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高国宏
魏下海
金融學院
金融學博士學位課程(中文學制)
博士
2020
低風險高收益悖論在中國 A 股 市場的實 證研究 : 兼論低風險量化投資策略
An Empirical Study on the Paradox of Low Risk and High Return in China's A-Share Market- also on Low-Risk Quantitative Investment Strategy
悖論 ; CAPM ; APT ; FF ; 因子方向 ; 金融異象
Paradox ; CAPM ; APT ; FF ; factor direction
現代金融學的基礎概念就是風險和收益，其一切研究的核心是資產定價，而定價的基礎是衡量風險的高低。高風險高回報,低風險低回報,成為一切定價理論之公理。荷蘭佛利特等人的“低風險高回報悖論”用資料結論對這一理論基準發出了質疑。中國市場目前還不是一個全面開放的市場，有著許多別的市場所不具備的個性化。儘管已經有人驗證悖論在中國存在，但是這裡的研究採用了AI單因子測試技術路線，發現了更多的細緻的中國現象。
比如，九月現象、行業現象、長週期中間組表現明顯勝於其他組現象，有成本回測後低貝塔策略很難超越基準，甚至賠錢，和佛利特保守多因子公式成分不同，我們採用其他因子和行業貝塔合作也可以獲得較好收益。這些都是我們的新發現。
本文將中國A股市場作為研究目標，利用Python量化交易研究平臺作為研究工具，展開大資料驗證，完成了三個目標：（1）對現代金融理論作出了中國式的驗證，這是一個“證真”的過程；（2）對“低風險高回報悖論”做出多角度系統地研究，這是一個“證偽”的過程。（3）在這個基礎上提出“因子方向”的概念來解析悖論，並進一步提出通用多因子研究框架，將過去的“風險-收益”理論，擴展到更具普適性的“因子-收益”說。
在達成目標時執行了如下幾個方面的研究行動：首先，對以傳統馬科維茨均值方差理論為起點的資本資產定價模型（CAPM）和風險定價模型（APT）等基本模型進行梳理驗證，對後期的Fama-French的修正模型執行梳理。其次，研究“低風險高回報悖論”的來龍去脈，分析其研究方法和方式是否適合中國A股市場。分析低風險高回報悖論對傳統定價模型的影響。第三，基於悖論做多因子模型的通用研究，歸納更加具有普適性研究框架，提出理論創新見解。
我們借助對悖論研究的特點，發現了因子具有方向性，通過構建通用的多因子模型來說明因子方向的取用規則使其一般化，並進一步提出風險因子也是普通因子，應和其他因子一樣參入因子貢獻評估，這樣，風險收益說就擴展到更具一般性的因子收益說。把技術分析、基本面分析、風險分析、行為分析統一到一個研究架構裡，並給出了基於向量機的機器學習的高級多因子策略來展示因子收益說對新技術的適用性。
所以本研究實乃從研究悖論現象發點，借助因子方向的新概念，提出了更具普適性的因子收益學說，完成了一個金融基本理論問題研究。至於因子收益說在不同市場和產品的表現，本文暫不涉及。本文為在股票市場實現低風險高回報量化投資策略和長期獲利策略提供了理論基礎和技術路線，至於外匯、商品期貨市場暫未涉及。
比如，九月現象、行業現象、長週期中間組表現明顯勝於其他組現象，有成本回測後低貝塔策略很難超越基準，甚至賠錢，和佛利特保守多因子公式成分不同，我們採用其他因子和行業貝塔合作也可以獲得較好收益。這些都是我們的新發現。
本文將中國A股市場作為研究目標，利用Python量化交易研究平臺作為研究工具，展開大資料驗證，完成了三個目標：（1）對現代金融理論作出了中國式的驗證，這是一個“證真”的過程；（2）對“低風險高回報悖論”做出多角度系統地研究，這是一個“證偽”的過程。（3）在這個基礎上提出“因子方向”的概念來解析悖論，並進一步提出通用多因子研究框架，將過去的“風險-收益”理論，擴展到更具普適性的“因子-收益”說。
在達成目標時執行了如下幾個方面的研究行動：首先，對以傳統馬科維茨均值方差理論為起點的資本資產定價模型（CAPM）和風險定價模型（APT）等基本模型進行梳理驗證，對後期的Fama-French的修正模型執行梳理。其次，研究“低風險高回報悖論”的來龍去脈，分析其研究方法和方式是否適合中國A股市場。分析低風險高回報悖論對傳統定價模型的影響。第三，基於悖論做多因子模型的通用研究，歸納更加具有普適性研究框架，提出理論創新見解。
我們借助對悖論研究的特點，發現了因子具有方向性，通過構建通用的多因子模型來說明因子方向的取用規則使其一般化，並進一步提出風險因子也是普通因子，應和其他因子一樣參入因子貢獻評估，這樣，風險收益說就擴展到更具一般性的因子收益說。把技術分析、基本面分析、風險分析、行為分析統一到一個研究架構裡，並給出了基於向量機的機器學習的高級多因子策略來展示因子收益說對新技術的適用性。
所以本研究實乃從研究悖論現象發點，借助因子方向的新概念，提出了更具普適性的因子收益學說，完成了一個金融基本理論問題研究。至於因子收益說在不同市場和產品的表現，本文暫不涉及。本文為在股票市場實現低風險高回報量化投資策略和長期獲利策略提供了理論基礎和技術路線，至於外匯、商品期貨市場暫未涉及。
High risk and high return, low risk and low return have become the axioms of all pricing theories. The "paradox of low risk and high return" of Pim van Vliet and others in Holland questions this theoretical benchmark with the data conclusion.
At present, China's market is not a completely open market. It has many characteristics that other markets do not have, such as a large number of retail investors, different sizes of institutions, and a large amount of nationalized assets of listed companies. Although some people have verified the existence of paradox in China, my research adopts AI technology route and finds more detailed Chinese phenomena.
For example, September phenomenon, industry phenomenon and long-term intermediate group are obviously better than other groups. Low beta strategy after cost retest is difficult to exceed the benchmark, even lose money. Unlike the components of the conservative multi factor formula of Vliet, we can get better benefits by using other factors and industry beta cooperation. These are all our new discoveries.
