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Hsinchu, Taiwan

Minghsin University of Science and Technology is a private university in Xinfeng Township, Hsinchu County, Taiwan. Wikipedia.


Hsu C.-M.,Minghsin University of Science and Technology
Expert Systems with Applications | Year: 2011

Stock price prediction is a very important financial topic, and is considered a challenging task and worthy of the considerable attention received from both researchers and practitioners. Stock price series have properties of high volatility, complexity, dynamics and turbulence, thus the implicit relationship between the stock price and predictors is quite dynamic. Hence, it is difficult to tackle the stock price prediction problems effectively by using only single soft computing technique. This study hybridizes a self-organizing map (SOM) neural network and genetic programming (GP) to develop an integrated procedure, namely, the SOM-GP procedure, in order to resolve problems inherent in stock price predictions. The SOM neural network is utilized to divide the sample data into several clusters, in such a manner that the objects within each cluster possess similar properties to each other, but differ from the objects in other clusters. The GP technique is applied to construct a mathematical prediction model that describes the functional relationship between technical indicators and the closing price of each cluster formed in the SOM neural network. The feasibility and effectiveness of the proposed hybrid SOM-GP prediction procedure are demonstrated through experiments aimed at predicting the finance and insurance sub-index of TAIEX (Taiwan stock exchange capitalization weighted stock index). Experimental results show that the proposed SOM-GP prediction procedure can be considered a feasible and effective tool for stock price predictions, as based on the overall prediction performance indices. Furthermore, it is found that the frequent and alternating rise and fall, as well as the range of daily closing prices during the period, significantly increase the difficulties of predicting. © 2011 Elsevier Ltd. All rights reserved. Source


Tsai P.S.M.,Minghsin University of Science and Technology
Expert Systems with Applications | Year: 2010

Association rule mining is an important research topic in the data mining community. There are two difficulties occurring in mining association rules. First, the user must specify a minimum support for mining. Typically it may require tuning the value of the minimum support many times before a set of useful association rules could be obtained. However, it is not easy for the user to find an appropriate minimum support. Secondly, there are usually a lot of frequent itemsets generated in the mining result. It will result in the generation of a large number of association rules, giving rise to difficulties of applications. In this paper, we consider mining top-k frequent closed itemsets from data streams using a sliding window technique. A single pass algorithm, called FCI-max, is developed for the generation of top-k frequent closed itemsets of length no more than max-l. Our method can efficiently resolve the mentioned two difficulties in association rule mining, which promotes the usability of the mining result in practice. © 2010 Elsevier Ltd. All rights reserved. Source


Hsu C.-M.,Minghsin University of Science and Technology
International Journal of Systems Science | Year: 2014

Portfolio optimisation is an important issue in the field of investment/financial decision-making and has received considerable attention from both researchers and practitioners. However, besides portfolio optimisation, a complete investment procedure should also include the selection of profitable investment targets and determine the optimal timing for buying/selling the investment targets. In this study, an integrated procedure using data envelopment analysis (DEA), artificial bee colony (ABC) and genetic programming (GP) is proposed to resolve a portfolio optimisation problem. The proposed procedure is evaluated through a case study on investing in stocks in the semiconductor sub-section of the Taiwan stock market for 4 years. The potential average 6-month return on investment of 9.31% from 1 November 2007 to 31 October 2011 indicates that the proposed procedure can be considered a feasible and effective tool for making outstanding investment plans, and thus making profits in the Taiwan stock market. Moreover, it is a strategy that can help investors to make profits even when the overall stock market suffers a loss. © 2013 Taylor & Francis. Source


Jaing C.-C.,Minghsin University of Science and Technology
Applied Optics | Year: 2011

This study elucidates the effects of columnar angles and deposition angles on the thermal expansion coefficients and intrinsic stress behaviors of MgF 2 films with columnar microstructures. The behaviors associated with temperature-dependent stresses in the MgF2 films are measured using a phase-shifting Twyman-Green interferometer with a heating stage and the application of a phase reduction algorithm. The thermal expansion coefficients of MgF2 films at various columnar angles were larger than those of glass substrates. The intrinsic stress in the MgF2 films with columnar microstructures was compressive, while the thermal stress was tensile. The thermal expansion coefficients of MgF2 films with columnar microstructures and their intrinsic stress evidently depended on the deposition angle and the columnar angle. © 2010 Optical Society of America. Source


Hsu C.-M.,Minghsin University of Science and Technology
Neural Computing and Applications | Year: 2013

Stock/futures price forecasting is an important financial topic for individual investors, stock fund managers, and financial analysts and is currently receiving considerable attention from both researchers and practitioners. However, the inherent characteristics of stock/futures prices, namely, high volatility, complexity, and turbulence, make forecasting a challenging endeavor. In the past, various approaches have been proposed to deal with the problems of stock/futures price forecasting that are difficult to resolve by using only a single soft computing technique. In this study, a hybrid procedure based on a backpropagation (BP) neural network, a feature selection technique, and genetic programming (GP) is proposed to tackle stock/futures price forecasting problems with the use of technical indicators. The feasibility and effectiveness of this procedure are evaluated through a case study on forecasting the closing prices of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) futures of the spot month. Experimental results show that the proposed forecasting procedure is a feasible and effective tool for improving the performance of stock/futures price forecasting. Furthermore, the most important technical indicators can be determined by applying a feature selection method based on the proposed simulation technique, or solely on the preliminary GP forecast model. © 2011 Springer-Verlag London Limited. Source

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