In this thesis, we propose an investment strategy for the world mutual funds. We first define the global trend indicator (GTI) for evaluating the price change trend of the funds in the future. Then, based on GTI, we derive the monitoring indicator (MI) to measure whether the fund market is in the bull or bear state. MI decides the buying or selling signal. The goodness of a fund is determined by some weighted combination of short-term performance and long-term performance. The weights of various performances are decided by the genetic algorithm, which can dynamically adjusted with priorities of interested funds according to their past performances (profitability). When a buying signal is triggered by MI, the funds with high performance are more likely to be picked than those with low performance. In our experimental results from January 1999 to December 2008 (10 years in total), we achieve the annual profit higher than 10%, which is a significant improvement to other existing methods.

This thesis deals with the development of supportive tool for investment in the stock markets. The result is a tool that allows investors in making decisions while investing and also can autonomously invest in stock markets by pre-learned heuristics that can help a potential investor to choose their own tactics for investing.

本研究針對臺灣摩根指數的成分股進行分析，研究樣本期間從2008年至2010年，合計三個年度，正好歷經景氣的一個多空循環週期。本研究利用技術指標作為判讀多空的工具，技術指標包含價與量的技術分析工具，價格的技術指標有趨勢指標MA、擺盪指標KD與MACD，量的技術指標則是OBV。並利用優化的方式挑選出合適的參數值。本研究的風險控管則是控管個股的偏離程度，當允許的偏離程度愈大，模型便愈能區別出強勢股與弱勢股，風險的衡量指標則是採用年化追蹤誤差值來衡量，本研究設定的限制條件為最大累積年化追蹤誤差值不得超越6%。 實證結果發現，當模組的模型年化追蹤誤差值設定愈大，個股的偏離程度就愈大，模組的報酬表現就愈佳，但同樣的風險也愈大，即年化追蹤誤差值愈大。當模型年化追蹤誤差值設定在24%，並搭配MA、MACD與OBV三個技術指標得到的績效最佳，同時亦能夠將風險控制在設定的6%水準之下。 / This study analyzed the component stocks in MSCI Taiwan Index. The analyzed data from 2008 to 2010 was exactly an economic cycle. The study was based on technical analysis, including price and volume to judge that the price was bullish or bearish. The price technical analysis included Moving Average (MA), Stochastic Line (KD) and Moving Average Convergence and Divergence (MACD). The volume technical analysis was On Balance Volume (OBV). The study used the method of optimization to choose the best parameter of each technical analysis. The risk control was to limit the bias of each stock. When the bias of each stock was larger, the model could easily distinguish the stock was bullish or bearish. The risk indicator was annual tracking error limited to 6% in the study. The empirical results showed that the larger the model annual tracking error set, the large bias the stock show, and the outperformance of the return. But with the performance of the return larger, the risk of tracking error was also getting larger. When the model annual tracking error set to 24%, and utilized MA, MACD and OBV would get the best performance and the risk of annual tracking error was under 6%.