This paper takes China's A-share market as the research target, and uses Python quantitative trading research platform as the research tool to carry out big data verification, and achieves three goals: (1) it makes a Chinese style verification of modern financial theory, which is a process of "verification".(2) It is a process of falsification to study the paradox of low risk and high return from multiple perspectives.(3) On this basis, the concept of "factor direction" is proposed to analyze the paradox, and a general multi factor research framework is proposed to extend the past "risk return" theory to a more universal "factor return" theory.
In order to achieve the goal, the following research actions were carried out: firstly, the basic models such as capital asset pricing model (CAPM) and risk pricing model (APT) based on the traditional Markowitz mean variance theory were sorted out and verified, and the later modified Fama French model was sorted out. Secondly, it studies the origin and development of the paradox of low risk and high return, and analyzes whether its research methods and methods are suitable for China's A-share market. This paper analyzes the impact of the paradox of low risk and high return on the traditional pricing model. Third, based on the paradox to do the general research of multi factor model, summarize the more universal research framework, and put forward the theoretical innovation.
Therefore, this study has completed a basic financial theory research with the help of a paradox phenomenon. As for the performance of factor return theory in different markets and products, this paper will not deal with it. This paper provides a theoretical basis and technical route for the realization of low risk and high return quantitative investment strategy and long-term profit strategy in the stock market. Foreign exchange and commodity futures markets are not involved for the time being.
At present, China's market is not a completely open market. It has many characteristics that other markets do not have, such as a large number of retail investors, different sizes of institutions, and a large amount of nationalized assets of listed companies. Although some people have verified the existence of paradox in China, my research adopts AI technology route and finds more detailed Chinese phenomena.
For example, September phenomenon, industry phenomenon and long-term intermediate group are obviously better than other groups. Low beta strategy after cost retest is difficult to exceed the benchmark, even lose money. Unlike the components of the conservative multi factor formula of Vliet, we can get better benefits by using other factors and industry beta cooperation. These are all our new discoveries.
This paper takes China's A-share market as the research target, and uses Python quantitative trading research platform as the research tool to carry out big data verification, and achieves three goals: (1) it makes a Chinese style verification of modern financial theory, which is a process of "verification".(2) It is a process of falsification to study the paradox of low risk and high return from multiple perspectives.(3) On this basis, the concept of "factor direction" is proposed to analyze the paradox, and a general multi factor research framework is proposed to extend the past "risk return" theory to a more universal "factor return" theory.
