Testing the Weak Form Efficiency of Shanghai Stock Exchange with Artificial Neural Networks
The efficiency of a market has been a longstanding topic for research. On one side, researchers have provided numerous empirical studies in supporting the market efficiency; on the other side, market participants have been searching for new techniques for the prediction the market and applying vario...
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| Format: | Dissertation (University of Nottingham only) |
| Language: | English |
| Published: |
2007
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| Online Access: | https://eprints.nottingham.ac.uk/21512/ |
| _version_ | 1848792260425023488 |
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| author | Wong, Tai-chi Alick |
| author_facet | Wong, Tai-chi Alick |
| author_sort | Wong, Tai-chi Alick |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | The efficiency of a market has been a longstanding topic for research. On one side, researchers have provided numerous empirical studies in supporting the market efficiency; on the other side, market participants have been searching for new techniques for the prediction the market and applying various rules in trading, aiming to beat the market. Artificial neural networks (ANNs), being one of the artificial intelligence (A.I.) methodologies, have become one of the many options for the stock market predictions. Many researches have shown the capability of the ANNs in forecasting with nonlinear function mapping. The objective of this study is to verify the weak form efficiency of one of the emerging stock market, the Shanghai stock exchange, with the use of artificial neural networks. Simultaneously, an overview of the efficient market hypothesis and the former literatures regarding the development and the application of ANNs would be visited in order to provide a theoretical background for the study. |
| first_indexed | 2025-11-14T18:41:35Z |
| format | Dissertation (University of Nottingham only) |
| id | nottingham-21512 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T18:41:35Z |
| publishDate | 2007 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-215122018-02-22T00:01:58Z https://eprints.nottingham.ac.uk/21512/ Testing the Weak Form Efficiency of Shanghai Stock Exchange with Artificial Neural Networks Wong, Tai-chi Alick The efficiency of a market has been a longstanding topic for research. On one side, researchers have provided numerous empirical studies in supporting the market efficiency; on the other side, market participants have been searching for new techniques for the prediction the market and applying various rules in trading, aiming to beat the market. Artificial neural networks (ANNs), being one of the artificial intelligence (A.I.) methodologies, have become one of the many options for the stock market predictions. Many researches have shown the capability of the ANNs in forecasting with nonlinear function mapping. The objective of this study is to verify the weak form efficiency of one of the emerging stock market, the Shanghai stock exchange, with the use of artificial neural networks. Simultaneously, an overview of the efficient market hypothesis and the former literatures regarding the development and the application of ANNs would be visited in order to provide a theoretical background for the study. 2007 Dissertation (University of Nottingham only) NonPeerReviewed application/pdf en https://eprints.nottingham.ac.uk/21512/1/07MSclixtcaw.pdf Wong, Tai-chi Alick (2007) Testing the Weak Form Efficiency of Shanghai Stock Exchange with Artificial Neural Networks. [Dissertation (University of Nottingham only)] (Unpublished) artificial intelligence artificial neural networks efficient market hypothesis market efficiency Shanghai stock exchange |
| spellingShingle | artificial intelligence artificial neural networks efficient market hypothesis market efficiency Shanghai stock exchange Wong, Tai-chi Alick Testing the Weak Form Efficiency of Shanghai Stock Exchange with Artificial Neural Networks |
| title | Testing the Weak Form Efficiency of Shanghai Stock Exchange with Artificial Neural Networks |
| title_full | Testing the Weak Form Efficiency of Shanghai Stock Exchange with Artificial Neural Networks |
| title_fullStr | Testing the Weak Form Efficiency of Shanghai Stock Exchange with Artificial Neural Networks |
| title_full_unstemmed | Testing the Weak Form Efficiency of Shanghai Stock Exchange with Artificial Neural Networks |
| title_short | Testing the Weak Form Efficiency of Shanghai Stock Exchange with Artificial Neural Networks |
| title_sort | testing the weak form efficiency of shanghai stock exchange with artificial neural networks |
| topic | artificial intelligence artificial neural networks efficient market hypothesis market efficiency Shanghai stock exchange |
| url | https://eprints.nottingham.ac.uk/21512/ |