本研究主要目的是在不同景氣循環下找出有效的選股因子，進而建構出在景氣擴張和緊縮兩種多因子阿爾法模型。接著我們另外又以日曆效應為基準，建構出適用於上、下半年的模型。最後我們再將這兩類效果結合討論其混合效應，將時間分為擴張上半年、擴張下半年、緊縮上半年以及緊縮下半年四種不同時期。另外，本模型賦予核心和衛星要素不同權重，旨在探討在週頻調整投資組合下，哪一個要素較為重要。
研究結果顯示在擴張以及上半年時期，投資人可透過觀察價值因子能有效選股；在緊縮及下半年則是規模及盈餘品質因子。價格動能及交易活動因子則幾乎不管在哪一時期都很重要。而在週頻調整投資組合的狀況下，核心要素的最適權重為0.3，衛星要素為 0.7。最後，本研究三種模型的資訊比例皆高於Hsu et al. (2011) 之多因子報酬模型及Wang (2011) 之多因子報酬多空模型。以本研究混合效應模型為例，在追蹤誤差控制在3%以下時，其資訊比例為1.40，主動報酬為3.09%，追蹤誤差為2.20%，周轉率為207% 以及交易成本為1.2%。

This study aims to find an effective linear combination of factors in different economic cycle periods and then construct two factor timing multi-factor alpha models, one each for the expansion and contraction periods. Then, we wish to examine a further two effects, namely calendar effect and cross effect. The calendar periods are divided into the first half year and the second half year. The cross effect is the combination of the economic cycle and the calendar effect. In addition, this study puts different loadings in core and satellite descriptors, which means we wish to examine which descriptors are more important when we rebalance our portfolio weekly.
The empirical results show that the Value factor is effective in expansion and the first half year, and the Size and Earnings Quality factors are effective in contraction and the second half year. Moreover, the Price Momentum and Trading Activity factors are effective most of the time. We find that the optimal weight for core descriptors is 0.3 and for satellite descriptors is 0.7. Finally, the information ratios of our models are superior to the Standard alpha model by Hsu et al. (2011) and the Market Trend-based alpha model by Wang (2011). Taking the AMCross as an example, when the tracking error is below 3%, the IR is 1.40, the active return is 3.09%, the tracking error is 2.20%, the turnover rate is 207% and the transaction costs are 1.2%.

ABSTRACT iv
I. INTRODUCTION 1
1.1 Background information 1
1.2 Research purpose 4
II. LITERATURE REVIEW 7
2.1 Multi-factor model 7
2.2 Optimization multi-factor alpha model 9
2.3 Factor timing 12
III. METHODOLOGY 16
3.1 Analytical procedures 16
3.2 Research of situations 19
3.3 Research of descriptors 22
3.4 Testing the effectiveness of descriptor 24
3.5 The loadings on core and satellite descriptors 31
3.6 Computing optimal descriptor and factor weights 34
3.7 Combining the weekly and monthly alpha score 35
3.8 Smoothing approach 36
3.9 Portfolio construction 38
IV. EMPIRICAL RESULTS 41
4.1 Data 41
4.2 Situations and sample 41
4.3 Empirical results 44
V. CONCLUSION 62
REPERENCES 67

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