Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/147715
Authors: 
Huang, Chin-yin
Lin, Philip K. P.
Year of Publication: 
2014
Citation: 
[Journal:] Cogent Economics & Finance [ISSN:] 2332-2039 [Volume:] 2 [Year:] 2014 [Issue:] 1 [Pages:] 2-17
Abstract: 
Stock market is considered too uncertain to be predictable. Many individuals have developed methodologies or models to increase the probability of making a profit in their stock investment. The overall hit rates of these methodologies and models are generally too low to be practical for real-world application. One of the major reasons is the huge fluctuation of the market. Therefore, the current research focuses in the stock forecasting area is to improve the accuracy of stock trading forecast. This paper introduces a system that addresses the particular need. The system integrates various data mining techniques and supports the decision-making for stock trades. The proposed system embeds the top-down trading theory, artificial neural network theory, technical analysis, dynamic time series theory, and Bayesian probability theory. To experimentally examine the trading return of the presented system, two examples are studied. The first uses the Taiwan Semiconductor Manufacturing Company (TSMC) data-set that covers an investment horizon of 240 trading days from 16 February 2011 to 23 January 2013. Eighty four transactions were made using the proposed approach and the investment return of the portfolio was 54% with an 80.4% hit rate during a 12-month period in which the TSMC stock price increased by 25% (from $NT 78.5 to $NT 101.5). The second example examines the stock data of Evergreen Marine Corporation, an international marine shipping company. Sixty four transactions were made and the investment return of the portfolio was 128% in 12 months. Given the remarkable investment returns in trading the example TSMC and Evergreen stocks, the proposed system demonstrates promising potentials as a viable tool for stock market forecasting.
Subjects: 
forecasting
decision-making
top-down trading
artificial neural network
dynamic time series
Bayesian probability
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/3.0/
Document Type: 
Article

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