隨著低利率時代來臨，投資者為了追求較高的報酬率，開始把資金投入股票投資市場，然而股市行情變換迅速，真正獲利的投資者不多，只有在適當時機點進場交易的投資者才能從中獲利。ㄧ般投資者大多利用技術指標做為進場時機的依據，然而使用技術指標會有ㄧ些問題，例如技術指標的選擇、互相矛盾或類似等問題，導致ㄧ般投資人很難利用這些資訊來輔助股市投資決策。
本研究結合馬可夫決策過程與遺傳演算法，提出新的分析架構，建立一個股市投資策略制訂的決策支援系統。本研究利用馬可夫決策過程具有的預測特性和近期資料即時分析能力，利用馬可夫決策過程在短期資料優秀的分析能力，解析過去歷史資料，達到擇時效果，再結合遺傳演算法的特殊編碼方式與極佳搜尋能力，以字串編碼表達不同的投資策略，以搜尋能力來求解出最佳投資策略，達到選股和資金配置效果。在資金與手中持股不足時，可透過此模型具有的融資融券方式來完成交易。經實驗證實本模型可以得到較高的報酬。

With the low interest rate coming, investors start to buy stocks to get more rewards. However, the stock market varied rapidly, seldom investors can get excess returns when trade in the proper time. Most investors use technical indicators as a tool for market timing. However using technical indicators has some problems, such as the choice of technical indicators, conflicting or similar and other prolems. So most investors are difficult to use those informations to determine stock market investment decisions.
This research combines Markov decision process and genetic algorithms to propose a new analytical framework and to develop the decision support system for making the stock trading strategies. This paper uses the prediction characteristics and real-time analysis capabilities of the Markov decision process to do timing decision. Doing the stock selection and fund allocation by using the string encoding to express different investment strategies and the search capabilities to solve the best investment strategy. Besides, when investors have no sufficient money and stocks, the architecture of this research can complete the transaction by credit transactions. By the experiments, it can confirm that the model of this research can get higher reward.

目錄
壹、 緒論	1
第一節、 研究背景與動機	1
第二節、 研究目的	2
貳、 文獻探討	3
第一節、 演化計算與股票投資組合	3
第二節、 馬可夫決策過程與股票投資組合	6
參、 研究架構	8
第一節、 進場訊號—累積報酬率之馬可夫決策過程	9
第二節、 買賣交易策略—隔日預期報酬率之馬可夫決策過程	11
第三節、 選股與資金配置(融資、融券)—遺傳演算法求解	12
第四節、 整合與交易過程	15
肆、 實驗分析	18
第一節、 一般遺傳演算法(SGA)	18
第二節、 馬可夫整合遺傳演算法	29
第三節、 綜合實驗評比	38
伍、 結論與未來展望	55
參考文獻	56

