利用動態調整預期投報率改善投資組合效益之研究

利用傳統MV模型所提供之權重於out-of-sample時，其績效結果不盡理想，歸咎於MV模型對於使用者所提供之參數，如以歷史資料計算之平均報酬率、變異數與共變異數矩陣實為敏感，且傳統參數無法真實反應未來之狀況。本研究採用動態模式與高估型期望報酬率來替代傳統的平均報酬率之參數；並以高估型期望報酬率為基建立出共變異數矩陣，本研究希望能提供適用於out-of-sample之參數來解決傳統MV模型所面臨的問題，採用動態模式與高估型期望報酬率確實提升了報酬率且得到更好的績效。

關鍵字

投資組合；指數型基金；期望報酬率；共變異數矩陣； MV模型；基因演算法

並列摘要

In the literature, the mean-variance model has been shown to perform poorly on out-sample data. The portfolio derived from the mean-variance model is very sensitive to its parameters, i.e., the expected return of each asset and the covariance matrix between assets’ returns. However, these parameters calculated from historical data often fail to reflect the dynamics of assets’ future returns. To overcome this problem, this thesis proposes several alternatives to estimate these parameters. Firstly, a dynamic model and an optimistic expected return are proposed to replace the traditional way of using mean for calculating the expected return. Then, the calculation of the covariance matrix is based on the optimistic expected return. Our experimental results show that using the dynamic model and the optimistic expected return can derive portfolio with better performance on out-sample data.

並列關鍵字

Portfolios ； ETFs ； Expected Return ； Covariance Matrix ； MV Model ； Genetic Algorithm

參考文獻

35. 范沛綱, 最佳投資組合研究-以臺灣50 指數為例. 碩士學位,統計研究所,國立中央大學, 2006.

38. 劉佳倩, 人工蜂群演算法於投資組合最佳化問題之應用. 碩士學位,元智大學,工業工程與管理學系, 2010.

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利用動態調整預期投報率改善投資組合效益之研究

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