本研究欲建立投資標的選擇方法與投資組合最適化配置兩大領域間的橋樑，將兩大領域方法銜接並完整實作，並分析各個最適化投資組合配置模型的績效。本文使用資料包絡分析法 (data envelopment analysis, DEA) 針對2020 年臺灣證券交易所上市公司進行經營效率評估，透過篩選各產業內經營效率最高之公司作為投資標的，並應用於均值-變異數模型、均數-平均絕對離差模型、均數-條件風險模型及風險平價模型，最後分析不同的優化投資組合模型之投資績效，並使用資料包絡分析法評估各投資組合模型之相對效率。研究結果顯示，均數-平均絕對離差模型、均數-條件風險模型及風險平價模型，不論是樣本內亦或者是樣本外，雖然在報酬表現上略比等權重模型遜色，但在年化波動率及最大回落皆小於等權重模型，顯示出模型極小化風險的目標特性。然而，各個最適化模型一開始所設定的目標式皆不盡相同，不適合單一指標作為投資組合績效衡量的標準，因此本文使用能客觀考量多項產出及投入項的 DEA 方法評估投資組合模型績效表現。研究結果發現，透過 DEA 方法篩選出投資標的所建構的風險平價模型，在樣本內及樣本外的效率相較其他模型表現都最好。

The goal of this paper is to fill the gap between the investment target selection method and portfolio allocation, implement the combined models, and analyze the performance of each portfolio allocation method. We use Data Envelopment Analysis (DEA) to evaluate the operating efficiency of listed companies on the Taiwan Stock Exchange in 2020. We select companies with the highest operating efficiency as investment targets and optimize them by applying various portfolio allocation methods, including Mean-variance (MV) model, Mean Absolute Deviation (MAD) model, Conditional Value-at-Risk (CVaR) model, and Risk Parity model. Finally, we analyze the performance and use DEA to assess the relative efficiency of different portfolio optimization methods.
We conduct extensive experiments. Although the empirical results demonstrate that MAD, CVaR, and Risk Parity models are slightly worse than the equal-weight model in terms of the return in both in-sample and out-of-sample conditions, the annualized volatility and maximum drawdown are smaller than the baseline, showing the property of minimizing risk in the objective of the models. Nevertheless, it is not suitable to use a single indicator to assess the performance of those models as they have such diverse objective functions. Therefore, we propose using DEA to evaluate the performance of portfolio allocation methods, which considers multiple inputs and outputs objectively. The results show that combing the DEA and Risk Parity models yields superior performance in both in-sample and out-of-sampling efficiencies.

中文參考文獻
1. 高強、黃旭男與 Toshiyuki Sueyoshi (2022)，《管理績效評估：資料包絡分析法》，(二版) 華泰文化出版社。
2. 廖慶榮 (2009)，《作業研究》，(二版)，華泰文化出版社。

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