Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/17831
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dc.contributor.authorPoon, Tze Kee.-
dc.date.accessioned2009-06-16T02:40:35Z-
dc.date.available2009-06-16T02:40:35Z-
dc.date.copyright2009en_US
dc.date.issued2009-
dc.identifier.urihttp://hdl.handle.net/10356/17831-
dc.description.abstractArtificial Neural Network (ANN) is a computer programme that mimics the cognitive processes of the human brain. Empirical researches show that ANN is a viable alternative to traditional statistical methods. ANN is a technique that used to be heavily researched and used widely in engineering and scientific fields for various purposes ranging from control systems to artificial intelligence. But now, due to its astonishing generalization power, financial researchers and practitioners are taking an interest in the feasibility of applying ANN in financial. This research attempts to explore the usefulness of neural network in stock index in stock index forecasting in the European context 30 days in the future. Technical indicators are used to train the ANN to forecast three European stock market indices. They are namely the Financial Times Stock Exchange 100 stock index (FTSE100), Compagnie Nationale des Agents de Change (CAC40) and last but not least, Deutscher Aktien-Index (DAX30). Optimized inputs are also obtained by trials and errors. Input variables found to be useful in forecasting stock indices include 2-years historical opening, high, low, close prices, simple moving average of closing price and volume, relative strength index of price and major world stock indices. Experiments were also carried out to find the most appropriate network parameters that generate the most ideal results. Comparisons were also done between the predicted outputs with the previous 30 days stock prices. Thus, a list of promising stocks can be selected and be recommended to the investors. The returns from simulated trading also calculated in this study.en_US
dc.format.extent65 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University-
dc.subjectDRNTU::Engineering::Computer science and engineering::Computing methodologiesen_US
dc.titleCross European markets examination of using neural network for stock pickingen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorQuah Tong Sengen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineeringen_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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