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研究生:林茂楠
研究生(外文):Mao-Nan Lin
論文名稱:應用高效率飛鳥演算法於多期動態資產配置最佳化的研究
論文名稱(外文):The Study of Applying High Efficient PSO to Multi-term Dynamic Asset Allocation
指導教授:游子宜游子宜引用關係
指導教授(外文):Yzu-Yi Yu
學位類別:碩士
校院名稱:國立暨南國際大學
系所名稱:資訊管理學系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:78
中文關鍵詞:粒子群最佳化演算法平行計算訊息傳輸介面
外文關鍵詞:Particle Swarm OptimizationParallel computingMessage Passing Interface
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在逐漸步入M型社會的今天,如果枯守著有限的薪水,想著微薄的儲蓄利息,很容易就會掉入M型社會中貧窮的那端而一蹶不振,因此如何進行投資來擴大有限的資產是刻不容緩的事。然而投資的本質中隱含著投機,市場中的風險無處不在,金融市場的大幅波動往往無法預警。大部份的投資人並非專業的投資專家,不易事先察覺金融市場的危機,往往閃避不及而造成損失。要如何趨避風險?最好的方式就是進行資產配置,將資產依最佳比例投入數個投資標的,分散風險、賺取利潤。為了掌握金融市場變化的脈動,本研究採多期資產配置,並將之與傳統策略進行比較,期望獲得最大投資報酬。有別於傳統人工窮舉法的多期資產配置,本研究採用粒子群演算法(Particle Swarm Optimization, PSO)進行多期資產配置比例的求解,並且結合平行計算的能力,提昇求解效率。根據研究進行的成果,多期資產配置策略優於傳統投資策略,傳統投資策略有買入持有(BH)、固定比例策略(CM)、固定比例投資策略(CPPI)、時間不變性投資組合策略(TIPP)。
It is well known that when the structure of the society turning to the “M” type, it will be very difficult to make good living by the limited salary and low interest rate. Without proper investments, one might fall into the other end of the “M” type of the society and become poor all the life. Hence, it is very critical and important to put the money in the right track to expend the wealth to maximum profits. However, where there are profits, there are risks. Especially, the variation of the financial marketing might be dramatically in a day. For most people who try to earn profits are generally not financial experts and can not sense the financial crisis in advance. Many of the investors lose the capitals instead of earning the profits because the un-prediction of the marketing changes. Therefore, how to skip the risks and earning the maximum profits has been an important issue for most of the people. The best way to reduce the risk is to distribute the investments to different targets. Thus, this study adopts the multi-term dynamic assets allocation as the financial model and tries to find the best outcome to compare the results from the traditional approaches. Instead of using exhausted search which was widely used in many researches, this study uses the parallelized “Particle Swarm Optimization” algorithm to obtain best assets allocation for maximum outcome efficiently. The results from this study are superior to those from Buy and Hold (BH), Constant Mix (CM), Constant-Proportion Portfolio Insurance (CPPI) and Time Invariant Portfolio Protection (TIPP). This has demonstrated the success of this study.
目 錄
論文摘要 I
ABSTRACT II
目 錄 III
圖 目 錄 V
表 目 錄 VI
第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 4
1.3 全文結構 6
第二章 財務模型與投資策略 8
2.1 財務模型 9
2.1.1 現金模型: 9
2.1.2 股票模型: 9
2.1.3 債券模型: 10
2.1.4 不動產模型: 11
2.2 投資策略 12
2.2.1 買入持有策略(BH): 12
2.2.2 固定比例策略(CM): 12
2.2.3 固定比例投資組合策略(CPPI): 13
2.2.4 時間不變性投資組合策略(TIPP): 13
第三章 粒子群演算法 15
3.1 粒子群演算法介紹: 15
3.2 粒子群演算法實例: 20
第四章 平行計算(MPI) 24
4.1 圓周率(π): 24
4.2 平行電腦分類: 26
第五章 演算法驗證 33
5.1 求極大值問題驗證: 36
5.1.1 問題一: 36
5.1.2 問題二: 36
5.2 求極小值問題驗證: 37
5.2.1 Shifted Sphere Function: 39
5.2.2 Schwefel's problem 2.22: 40
5.2.3 Rotated Hyper-Ellipsoid Function: 41
5.2.4 Rosen Brock’s Function: 42
5.2.5 Step Function: 43
5.2.6 Quartic Function with noise: 44
5.2.7 Schewefel’s problem 2.26: 45
5.2.8 Rastrigin’s Function: 46
5.2.9 Ackley’s Function: 47
5.2.10 Shifted Rotated Griewank’s Function: 48
第六章 研究成果與貢獻 49
第七章 結論與建議 52
7.1 關於財務模型: 52
7.2 關於粒子群演算法: 53
參考文獻: 54
中文參考文獻: 54
英文參考文獻: 55
網路參考資料: 57
中文參考文獻:
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2.邱昭彰、田哲溢,粒子群最佳化在個人資產配置上之模型建構研究 -以財富管理為例
3.戴兢志、鄧永亟,利用PSO演算法探討高速銑削最佳化,中國機械工程學會,第二十一屆全國學術研討會,11,2004。
4.鄭守成,MPI平行計算程式設計,新竹:國家高速網路與計算中心,2004。
5.陳飛文,平行遺傳演算法於營建排程運用之探討,國立台灣科技大學,2001。
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