In order to achieve the goal, the following research actions were carried out: firstly, the basic models such as capital asset pricing model (CAPM) and risk pricing model (APT) based on the traditional Markowitz mean variance theory were sorted out and verified, and the later modified Fama French model was sorted out. Secondly, it studies the origin and development of the paradox of low risk and high return, and analyzes whether its research methods and methods are suitable for China's A-share market. This paper analyzes the impact of the paradox of low risk and high return on the traditional pricing model. Third, based on the paradox to do the general research of multi factor model, summarize the more universal research framework, and put forward the theoretical innovation.
Therefore, this study has completed a basic financial theory research with the help of a paradox phenomenon. As for the performance of factor return theory in different markets and products, this paper will not deal with it. This paper provides a theoretical basis and technical route for the realization of low risk and high return quantitative investment strategy and long-term profit strategy in the stock market. Foreign exchange and commodity futures markets are not involved for the time being.
2021
中文
219
致 謝 IV
摘 要 VI
Abstract VIII
圖目錄 XIII
表目錄 XVI
第一章 緒 論 1
1.1 選題背景及意義 1
1.1.1研究背景 4
1.1.2研究意義 6
1.2 研究內容及方法 7
1.2.1研究內容 7
1.2.2研究方法 13
1.3 本文的創新之處 14
第二章 文獻評論及基礎理論 17
2.1 基礎理論的梳理 18
2.2 悖論簡介 24
2.3 文獻綜述及評價 27
2.3.1 投資組合部分 27
2.3.2 低風險部分 32
2.3.3 多因子部分 34
第三章 “悖論”研究方法及設計 49
3.1 悖論發現者——豪根研究 50
3.1.1豪根本人介紹 51
3.1.2豪根的主要觀點 52
3.2 悖論踐行者——弗利特等人的論文研究 58
3.2.1佛裡特論文研究 60
3.2.2佛裡特選股方法的分析 65
3.2.3 佛裡特-多因子投資策略 67
3.3悖論相關的其他論文研究 69
3.3.1著名的四人論文 69
3.3.2布利茨的論文 71
3.3.3小結 72
第四章 若干金融理論的A股驗證 73
4.1量化研究平臺介紹 73
4.2組合是否可以降低風險之驗證 75
4.3組合中的股票規模合理性研究 80
4.3.1 ARCH模型的由來 80
4.3.2. GARCH模型的由來 82
4.3.3股票規模的非線性研究 83
4.4組合中各種正態性測試 87
4.4.1歸一化視覺化約定 88
4.4.2股票收益正態性驗證 89
4.5投資組合理論的三個驗證 96
4.5.1組合可行集的視覺化 97
4.5.2有效前沿的視覺化 99
4.5.3資本市場線的視覺化 102
4.6 單因子（CAPM）驗證 104
4.7 Fama多因子驗證 108
4.8中國股市布朗運動的驗證 113
4.9投資組合業績的評價標準確定 117
4.10本章小結 120
第五章悖論的中式驗證及深層次分析 121
5.1 悖論在中國市場的驗證 122
5.1.1測試一 124
5.1.2測驗二 128
5.1.3測驗三 130
5.1.4測驗四 131
5.2 悖論深層次機理的理論探析 132
5.2.1悖論現象與波動率 132
5.2.2悖論現象與交易擁擠 135
5.2.3 另外一些發現 139
5.3 基於悖論的策略探析 141
