Browsing by Author "Huang, Chiung-Fen"

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Application of Self-Organizing Mapping Neural Network for Discovery of Market Behavior of Equity Fund
(2009)
Chen, Jen-Hua
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Huang, Chiung-Fen
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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
Maximizing the profit and minimizing the loss notwithstanding the trend of the market is always desirable in any investment strategy. The present research develops an investment strategy, which has been verified effective in the real world, by employing self-organizing map neural network for mutual funds and tracking the trends of stock market indices according to macro-economy indicators, weighted indices, and rankings of mutual funds. Our experiment shows if utilizing strategy 3 according to our model during a period from January 2002 to December 2008 the total returns could be at 122 percents even though the weighted index falls 22 percents and averaged investment returns for random transaction strategies stand at minus 25 percents during the same period. As such, we conclude that our model does efficiently increase the investment return.
Exchange Traded Fund Day-Trade Investment Strategy Formulation Based on Knowledge Discovery
(2011-08-01)
Huang, Chiung-Fen
;
Chen, An-Pin
;
資訊管理與財務金融系註：原資管所+財金所
;
Department of Information Management and Finance
There are numerous studies utilizing conventional APRIORI algorithms to extract information from data of mass volume. Unfortunately, the conventional algorithms are designed to handle the information in the same transaction or on the same transaction day, and thus are not suitable for predicting the trend of a market. This paper utilizes a modified APRIORI algorithm called Multi-Dimension Non-Continuous (MDNC) to eliminate the limitations imposed by traditional pattern matching of continuous data before mining the associated rules in the cross-day discrete trading data for formulating exchange traded fund (ETF) day-trade investment strategy. This paper further capitalizes on characteristics including low cost and tax and trading flexibility associated with ETF to develop the day-trade strategy with high probability of positive investment return. Our model verification suggests that the approach proposed by the present paper outperforms Random Walk investment strategy, in terms of the investment return and risk level notwithstanding the overall economy.
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
;
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.