圖目錄
圖1 研究架構圖 9
圖2 累積報酬率狀態變數	10
圖3 隔日預期報酬率狀態變數	11
圖4 染色體編碼	13
圖5 交配 14
圖6 突變 14
圖7 進場訊號、買賣訊號與染色體資產配置(融資、融券)	16
圖8 SGA染色體交配示意圖	19
圖9 SGA突變示意圖	19
圖10 SGA交易策略示意圖	20
圖11 2003年月報酬率最高月份報酬率和適應值收斂圖	20
圖12 2003年月報酬率最低月份報酬率和適應值收斂圖	21
圖13 2004年月報酬率最高月份報酬率和適應值收斂圖	21
圖14 2004年月報酬率最低月份報酬率和適應值收斂圖	22
圖15 2005年月報酬率最高月份報酬率和適應值收斂圖	22
圖16 2005年月報酬率最低月份報酬率和適應值收斂圖	23
圖17 2006年月報酬率最高月份報酬率和適應值收斂圖	23
圖18 2006年月報酬率最低月份報酬率和適應值收斂圖	24
圖19 2007年月報酬率最高月份報酬率和適應值收斂圖	24
圖20 2007年月報酬率最低月份報酬率和適應值收斂圖	25
圖21 2008年月報酬率最高月份報酬率和適應值收斂圖	25
圖22 2008年月報酬率最低月份報酬率和適應值收斂圖	26
圖23 2009年月報酬率最高月份報酬率和適應值收斂圖	26
圖24 2009年月報酬率最低月份報酬率和適應值收斂圖	27
圖25 2010年月報酬率最高月份報酬率和適應值收斂圖	27
圖26 2010年月報酬率最低月份報酬率和適應值收斂圖	28
圖27 2011年月報酬率最高月份報酬率和適應值收斂圖	28
圖28 2011年月報酬率最低月份報酬率和適應值收斂圖	29
圖29 2003年月報酬率最高月份報酬率和適應值收斂圖	30
圖30 2003年月報酬率最低月份報酬率和適應值收斂圖	30
圖31 2004年月報酬率最高月份報酬率和適應值收斂圖	31
圖32 2004年月報酬率最低月份報酬率和適應值收斂圖	31
圖33 2005年月報酬率最高月份報酬率和適應值收斂圖	32
圖34 2005年月報酬率最低月份報酬率和適應值收斂圖	32
圖35 2006年月報酬率最高月份報酬率和適應值收斂圖	33
圖36 2006年月報酬率最低月份報酬率和適應值收斂圖	33
圖37 2007年月報酬率最高月份報酬率和適應值收斂圖	34
圖38 2007年月報酬率最低月份報酬率和適應值收斂圖	34
圖39 2008年月報酬率最高月份報酬率和適應值收斂圖	35
圖40 2008年月報酬率最低月份報酬率和適應值收斂圖	35
圖41 2009年月報酬率最高月份報酬率和適應值收斂圖	36
圖42 2009年月報酬率最低月份報酬率和適應值收斂圖	36
圖43 2010年月報酬率最高月份報酬率和適應值收斂圖	37
圖44 2010年月報酬率最低月份報酬率和適應值收斂圖	37
圖45 2011年月報酬率最高月份報酬率和適應值收斂圖	38
圖46 2011年月報酬率最低月份報酬率和適應值收斂圖	38
圖47 2003年實驗結果比較圖	39
圖48 2004年實驗結果比較圖	41
圖49 2005年實驗結果比較圖	42
圖50 2006年實驗結果比較圖	44
圖51 2007年實驗結果比較圖	45
圖52 2008年實驗結果比較圖	47
圖53 2009年實驗結果比較圖	48
圖54 2010年實驗結果比較圖	50
圖55 2011年實驗結果比較圖	51
圖56 半年的報酬率變化圖	53

表目錄
表1 累積報酬率移轉次數矩陣	10
表2 累積報酬率移轉機率矩陣	10
表3 隔日報酬率移轉次數矩陣	11
表4 隔日報酬率移轉機率矩陣	12
表5 台灣銀行公告定存利率表	15
表6 資金配置調整表	15
表7 SGA染色體編碼	18
表8 2003年1月~12月實驗比較表	39
表9 2003年執行時間與收斂代數統計表	39
表10 2004年1月~12月實驗比較表	40
表11 2004年執行時間與收斂代數統計表	41
表12 2005年1月~12月實驗比較表	42
表13 2005年執行時間與收斂代數統計表	42
表14 2006年1月~12月實驗比較表	43
表15 2006年執行時間與收斂代數統計表	44
表16 2007年1月~12月實驗比較表	45
表17 2007年執行時間與收斂代數統計表	46
表18 2008年1月~12月實驗比較表	46
表19 2008年執行時間與收斂代數統計表	47
表20 2009年1月~12月實驗比較表	48
表21 2009年執行時間與收斂代數統計表	48
表22 2010年1月~12月實驗比較表	49
表23 2010年執行時間與收斂代數統計表	50
表24 2011年1月~12月實驗比較表	51
表25 2011年執行時間與收斂代數統計表	51
表26 半年平均報酬率統計表	52
表27 半年平均收斂代數與平均執行時間統計表	54

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