Browsing by Author "Wu, Mei-Chen"

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Data Mining Application to Financial Market to Discover the Behavior of Entry Point - A Case Study of Taiwan Index Futures Market
(2016)
Wu, Mei-Chen
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Yang, Bo-Wen
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Lin, Chiou-Hung
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Huang, Ya-Hui
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Chen, An-Pin
The value of the investment method is that investors who are anxious to pursue, there are many value investing methods have been proposed, but only a minority of the value investing method were proved to be effective. The study is based on messages generated defined value of the investment by Steidlmayer in 1984 proposed market profile theory. In order to extract trading behavior of dealer and product value by the huge financial trading information, the model used the trading data to capture feature patterns, and find the double distribution trend day generated by market profile. The experimental results show the single print as an entry point, and the reference to historical support and pressure line as an exit point. The results show the returns are 24.09 points and the accuracy achieved 57.45%. The results had shown the analysis model can find the investment goods real value from the huge trading information, and help investors obtain excess returns.
The Discovery of Financial Market Behavior Integrated Data Mining on ETF in Taiwan
(2016)
Yang, Bo-Wen
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Wu, Mei-Chen
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Lin, Chiou-Hung
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Huang, Chiung-Fen
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Chen, An-Pin
In practice, many physics principles have been employed to derive various models of financial engineering. However, few studies have been done on the feature selection of finance on time series data. The purpose of this paper is to determine if the behavior of market participant can be detected from historical price. For this purpose, the proposed algorithm utilizes back propagation neural network (BPNN) and works with new feature selection approach in data mining, which is used to generate more information of market behavior. This study is design for exchange-traded fund (ETF) to develop the day-trade strategy with high profit. The results show that BPNN hybridized with financial physical feature, as compared with the traditional approaches such as random walk, typically result in better performance.
Treand Behavior Research by Pattern Analysis in Financial Big data - A Case Study of Taiwan Index Futures Market
(2016-01-01)
Lee, Anthony
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Wu, Mei-Chen
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Chen, An-Pin
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資訊管理與財務金融系註：原資管所+財金所
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Department of Information Management and Finance
Market structure provides concrete information about the market. Price patterns can be imagined as the evidence of a supply and demand states in the market. Price shifts higher as the demands exceed the available supply and vice-versa. These patterns convey precious information about what is going to happen in the market. The purpose of this study is to investigate the underlying relation between price pattern in Taiwan Futures Exchange (TAIFEX) Futures Index Market and its following trend. Forecasting the directions of price shift following the pattern through supervised learning and testing with artificial neural network (ANN). This research implements changepoint-analysis (CPA) under statistics field, and perceptually important points (PIP) theory. CPA finds the locations where the shifts in value occur. Then, PIP algorithm performs the feature extraction of the pattern. Then, the PIP is then fed to ANN to forecast the following trends. To simulate the research concept, a control model is built based on online time segmentation algorithm for comparison. The results of this research shows that robust patterns found by CPA have the ability to forecast market trend direction up to 83.6% accuracy. The result indicates that TAIFEX Futures market directions can be forecasted through its historical price robust patterns. Thus, rejecting that TAIFEX Futures Index Market follows random walk theory. In contrast, the control model which was built based on online time segmentation also has the ability to forecast but not as accurate as using the CPA method. In conclusion, analyzing the patterns reflected in the market effectively provide precious insights about its trends behavior.