages to reduce the complexity of the whole data space. The first stage, noise filtering, is used to make our raw data clean and smooth. Variable selection is second stage; we use stepwise regression analysis to choose the key variables been considered in the model. In the third stage, K-means is used to divide the data into subpopulations to decrease the effects of noise and rebate complexity of the patterns. At next stage, extraction of Mamdani type fuzzy rule-based system will be carried out for each cluster by means of genetic algorithm and evolutionary strategy. In the fifth stage, we use binary genetic algorithm to rule filtering to remove the redundant rules in order to solve over learning phenomenon. In the sixth stage, we utilize the genetic tuning process to slightly adjust the shape of the membership functions. Last stage is the testing performance of tool and adjusts parameters. This is the first study on using an approximate fuzzy rule base system and evolutionary strategy with the ability of extracting the whole knowledge base of fuzzy expert system for stock price forecasting problems. The superiority and applicability of DEFES are shown for International Business Machines Corporation and compared the outcome with the results of the other methods. Results with MAPE metric and Wilcoxon signed ranks test indicate that DEFES provides more accuracy and outperforms all previous methods, so it can be considered as a superior tool for stock price forecasting problems. |
| Author | Asadi, Shahrokh Mehmanpazir, Farhad |
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| Cites_doi | 10.1002/int.20456 10.1109/TKDE.2005.39 10.1016/S0165-0114(98)00349-2 10.1016/j.eswa.2009.11.020 10.1016/j.ins.2014.09.038 10.1007/s12555-010-0325-2 10.1186/2251-712X-9-1 10.1007/s40092-014-0060-z 10.1016/j.knosys.2016.01.005 10.1016/j.eswa.2011.11.002 10.1109/TSMCB.2004.842257 10.1186/2251-712X-8-5 10.1016/j.neucom.2014.09.018 10.1186/2251-712X-8-24 10.1109/TFUZZ.2011.2147794 10.1016/j.eswa.2010.08.083 10.1080/18756891.2012.685272 10.1016/j.ijar.2015.02.001 10.1016/j.asoc.2009.07.001 10.1016/j.knosys.2013.01.014 10.1016/j.neucom.2016.01.010 10.1016/j.ifacol.2015.06.109 10.1016/S0169-2070(01)00093-0 10.1016/j.eswa.2006.04.007 10.1007/s40092-015-0121-y 10.1016/j.patcog.2011.01.017 10.1186/2251-712X-8-21 10.1016/j.neucom.2013.05.023 10.1016/j.eswa.2006.08.020 10.1016/j.knosys.2014.04.018 10.1016/j.neucom.2014.03.026 10.1016/j.knosys.2009.02.006 10.1016/j.ijar.2013.09.014 10.1016/j.knosys.2012.05.003 10.1016/j.knosys.2010.05.004 10.1016/j.knosys.2010.11.001 10.1016/j.knosys.2013.08.006 10.1016/j.procs.2015.03.200 10.1007/s11063-008-9085-x 10.1007/s12065-007-0001-5 10.1016/j.neucom.2008.09.029 10.1007/3-540-46027-6 10.1142/4177 10.1007/978-3-319-16598-1_5 10.1109/ISDA.2005.85 10.1109/IJCNN.2000.861443 10.1186/2251-712X-8-1 10.1002/9780470061190 |
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| Keywords | Fuzzy expert system Noise filtering Data mining Evolutionary strategy Genetic algorithm Stock price forecasting |
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| SubjectTerms | Data mining Engineering Engineering Economics Evolutionary strategy Facility Management Fuzzy expert system Genetic algorithm Industrial and Production Engineering Logistics Marketing Mathematical and Computational Engineering Noise filtering Organization Original Research Quality Control Reliability Safety and Risk Stock price forecasting |
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