Pemodelan data indeks komposit Kuala Lumpur menggunakan neurofuzzy
Stock market transaction is one of the most popular investments activities. There are many conventional techniques being used as a tool to predict the behaviour of the stock market, and these include technical and fundamental analysis. Recently, Artificial Intelligence (AI) such as Artificial Neural...
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| Format: | Thesis |
| Language: | English |
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2006
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| Online Access: | http://eprints.utm.my/5508/1/NoorKhaidaWatiMFS2006.pdf |
| _version_ | 1796853926571540480 |
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| author | Mohd. Yunos, Zuriahati |
| author_facet | Mohd. Yunos, Zuriahati |
| author_sort | Mohd. Yunos, Zuriahati |
| collection | ePrints |
| description | Stock market transaction is one of the most popular investments activities. There are many conventional techniques being used as a tool to predict the behaviour of the stock market, and these include technical and fundamental analysis. Recently, Artificial Intelligence (AI) such as Artificial Neural Networks (ANN), Genetic Algorithms (GA), Fuzzy Logic (FL) and Rough Set (RS) are widely used by the researchers due to their ability to predict the behaviour of the stock market efficiently. In this research, a comprehensive pre-processing data modeling of stock market is developed to acquire granular informations that represent the behaviour of the data that is to be fed to the classifier. The pre-process methodology includes splitting, scaling, normalization, feature selection, and follows by the Ten-Fold Cross Validation method as a benchmark for estimating the predictive accuracy and effectiveness of splitting and selecting the input data. Daily data of Kuala Lumpur Composite Index (KLCI) is captured and analyzed, and it is found that the movements of the Indices are unstable; hence the forecasting process becomes difficult. Therefore, in this study, a Hybrid Neurofuzzy approached using Adaptive Neurofuzzy Inference System (ANFIS) model is suggested to predict the behaviour of the Indices. Furthermore, technical indicator such as moving average, relative strength index, stochastic indicator and price of change are used to analyze the data, and these parameters become input to ANFIS. Root Mean Square Error (RMSE) and Mean Absolute Percentage of Error (MAPE) are chosen to measure the prediction accuracy. In addition, to verify the effectiveness of the ANFIS model, the economic related factors as well as natural disaster are also provided. These factors such as tsunami, human actions, politics and even psychological have influenced on stock movement, thus compliance with the proposed model The results are promising and conforming with the actual price of Composite Index at Bursa Saham Malaysia. |
| first_indexed | 2024-03-05T18:06:49Z |
| format | Thesis |
| id | utm.eprints-5508 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T18:06:49Z |
| publishDate | 2006 |
| record_format | dspace |
| spelling | utm.eprints-55082018-03-10T12:10:59Z http://eprints.utm.my/5508/ Pemodelan data indeks komposit Kuala Lumpur menggunakan neurofuzzy Mohd. Yunos, Zuriahati QA75 Electronic computers. Computer science Stock market transaction is one of the most popular investments activities. There are many conventional techniques being used as a tool to predict the behaviour of the stock market, and these include technical and fundamental analysis. Recently, Artificial Intelligence (AI) such as Artificial Neural Networks (ANN), Genetic Algorithms (GA), Fuzzy Logic (FL) and Rough Set (RS) are widely used by the researchers due to their ability to predict the behaviour of the stock market efficiently. In this research, a comprehensive pre-processing data modeling of stock market is developed to acquire granular informations that represent the behaviour of the data that is to be fed to the classifier. The pre-process methodology includes splitting, scaling, normalization, feature selection, and follows by the Ten-Fold Cross Validation method as a benchmark for estimating the predictive accuracy and effectiveness of splitting and selecting the input data. Daily data of Kuala Lumpur Composite Index (KLCI) is captured and analyzed, and it is found that the movements of the Indices are unstable; hence the forecasting process becomes difficult. Therefore, in this study, a Hybrid Neurofuzzy approached using Adaptive Neurofuzzy Inference System (ANFIS) model is suggested to predict the behaviour of the Indices. Furthermore, technical indicator such as moving average, relative strength index, stochastic indicator and price of change are used to analyze the data, and these parameters become input to ANFIS. Root Mean Square Error (RMSE) and Mean Absolute Percentage of Error (MAPE) are chosen to measure the prediction accuracy. In addition, to verify the effectiveness of the ANFIS model, the economic related factors as well as natural disaster are also provided. These factors such as tsunami, human actions, politics and even psychological have influenced on stock movement, thus compliance with the proposed model The results are promising and conforming with the actual price of Composite Index at Bursa Saham Malaysia. 2006-06 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/5508/1/NoorKhaidaWatiMFS2006.pdf Mohd. Yunos, Zuriahati (2006) Pemodelan data indeks komposit Kuala Lumpur menggunakan neurofuzzy. Masters thesis, Universiti Teknologi Malaysia, Faculty of Computer Science and Information System. |
| spellingShingle | QA75 Electronic computers. Computer science Mohd. Yunos, Zuriahati Pemodelan data indeks komposit Kuala Lumpur menggunakan neurofuzzy |
| title | Pemodelan data indeks komposit Kuala Lumpur menggunakan neurofuzzy |
| title_full | Pemodelan data indeks komposit Kuala Lumpur menggunakan neurofuzzy |
| title_fullStr | Pemodelan data indeks komposit Kuala Lumpur menggunakan neurofuzzy |
| title_full_unstemmed | Pemodelan data indeks komposit Kuala Lumpur menggunakan neurofuzzy |
| title_short | Pemodelan data indeks komposit Kuala Lumpur menggunakan neurofuzzy |
| title_sort | pemodelan data indeks komposit kuala lumpur menggunakan neurofuzzy |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/5508/1/NoorKhaidaWatiMFS2006.pdf |
| work_keys_str_mv | AT mohdyunoszuriahati pemodelandataindekskompositkualalumpurmenggunakanneurofuzzy |