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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Title:	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
Authors:	侯佳利;Jia-Li Hou
Contributors:	資訊管理研究所
Keywords:	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;Genetic Programming;Portfolio;Artificial Intelligence;Capital Allocation;Investment Strategy;Linear Capital Allocation;Non-Linear Capital Allocation
Date:	2007-01-03
Issue Date:	2009-09-22 15:22:09 (UTC+8)
Publisher:	國立中央大學圖書館
Abstract:	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。 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.
Appears in Collections:	[Graduate Institute of Information Management] Electronic Thesis & Dissertation

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