洪卉，2015年获得爱尔兰利默瑞克大学金融专业博士学位。曾为江西师范大学特聘教授，现为南昌大学校聘教授。研究领域涵盖金融经济、宏观经济及行为理论。主持国家社会科学基金项目研究，并以第一作者身份在国际权威期刊如《北美经济与金融评论》《国际经济与金融评论》《应用经济快报》《经济与金融季刊》等发表相关论文。

Chapter 1 Introduction
1.0 Introduction
1.1 Research Motivation
1.2 Objectives of the Research Study
1.3 Structure of the Book
1.4 Conclusion
Chapter 2 The Chinese Equity Market: Historical Development and Characteristics
2.0 Introduction
2.1 Historical Development
2.2 General Data on the Market
2.3 Summary and Conclusion
Chapter 3 Academic Literature on the Chinese Equity Market
3.0 Introduction
3.1 Equity Pricing Factors
3.2 Dynamic Linkages among Equity Markets
3.3 Efficient Market Hypothesis
3.4 Summary
Chapter 4 Equity Market Returns and Predictor Variables: Theoretical Explanations and Empirical Evidence
4.0 Introduction
4.1 Standard Dividend Discount Model
4.2 Relationships between Predictor Variables and Equity Returns
4.3 Model Instability
4.4 Summary
Chapter 5 Data and Methodology
5.0 Introduction
5.1 Data Description
5.2 Methodological Background on the Empirical Research Presented in Chapter 6
5.3 Methodological Background on the Empirical Research Presented in Chapter 7
5.4 Summary
Chapter 6 Equity Market Downturn Predictability and Model Instability
6.0 Introduction
6.1 Descriptive Statistics
6.2 Correlation Analysis: Excess Market Returns and Predictor Variables
6.3 Further Information on Correlation Analysis
6.4 Equity Market Downturn Predictability
6.5 Robustness Tests
6.6 Model Instability
6.7 Summary and Discussion
Chapter 7 Equity Market Return Predictability and Model Instability
7.0 Introduction
7.1 Descriptive Statistics
7.2 Prediction Models
7.3 Model Instability
7.4 Summary and Discussion
Chapter 8 Summary, Discussion and Conclusion
8.0 Introduction
8.1 Synopsis of Research Findings
8.2 Implications of Research Findings
8.3 Areas for Further Research
8.4 Concluding Remarks
Appendices
References

本书主要关注模型不稳定情况下中国股票市场表现的可预测性。书中考察了模型不稳定情况下中国股市（沪深两市）下行行为和股市收益率可预测性的统计和经济意义。研究发现具有统计上显著效应的预测变量在构建市场择机策略时并非能够产生超额回报，研究认为模型不稳定性是投资风险的重要来源，会显著影响收益的可预测性，从而影响投资者的长期财富。因此在进行资产配置时必须考虑模型存在不稳定的可能。