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一個使用遺傳演算法改良之投資組合保險模型之研究 = A Study of...

國立高雄大學資訊工程學系碩士班

一個使用遺傳演算法改良之投資組合保險模型之研究 = A Study of Improved Portfolio Insurance Models Using Genetic Algorithms

Record Type:	Language materials, printed : monographic
Paralel Title:	A Study of Improved Portfolio Insurance Models Using Genetic Algorithms
Author:	蔡岳霖,
Secondary Intellectual Responsibility:	國立高雄大學
Place of Publication:	[高雄市]
Published:	撰者;
Year of Publication:	2013[民102]
Description:	70面圖，表 : 30公分;
Subject:	固定比例投資組合保險策略
Subject:	Constant proportional portfolio insurance
Online resource:	http://handle.ncl.edu.tw/11296/ndltd/86577016235048792294
Notes:	參考書目：面57-61
Notes:	103年12月16日公開
Summary:	在投資研究的領域中，一個著名的可同時降低投資風險並取得超額報酬的方法為固定比例投資組合保險策略(constant proportional portfolio insurance, CPPI)，在傳統論文大都採用報酬率或是夏普比率(Sharpe ratio)檢驗相關方法的績效。在本篇論文中，我們提出一個應用遺傳演算法(Genetic Algorithms, GA)改良既有之CPPI使其較既有方式更具有降低風險與提升績效之投資策略模型，其成份包含：(1)運用多種技術指標協助模型判斷適當的進出場時機；(2)利用CPPI作為資金配置方式；(3)採用多種績效指標用來進行回測或驗證模型的優劣，包含複利報酬率、最大下跌幅度(maximum drawdown)、左尾偏動差(lower partial moment)、夏普比率、索丁諾比率(Sortino ratio)、克瑪比率(Calmar ratio)、歐米茄比率(Omega ratio)以及資訊比率(information ratio)。實驗結果顯示，利用克瑪比率經遺傳演算法所建立的投資策略，在增加獲利與迴避風險狀況皆優於傳統之方式。我們期望本研究方法能夠推進目前遺傳演算法在投資領域上的應用研究。 Constant proportional portfolio insurance (CPPI) is a well-known money management strategy in the research area of investment. Past effort along this line of research mainly employed various return ratios or the risk-adjusted Sharpe ratio to examine the performance of relevant models developed for CPPI. In this thesis an improved CPPI-based model is proposed. The main ingredients in the proposed model consist of three major components: (1) technical indicators are used to determine market timing; (2) the CPPI strategy is used for money management; and (3) The class of Genetic Algorithms is used for model selection and parameter optimization. In this study, multiple ratios are employed as distinct fitness functions to evolve the solutions and a comparative study of these methods is presented. I will show that the proposed method using Calmar ratio typically outperform the models using other ratios. It is thus expected that this proposed methodology can advance the current state of machine learning for computational finance and investment.

一個使用遺傳演算法改良之投資組合保險模型之研究 = A Study of Improved Portfolio Insurance Models Using Genetic Algorithms
蔡, 岳霖

一個使用遺傳演算法改良之投資組合保險模型之研究 = A Study of Improved Portfolio Insurance Models Using Genetic Algorithms / 蔡岳霖撰 - [高雄市] : 撰者, 2013[民102]. - 70面 ; 圖，表 ; 30公分.
參考書目：面57-61103年12月16日公開.
固定比例投資組合保險策略Constant proportional portfolio insurance

一個使用遺傳演算法改良之投資組合保險模型之研究 = A Study of Improved Portfolio Insurance Models Using Genetic Algorithms
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330 $a 在投資研究的領域中，一個著名的可同時降低投資風險並取得超額報酬的方法為固定比例投資組合保險策略(constant proportional portfolio insurance, CPPI)，在傳統論文大都採用報酬率或是夏普比率(Sharpe ratio)檢驗相關方法的績效。在本篇論文中，我們提出一個應用遺傳演算法(Genetic Algorithms, GA)改良既有之CPPI使其較既有方式更具有降低風險與提升績效之投資策略模型，其成份包含：(1)運用多種技術指標協助模型判斷適當的進出場時機；(2)利用CPPI作為資金配置方式；(3)採用多種績效指標用來進行回測或驗證模型的優劣，包含複利報酬率、最大下跌幅度(maximum drawdown)、左尾偏動差(lower partial moment)、夏普比率、索丁諾比率(Sortino ratio)、克瑪比率(Calmar ratio)、歐米茄比率(Omega ratio)以及資訊比率(information ratio)。實驗結果顯示，利用克瑪比率經遺傳演算法所建立的投資策略，在增加獲利與迴避風險狀況皆優於傳統之方式。我們期望本研究方法能夠推進目前遺傳演算法在投資領域上的應用研究。 Constant proportional portfolio insurance (CPPI) is a well-known money management strategy in the research area of investment. Past effort along this line of research mainly employed various return ratios or the risk-adjusted Sharpe ratio to examine the performance of relevant models developed for CPPI. In this thesis an improved CPPI-based model is proposed. The main ingredients in the proposed model consist of three major components: (1) technical indicators are used to determine market timing; (2) the CPPI strategy is used for money management; and (3) The class of Genetic Algorithms is used for model selection and parameter optimization. In this study, multiple ratios are employed as distinct fitness functions to evolve the solutions and a comparative study of these methods is presented. I will show that the proposed method using Calmar ratio typically outperform the models using other ratios. It is thus expected that this proposed methodology can advance the current state of machine learning for computational finance and investment.
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