A Five Line Stave Decision Model for Constructing a Big Trend Decision Support System for ETF Mutual Fund Investment
Journal: International Journal of Engineering Research (IJER) (Vol.6, No. 2)Publication Date: 2017-02-01
Authors : Weissor Shiue; Hao-Wei Chen; Wei-Sheng Liang; Annie Y.H. Chou; Frank S.C. Tseng;
Page : 61-65
Keywords : Five Line Stave Decision Model (5LSDM); Big Trend; Exchange Traded Funds (ETF);
Abstract
Stock investment is an easy and direct way for investors, but the literature shows that almost 80% of professional investment institutions (institutional investors)cannot beat the global market when trackingtheir performances for more than 20 years.To provide more direct and convenient investing channels, some professional investment institutions eager to issue Exchange-Traded Funds (ETF) for their clients. In this paper, we will propose a five line stave decision model, named 5LSDM, based on tracking the trend line as the center line, then respectively add one and two standard deviations to form the upper andtop lines, and finally subtract one and two standard deviations to form thelower and bottom lines, respectively. Then, we construct a decision support system by regarding the top and upper lines as reference signals of selling, and by considering the bottom and lower lines as reference signals of buying.Meanwhile, in order to avoid grasping a falling knife in bear market, and releasing the target in bull market when the prices are sharply falling or soaring, respectively, we have extended the model by adding a big trend operating condition as a trading basis. An experimental study has also been conducted by selecting five cases of transactions, including Vanguard Total Stock Market ETF (VTI), iShares MSCI ACWI (ACWI), iShares MSCI Russia Capped (ERUS), iShares MSCI Brazil Capped (EWZ), and Yuanta/P-shares Taiwan Top 50 ETF (0050.TW) by collecting data from 2016/01/01 to 2016/07/08. The derived average ROIis nearly 26.75%, which is higher than the 4.46% performance of S&P500 based on the same experimental period.
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Last modified: 2017-02-02 19:02:27