5 結論
本研究的主要目的在於利用遺傳演算法及模糊理論中的三角模糊數來探討投資人 情緒與股票報酬之關係。在本研究中，我們建構出三種投資模型：跟隨前一期投資人情 緒樂觀之股票即「買進前一期投資人情緒樂觀之股票」、跟隨前一期投資人情緒悲觀之 股票即「買進前一期投資人情緒悲觀之股票」以及股票排序指標自由演化的投資方法。
在本研究所設計的實驗方法，分為五個部分討論：
第一部分：探討個別投資模型其投資績效
在實驗結果中，我們可以發現不論在選擇分數較高的前 10、20 以及 30 支股票，利 用股票排序指標自由演化的方法都會有不錯的績效；在選擇分數較高的前 20 以及 30 支股票時，本研究所設計的三種投資模型其投資報酬率都能有超過一半以上的機率勝過 所有公司的投資報酬率，且對於買進前一期投資人情緒悲觀之股票的投資策略所獲得的 投資績效會優於另外兩種投資方法。而針對投資人情緒角度來看，不論在選擇分數較高 的前 10、20 以及 30 支股票，買進前一期投資人情緒悲觀之股票的投資策略所獲得的投 資績效會優於買進前一期投資人情緒樂觀之股票的投資策略。
第二部分：探討個別投資模型之累積報酬率
針對本研究所設計的投資模型，我們分別畫出其累積報酬率圖。我們可以發現在選 擇分數較高的前 10、20 以及 30 支股票時，我們研究所設計的演算法能夠建構出績效不 錯(亦即投資模型之累積報酬率勝過投資所有樣本公司的累積報酬率) 的三種投資模 型。在選擇分數較高的前 10 支股票時，股票排序指標自由演化之策略其所獲得的累積 報酬率相較於另外兩種投資模型來得高。在選擇分數較高的前 20、30 支股票時，買進 前一期投資人情緒悲觀股票與股票排序指標自由演化的投資模型都能有不錯的累積報 酬率，而買進前一期投資人情緒樂觀股票的投資模型，相較於另外兩種投資方法，其效 果明顯不彰。
第三部分：探討個別投資模型之投資風險比較
在本研究中，我們也詴著比較其個別投資模型的投資風險。其研究結果發現，我們 所建立的三種投資方法，能夠符合分散風險原則亦即若選擇投資股數愈多，則風險會愈 來愈低。我們所設計的三種投資模型，隨著選擇股數越來越多，其月均化報酬率的標準 差會越來越低。因此，本研究所設計的投資模型，能夠適時降低投資者的投資風險，以 防止在股市中選擇到績效不佳之股票。
第四部分：應用模糊理論對投資績效之貢獻
在實驗結果中，我們可以發現在選擇少數股(選擇 10 股)時，應用模糊理論之方法 所帶來的投資績效的效益不大(較無應用模糊理論的選方法遜色)，但在選擇多數股(選擇 20 股、選擇 30 股以及選擇 40 股)時，應用模糊理論的選股方法，能夠有效提升其投資 績效(較無應用模糊理論的選方法佳)。因此，模糊理論之應用在本研究中，選股數越多 時能夠讓選股模型發揮更大效益。
第五部份：投資人情緒指標之權重值探討
在本研究所提出的股票排序指標自由演化的投資策略，實驗結果發現本益比、股價 淨值比與法人情緒這三項指標所獲得之權重值相對於其它指標高，亦即表示這三項指標 對於股票排序指標自由演化的投資模型有相當之重要性。因此，未來也許能針對這三項 投資人情緒指標對於選股做深入的探討，並深入研究其意義所在。
總結上述，本研究之貢獻為所設計的三種投資模型，若針對投資人情緒來看，買進 前一期投資人情緒悲觀股票的投資模型其投資績效會優於買進前一期投資人情緒樂觀 之股票的投資模型，而股票排序指標自由演化的投資模型不論在選股數低或高時，也能 有較佳的投資效益。另一方面，本研究所建構的三種投資模型能有效帶給投資者減低其
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此外，本研究也發現遺傳演算法對於測詴期長短出現反應不足的地方，隨著訓練期 越短或者測詴期越短，遺傳演算法似乎會顯現出反應不足的地方。未來或許能多加入相 關的投資人情緒指標，或是將研究期間加長亦或是應用非線性模型來調整遺傳演算法整 體的穩健度，是值得深入研究的議題。另外，也能考慮根據投資人情緒的悲觀或樂觀程 度，適時做進出場的交易動作，將經驗與技術加以整合以便發展出能夠獲利的投資工 具，是本研究未來要努力研究的方向。
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