以遺傳程式規劃建構靜態及動態非線性投資策略

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侯佳利(Jia-Li Hou) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

以遺傳程式規劃建構靜態及動態非線性投資策略
(Constructing Static and Dynamic Investment Strategy Portfolios by Genetic Programming)

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摘要(中)

本研究係提出一投資組合問題之研究架構，將投資問題以資金分配頻率及分配方式兩個維度區分為四個象限，在資金分配方式上區分為線性及非線性方式，在資金分配頻率方面，則區分為靜態與動態配置。其中若將所有投資標的投資期間視為相同，統一批次於期初完成資金分配則屬靜態資金分配，若將各投資標的期間視為各異，於需要資金時使配置資金即屬動態資金配置。
傳統財務領域所探討之投資組合問題多係屬於線性靜態投資問題，係將所有的投資標的投資期間視為相同，於期初以買入持有方式進行投資，因此係將資金以線性方式靜態直接分配在多個投資標的物上，以求得最大化報酬或最小化風險[Huang, 2008; Li, 2008]。於期末再重新決定下一期之資金配置。
本研究並提出以『投資策略』為投資標的，本研究係將投資標的物與交易規則進行配對成為投資策略，再將資金分配在投資策略上，而非直接分配在投資標的上。並提出一非線性資金分配方式，透過柔性運算技術以遺傳程式規劃產生資金分配樹，決定每一投資策略所分配之資金比重，並分別提供靜態與動態資金配置頻率之解決方案。
本研究透過於美國股市以道瓊工業指標之三十個成份股配合教科書、學術研究及投資市場常用的九項技術指標所構成之八十一個簡單交易規則，成為二千四百三十個投資策略，透過遺傳程式規劃進行資金配置，並以1991至2006年之股票日交易資料進行實驗測試，實驗結果顯示在測試期中靜態、動態非線性投資組合策略相較於買入持有策略，不但可以獲得相當之投資報酬，而且可以有較低之投資風險。

摘要(英)

The study comes up with a framework of portfolio, dividing investment issues into four quadrants based on two dimensions: capital allocation frequency and allocation approach. In allocation approach, there are linear and non-linear. In capital allocation frequency selection approach, there are static and dynamic allocation approaches. In the framework, static allocation, based on the assumption that if investment duration is identical, is to complete capital allocation selection at the beginning of duration; dynamic allocation, based on the assumption that each investment period is different, is to allocate capital when needed.
In traditional financial area, investment portfolios are linear and static investment issue, which is take all investment duration are the same, and to buy in at the beginning of period, therefore, invest decision is to directly allocate capital on multiple investment objectives by static allocation, in order to gain the greatest profit or minimize the risk probability.[Huang, 2008; Li, 2008] And reconsidering investment decision for next duration at the end of duration.
The framework of the research takes “investment strategy” as investment objectives. The research is to make pairs of investment objectives and transaction rules, and allocate capital on investment strategies rather on investment objectives directly. And the research comes up a solution of non-linear capital allocation approach, including planning a capital allocation tree by soft computing and genetic algorithms, calculating every capital weight on every investment strategies, and providing static and dynamic capital frequency strategies.
The research takes 30 stocks in Dow Jones Industrial Average of U.S. stock market、textbook、academic researches and 9 technical indexes which are commonly used in investment markets to comprise 81 simple transaction rules and constitute 2,430 investment strategies which are planned by genetic algorithms. And experiment test of research is based on 1999 to 2006 stock market data, the outcome of experiment shows that static and dynamic and non-linear portfolios gains greater profit and smaller probability of risk, comparing to buy-in strategy.

關鍵字(中)

★ 非線性資金分配
★ 線性資金分配
★ 資金配置
★ 投資策略
★ 投資組合
★ 人工智慧
★ 遺傳程式規劃

關鍵字(英)

★ Genetic Programming
★ Portfolio
★ Artificial Intelligence
★ Capital Allocation
★ Investment Strategy
★ Linear Capital Allocation
★ Non-Linear Capital Allocation

論文目次

第一章、緒論............1
第一節、研究背景............1
第二節、研究動機............2
第三節、研究目的............2
第四節、預期研究貢獻............2
一、提出一投資組合策略架構............3
二、提出投資策略的概念............3
三、提出SGPIS與DGPIS............3
四、提出動態資金配置方式............3
第五節、論文章節說明............3
第二章、文獻探討............5
第一節、財務領域之投資理論............5
一、投資組合理論............5
二、效率前沿 (Efficient frontier)............6
三、雙基金定理與單基金定理............9
四、 Sharpe指標............10
第二節、交易規則之研究............12
一、效率市場假說............12
二、技術指標............15
第三節、以未來股票價值之選股分析............19
一、財務指標分析............19
二、企業價值分析............20
三、股價評價模型............20
第四節、財務方法在投資組合之研究............21
一、在資產配置之最適策略之應用............21
二、在投資組合資產配置策略之研究............22
三、在投資組合風險評價方面的研究............22
四、 Sharpe 指標之VaR 形式應用............23
第五節、人工智慧在投資研究的分類整理............24
一、交易策略(Trading Strategy)：............24
二、選股策略(Selection Strategy)：............26
三、資金策略(Capital Strategy)：............27
第三章、遺傳程式規劃............28
第一節、遺傳演算法............28
一、遺傳演算法的運算程序............29
二、交配的方式............31
三、控制參數(Control parameter)............32
第二節、遺傳程式規劃............34
第三節、遺傳程式規劃的演化運算............36
第四章、研究架構............41
第一節、研究流程............41
第二節、投資策略............42
第三節、投資組合問題研究架構............46
一、投資組合問題四象限............46
二、線性靜態投資組合問題............46
三、線性動態投資組合問題............47
四、非線性靜態投資組合問題............47
五、非線性動態投資組合問題............48
第五章、實驗測試............49
第一節、實驗工具與資料來源............49
一、系統軟硬體設備............49
二、資料來源............49
第二節、實驗參數之決定............51
一、實驗投資標的之選擇............51
二、資料前處理............51
三、實驗環境參數............60
第三節、實驗一技術指標與時間的關係............61
一、實驗說明............61
二、實驗數據說明............61
三、實驗結論............72
第四節、實驗二 SGPIS靜態遺傳程式規劃投資策略............73
一、實驗說明............73
二、實驗數據說明............77
三、實驗結論............82
第五節、實驗三同時考慮報酬與風險............83
一、實驗說明............83
二、實驗數據說明............87
三、實驗結論............91
第六節、實驗結果綜合分析............93
第六章、結論與討論............94
第一節、研究發現............94
一、投資策略在歷史資料的回測............94
二、多頭市場買入持有策略的有效效............94
第二節、研究貢獻............94
一、提出一投資組合策略架構............94
二、提出投資策略的概念............95
三、提出SGPIS與DGPIS............95
四、提出動態資金配置方式............95
第三節、研究限制............96
一、實驗範圍的限制............96
二、研究時間的限制............96
三、資金分配的限制............96
第四節、未來研究方向............97
一、加入多期的訓練方式............97
二、考慮複雜的交易規則............97
三、加入停損、停利規則，進一步降低風險............97
四、考慮同時進行多、空操作............97
五、考慮於動態配置資金時加入Fuzzy區間控制............97
參考文獻............98

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指導教授

陳稼興、陳彥良
(Jiah-Shing Chen、Yen-Liang Chen)

審核日期

2008-1-8

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