具移動窗格功能投資組合成份配置之獲利風險評估平台;Design and Implementation of Profit Risk Assessment Platform for Investment Portfolio with Moving Window Function

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题名:	具移動窗格功能投資組合成份配置之獲利風險評估平台;Design and Implementation of Profit Risk Assessment Platform for Investment Portfolio with Moving Window Function
作者:	蔡安翔;Tsai, An-Hsiang
贡献者:	資訊管理學系
关键词:	投資組合;現代投資組合理論;分層風險平價;成份配置;移動窗格;重新平衡頻率;Investment Portfolio;Modern Portfolio Theory;Hierarchical Risk Parity;Ingredients Configuration;Moving Window;Rebalance Frequency
日期:	2020-06-29
上传时间:	2020-09-02 17:47:39 (UTC+8)
出版者:	國立中央大學
摘要:	金融科技在近幾年快速崛起，其中，在金融投資的環節，常會看到投資理財機器人的出現，理財機器人會根據投資者的偏好，推薦出相對應的投資組合。而在投資組合中，績效因子的選取與分配權重，是決定投資組合優異與穩健的關鍵項目。現代投資組合理論是常見的投資組合權重配置方法，但此方法忽略了商品有層次結構的概念，導致投資組合在樣本外驗證時非常不穩定；而普遍的程式交易研究多半是針對單一交易策略的參數最佳化，或是對投資組合作全樣本內最佳化驗證，因此要妥當的驗證投資組合的成份配置是非常複雜的一件事。針對此一問題，本研究將投資組合與移動窗格法做結合，致力於建置出投資組合的風險獲利評估工具，讓投資人可以透過此工具將投資的每日賺賠績效檔案匯入，設定績效排名方式與權重配置模型等參數，本研究工具將會透過移動窗格法，在樣本內窗格選取績效因子並配置權重大小組成投資組合，在樣本外窗格模擬投資並重新平衡成份及權重，自動化計算出不同窗格大小配置的績效表現，並透過熱點圖呈現窗格績效，找出最佳績效的窗格的大小配置以及績效穩健的高原參數。本研究設立六個驗證案例，欲透過本研究工具達成各驗證案例之目的，並推薦投資人各驗證案例的投資組合參數以及窗格大小設定。而經由驗證結果可得知，各驗證案例皆能透過本研究工具所得之績效熱點圖，觀察高原類型、最大高原面積等，推薦出最合適的窗格大小設定與穩健的投資組合高原參數。未來在本研究工具能擴增更多績效排名因子與權重配置模型，同時加入資金再投入等方法，將窗格驗證更擬合真實投資情形，讓此工具能更有效的協助投資人建置出最合適的投資組合。;In recent years, fintech has risen rapidly. In the financial investment, we will often see the Robo-Advisor. The Robo-Advisor will recommend an investment portfolio based on investors′ preferences. In the investment portfolio, the selection and allocation of investment components are the keys to determine the excellent and stable investment portfolio. Modern portfolio theory is a common method of portfolio weight allocation, but this method ignores the concept of a hierarchical structure of commodities, resulting in a portfolio that is very unstable when verified outside the sample. Most of the general program trading research is mostly for the parameters of a single trading strategy optimization, or optimization of the entire sample of the investment portfolio, so it is very complicated to properly verify the composition of the investment portfolio. To response this problem, this study combines the portfolio with the moving window method and is committed to building a portfolio risk assessment tool that allows investors to import the daily profit performance files of the investment through this tool. The investors can set performance ranking methods, weight allocation model and other parameters. The research tool will use the moving window method to select investment components and allocate weights inside the sample, and then simulate investment and rebalance the components and weights of investment portfolio outside the sample. Automatically calculate the performance of different window configurations, and present the window performance through a heat map to find out the window of the best performance and the plateau parameters of stable performance. In this study, we have set up six verification cases. We want to use this research tool to achieve the purpose of each verification case and recommend the investment portfolio parameters of individual verification cases. Through the verification results, we can know that each case can observe the plateau type and maximum plateau area through the performance heat map obtained by this research tool, and recommend the most suitable window size setting and stable portfolio plateau parameters. In the future, this research tool can add more performance ranking factors, weight allocation models, and use reinvestment method to fit the verification to the real investment situation, so that the tool can effectively assist investors to build the most suitable investment portfolio.
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