| Summary: | 碩士 === 國立中興大學 === 科技管理研究所 === 98 === This study adopts two scientific approaches, data envelopment analysis (DEA) and the analytical hierarchy process (AHP), to arrive at a stock selection strategy for communications and Internet stocks. Though the “Internet Bubble” burst in the year two-thousand, Internet stocks still interest numerous inventors by their high growth potential and profits. Therefore, building a scientific and sound system for stock selection is important. The goals of this thesis were threefold: first, to measure the profitability and marketability of Taiwan communications and Internet companies; second, to compare the important investment criteria for choosing profitable communications and Internet stocks; and third, to rank the priority of Taiwan communications and Internet stocks in the global communications market. The study samples component stocks from sixteen listed communications and Internet companies in the Taiwan Economic Journal (TEJ) Co. database from the year 2003 to 2008. The empirical results are as follows:
1. There were only three communications and Internet companies with 100%
efficiency in 2003. These top three companies were DrayTek Co., Shun on Electronic Co. and Merry Electronics Co. There were only two communications and Internet companies with 100% efficiency in 2005, namely, Compal Communications Inc., and Shun on Electronic Co. In 2006 and 2007, the only company with 100% efficiency was the HTC Co.
2. The study computes the best investment priority in communications and Internet
stocks according to efficiency and stock price. Companies chosen by this rule are well-known and have an excellent reputation in the market. This means that the results of this study are reasonable.
3. The main contribution of this thesis is that it combines DEA with AHP to measure
profitability and marketability, thus providing investors with a quantitative investment tool for selecting efficient and valuable Taiwan communications and Internet stocks. By following the rule in this study, investors, analysis and fund managers will be able to avoid relying too heavily on historical data and subjective decisions in their search for the best portfolios with the lowest risk of investment.
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