臺灣博碩士論文加值系統

只要是投資就會有風險，為了降低投資風險而產生了投資組合的概念，投資組合的概念是藉由購買一籃子股票以降低投資風險。如何選擇最佳的投資組合，須考慮投資標的的選取以及資金的分配。當一旦選定了投資組合以後，買賣的時間點也很重要，尤其在股票市場中，股價常常受到不確定性的政治和經濟局勢所影響，股價起伏波動頻繁，難以掌握進場及出場的時間點，因此找出好的買賣時機，降低損失及賺取利潤，在股票投資上，也是非常重要的課題。
本研究使用基因演算法 (Genetic Algorithm)作為挑選股票及資金分配的方法。利用基因演算法每一代交配及突變的演化，找出最佳的股票投資組合；並且利用加權模糊時間序列 (Weighted Fuzzy Time Series)，預測出每一候選的股票投資組合的未來價格，計算出未來可能報酬率，作為基因演算法找出最佳股票投資組合的依據。當選定好一組股票投資組合後，本研究對買賣交易擬定兩種策略：一、定期檢查 二、設定停損、停利點。以每日觀察投資報酬是否達到停損，以及定期比較投資組合報酬率找出更好的投資組合，來判定何時應作出買賣決策，經實驗發現，本方法可有效增加投資報酬率。

Investments in a financial market may incur risk. To reduce the risk in an investment, many portfolio selection methods have been introduced. By buying a set of financial assets, portfolio selection aims at maximizing the return of the investment for a given level of risk. To build an optimal portfolio, one needs to select proper assets and to decide the proportion of the investment for each selected asset. On the other hand, prices of assets vary as time goes by. As a result, the return of the portfolio is also varies with time. Thus, besides the portfolio selection, how to discover the selling and buying points of a portfolio is also a very important issue.
In this study, we develop a genetic algorithm to build optimal stock portfolios. The genetic algorithm uses an evolutionary process to find the portfolio with the highest fitness value as the optimal portfolio. In the proposed method, we use a weighted fuzzy time series to predict the return of the stock portfolio, which is then used as the fitness value of the genetic algorithm. Furthermore, we propose a stock portfolio trading model to find the selling and buying points of the stock portfolio. There are two strategies in the stock portfolio trading model: to periodically check the portfolio returns and to set a stop-loss point for the portfolio. With these two strategies we can find the best selling and buying points for the portfolios. Through experiments on the stocks in Taiwan 50, the proposed method outperforms TAIEX index and Taiwan 50 index in terms of 7-year average return.

中文摘要 I
英文摘要 II
圖表目錄 V
第一章緒論 1
1.1 研究背景 1
1.2 研究動機與目的 2
1.3 研究架構 3
第二章相關研究 4
2.1效率市場理論 6
2.2股票投資組合交易之相關文獻 6
2.3模糊集合理論 8
2.3.1模糊集合 8
2.3.2歸屬函數 8
2.4模糊時間序列 11
2.4.1加權模糊時間序列 12
2.5基因演算法 13
2.5.1編碼及初始族群 15
2.5.2適應函數 15
2.5.3選擇 15
2.5.4交配 16
2.5.5突變 17
2.5.6滿足終止條件 18
2.5.7最佳解 18
第三章 研究方法 19
3.1股票組合交易步驟 19
3.1.1選取股票及資金分配 19
3.1.2預測未來報酬率 19
3.1.3買賣策略 22
3.1.4選取股票及資金分配 22
3.1.5計算報酬率 23
3.2實驗方法架構 24
3.3執行例子 25
第四章 實驗數據與分析 33
4.1資料來源與說明 33
4.2衡量對象 34
4.2.1加權股價指數 34
4.2.2台灣50指數 34
4.2.3台灣50 ETF 34
4.3實驗環境與參數設定 35
4.4結果分析 35
4.4.1實驗一：未扣除交易手續費 36
4.4.2實驗二：扣除交易手續費 38
4.4.3統計假設檢定 40
4.4.4買進持有比較 44
第五章 結論與未來展望 48
5.1結論 48
5.2未來展望 49
參考文獻 50

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