5.3.1實驗一 143
5.3.2實驗二 145
5.3.3實驗三 145
5.3.4實驗四 146
5.3.5實驗五 147
5.3.6實驗六 147
5.3.7實驗七 148
5.4本章總結 149
第六章 緣起低貝塔的多因子框架研究 153
6.1 量化交易策略的流程 156
6.2 量化回測框架的機理 158
6.3 多因子策略的設計與實現 161
6.4 因子資料處理的基本流程 165
6.4.1去極值 165
6.4.2標準化 167
6.4.3市值中性化處理 167
6.5 單因子有效性分析 169
6.5.1單因子分析的基本框架 170
6.5.2簡單單因子的計量分析 171
6.5.3因子專業分析工具 173
6.5.4複合單因子的處理 179
6.6 多因子策略回測 181
6.6.1多因子打分法選股 181
6.6.2多因子回歸法選股 183
6.7 小結 184
6.8 補充：機器學習多因子策略 185
第七章 結論和建議 189
7.1因子收益說 190
7.1.1處理理論衝突 191
7.1.2金融史研究不再被割裂 192
7.1.3多因子模型對新技術介入的支撐作用 193
7.2關於多因子模型的進一步思考 194
7.2.1關於多因子的新表達形式 194
7.2.2多因子與其他金融理論的關係 196
7.3關於後續研究的建議 198
7.4本文總結 199
參考文獻 203
作者簡歷 217
附 錄 219
摘 要 VI
Abstract VIII
圖目錄 XIII
表目錄 XVI
第一章 緒 論 1
1.1 選題背景及意義 1
1.1.1研究背景 4
1.1.2研究意義 6
1.2 研究內容及方法 7
1.2.1研究內容 7
1.2.2研究方法 13
1.3 本文的創新之處 14
第二章 文獻評論及基礎理論 17
2.1 基礎理論的梳理 18
2.2 悖論簡介 24
2.3 文獻綜述及評價 27
2.3.1 投資組合部分 27
2.3.2 低風險部分 32
2.3.3 多因子部分 34
第三章 “悖論”研究方法及設計 49
3.1 悖論發現者——豪根研究 50
3.1.1豪根本人介紹 51
3.1.2豪根的主要觀點 52
3.2 悖論踐行者——弗利特等人的論文研究 58
3.2.1佛裡特論文研究 60
3.2.2佛裡特選股方法的分析 65
3.2.3 佛裡特-多因子投資策略 67
3.3悖論相關的其他論文研究 69
3.3.1著名的四人論文 69
3.3.2布利茨的論文 71
3.3.3小結 72
第四章 若干金融理論的A股驗證 73
4.1量化研究平臺介紹 73
4.2組合是否可以降低風險之驗證 75
4.3組合中的股票規模合理性研究 80
4.3.1 ARCH模型的由來 80
4.3.2. GARCH模型的由來 82
4.3.3股票規模的非線性研究 83
4.4組合中各種正態性測試 87
4.4.1歸一化視覺化約定 88
4.4.2股票收益正態性驗證 89
4.5投資組合理論的三個驗證 96
4.5.1組合可行集的視覺化 97
4.5.2有效前沿的視覺化 99
4.5.3資本市場線的視覺化 102
4.6 單因子（CAPM）驗證 104
4.7 Fama多因子驗證 108
4.8中國股市布朗運動的驗證 113
4.9投資組合業績的評價標準確定 117
4.10本章小結 120
第五章悖論的中式驗證及深層次分析 121
5.1 悖論在中國市場的驗證 122
5.1.1測試一 124
5.1.2測驗二 128
5.1.3測驗三 130
5.1.4測驗四 131
5.2 悖論深層次機理的理論探析 132
5.2.1悖論現象與波動率 132
5.2.2悖論現象與交易擁擠 135
5.2.3 另外一些發現 139
5.3 基於悖論的策略探析 141
5.3.1實驗一 143
5.3.2實驗二 145
5.3.3實驗三 145
5.3.4實驗四 146
5.3.5實驗五 147
5.3.6實驗六 147
5.3.7實驗七 148
5.4本章總結 149
第六章 緣起低貝塔的多因子框架研究 153
6.1 量化交易策略的流程 156
6.2 量化回測框架的機理 158
6.3 多因子策略的設計與實現 161
6.4 因子資料處理的基本流程 165
6.4.1去極值 165
6.4.2標準化 167
6.4.3市值中性化處理 167
6.5 單因子有效性分析 169
6.5.1單因子分析的基本框架 170
6.5.2簡單單因子的計量分析 171
6.5.3因子專業分析工具 173
6.5.4複合單因子的處理 179
6.6 多因子策略回測 181
6.6.1多因子打分法選股 181
6.6.2多因子回歸法選股 183
6.7 小結 184
6.8 補充：機器學習多因子策略 185
第七章 結論和建議 189
7.1因子收益說 190
7.1.1處理理論衝突 191
7.1.2金融史研究不再被割裂 192
7.1.3多因子模型對新技術介入的支撐作用 193
7.2關於多因子模型的進一步思考 194
7.2.1關於多因子的新表達形式 194
7.2.2多因子與其他金融理論的關係 196
7.3關於後續研究的建議 198
7.4本文總結 199
參考文獻 203
作者簡歷 217
附 錄 219
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