本研究的目的，在於利用時間序列的非線性模型掌握市場情緒與因子報酬的非線性關係。運用分量回歸(Quantile Regression)的技術幫助我們獲得更多有用的資訊，並藉此設計調整策略，反映於投資組合中股票的篩選。在量化的投資操作上，我們以多因子模型(Multi-Factor Model)為基礎，挑選投資組合中的股票，如果市場情緒指標(外生變數)對於預測下一期的特色因子報酬有顯著效果的話，則可藉此現象來調整投資組合的股票風格以增加投資組合報酬。

對於何謂市場情緒或投資人的情緒，至今在財務領域上仍無明確的定義與共識。我們以文獻探討的方式蒐集各種可代表市場情緒的指標，如股票市場的價量資料、賣權買權比、VIX指數等。分別測試這些情緒代理變數，發現股票市場週轉率、市場融資融券比及消費者信心成長率皆有顯著的結果。這代表某些情緒指標對於未來某些風格的股票表現，確實會有所影響。使得因子擇時(Factor Timing)的技術能顯著地提升投資組合的績效與資訊比率(Information Ratio)

The objective of this study is to discover the non-linear effect of market sentiment to characteristic factor returns. We run ‘Quantile Regression’ to help us extract useful information and design an effective strategy. Based on the quantitative investment method, using the platform of Multi-Factor Model (MFM), we attempt to construct a portfolio and enhance portfolio performance. If the market-sentiment variable increases performance, we could conclude that some characteristic factors in a high sentiment period will perform better or worse in the next period.

What is the market or investor sentiment? It is still a problem in the finance field. There is no co-definition or consensus so far. We do our best to collect the indirect data, such as transaction data, price and volume data, and some indicators in other studies, VIX, put/call ratio and so on. Then, we test the proxy variables independently, and obtain some interesting results. The market turnover, the ratio of margin lending on funds/ margin lending on securities, and the growth rate of consumer confidence index have significant effects on some of the characteristic factors. This holds that some market sentiment variables could influence stocks with certain characteristics, and the factor timing approach could improve portfolio performance under examination by information ratio.

Content
Chapter 1 Introduction 8
1. Introduction and Background 8
2. Motivations 9
3. Contributions 10
Chapter 2 Literature Review 11
1. Market Sentiment and Stock Return 11
2. Fundamental Multi-Factor Model 13
3. The Mathematics of Fundamental Multi-Factor Model 14
(1) Equation 15
(2) Matrix Notation 15
(3) Assumptions 16
4. Quantile Regression and Application 16
(1) Conditional Quantile Function 17
(2) The Advantages of QR Model 18
Chapter 3 Methodology and Data 19
1. The Analytical Framework of Fundamental Multi-Factor Model 19
(1) Develop the Compound Descriptors 19
(2) Develop the Significant Composite Factors 20
(3) Estimate the Factor Returns 22
2. The Analytical Framework of Quantile Regression 24
(1) The Procedures of QR Application 24
(2) Methodology 26
3. Data 30
4. Sample 30
(1) Definition of Sentiment Variables 30
(2) Basic Statistics 32
(3) Sample Period 34
(4) Unit Root Test 35
Chapter 4 Empirical Study 36
1. Empirical Results 36
(1) Output the Strategy Table 36
(2) Back-testing in In-sample and Out-sample Period 40
(3) Multi-adjusted Strategy 43
Chapter 5 Conclusion and Suggestions 45
1. Conclusion 45
2. Suggestions 46
References 48
Appendix A Outcomes of In-sample Period 50
Appendix B Outcomes of Out-sample Period 55

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