臺灣博碩士論文加值系統

本研究主要目的是透過將落後期的要素與原本單期的要素值結合產生新的要素值，希望能改善過去只用單期要素值和下一期報酬的關係建構量化多因子選股模型的投資組合報酬。首先，本文採用一個時間序列模型─多項式分配落後期模型去偵測每間公司的某要素對下一期報酬的最適落後項。之後，再採用公司累計比率計算每個要素的合適落後期，並透過指數平滑化的方式按照此期數合成新要素。不採用以往文獻所使用的主觀方法，本模型將改用觀察統計量方式來篩選有效要素。
研究結果指出原要素值應該被新合成要素值取代，特別是歸為價值型因子的要素。與用原要素值建構的投資組合比較，用新的周、月要素值建構的投資組合其資訊比率將從0.203提升至0.612。雖然新要素建構的投資組合其追蹤誤差些微上升0.28%，但其仍符合增值指數基金的要求，亦即追蹤誤差必須低於3%。

The main objective of this study is to generate values by combining the current and prior values of descriptors to improve the performance of a portfolio constructed based on the standard alpha model of Hsu et al. (2011). The Polynomial Distributed Lag Model, a time-series model, is adopted to detect the optimal lag length of each company in our research. After measuring an “adequate” lag length for each descriptor, we use the approach of exponential smoothing to combine the current and multi-lag-period descriptors. Instead of using the subjective method applied inHsu et al. (2011), our study calculates some statistics to filter the valid descriptors.
The empirical results suggest that the new values of the monthly and weekly frequency descriptors should substitute the original ones, especially those within the Value factor. When compared with a portfolio constructed using the raw descriptor values, the IR of the portfolio with the new values of monthly and weekly descriptors is increased from 0.203 to 0.612. Although its tracking error rises slightly by 0.28%, this portfolio still achieves the requirement of an enhanced index fund, which is below 3%.

摘要 II
ABSTRACT III
I. INTODUCTION 1
1.1 Background 1
1.2 Motivation for Research 3
II. LITERATURE REVIEW 6
2.1 Modern portfolio theory 6
2.2 Multi-Factor model 7
2.3 Information Analysis 10
2.4 Optimal Alpha Model 11
III. METHODOLOGY 14
3.1 Analytical framework 14
3.2 Data pre-processing 18
3.3 Extracting the multi-period information of descriptors 21
3.4 Composition of the multi-lag-period information by exponential smoothing 26
3.5 Testing of the descriptor validity 27
3.6 Computing alpha score via two-stage model 33
3.7 Blending the weekly descriptors and the monthly descriptors 36
3.8 Exponential smoothing of alpha scores 37
3.9 Constructing the enhanced index portfolio to diagnose the validity of alpha scores 38
IV.EMPIRICAL RESULTS 41
4.1 Data and Databases 41
4.2 Raw Sample 42
4.3 Results 46
V. CONCLUSION 72
REFERENCES 76

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