Title Page
Abstract
Contents
1. Environmental Taxation and Eco-conscious Technology Innovation 11
1.1. Introduction 11
1.2. Institutional Overview, Literature Review, and Mechanism 16
1.2.1. Institutional Overview 16
1.2.2. Literature Review and Mechanism 18
1.3. Theoretical Basis 28
1.3.1. Externality Theory 28
1.3.2. Pigou Tax Theory 30
1.3.3. Porter Hypothesis 31
1.4. Research Design 34
1.4.1. Data Sources and Indicator Processing 34
1.4.2. Model Design 38
1.4.3. Statistical Summarization 40
1.5. Analysis of Benchmark Results 40
1.6. Robustness Tests 42
1.6.1. Parallel Trend Testing and Dynamic Effects 42
1.6.2. Placebo Tests 44
1.6.3. Exclude Disruptive Policies 47
1.6.4. Change the Explained Variable 48
1.7. Heterogeneity Analyses 49
1.7.1. Property Heterogeneity 49
1.7.2. Size Heterogeneity 51
1.7.3. Industry Pollution Level Heterogeneity 53
1.8. Further Discussion 55
1.8.1. Eco-conscious Technology Innovations' Quality: Substantive or Symbolic Innovation? 56
1.8.2. Eco-conscious Technology Innovations' Organization: Independent or Collaborative Innovation? 58
1.9. Research Conclusions and Policy Implications 60
2. Multi-Channel Investor Sentiment and Stock Returns 63
2.1. Introduction 63
2.2. Theory and Literature Review 65
2.2.1. The Definition of Investor Sentiment 66
2.2.2. The Psychological Basis of Investor Sentiment 68
2.2.3. Measure of Investor Sentiment 73
2.2.4. Impact of Investor Sentiment on Stock Returns 77
2.3. Methodology and Hypotheses 81
2.3.1. Market-based Measurement of Investor Sentiment 82
2.3.2. News-based and Media-based Measurements of Investor Sentiment 96
2.3.3. Hypotheses of Investor Sentiment and Stock Returns 101
2.4. Results 105
2.4.1. Model Setting 105
2.4.2. Description of Main Variables and Data Selection 105
2.4.3. Descriptive Statistics 107
2.4.4. Benchmark regression 107
2.5. Robustness Tests 110
2.5.1. Investor Sentiment Lagged One Period 110
2.5.2. A U-shaped Test 112
2.5.3. Adding More Control Variables 114
2.6. Heterogeneity Analyses 116
2.6.1. Heterogeneity Analysis of Turnover Rate 116
2.6.2. Heterogeneity Analysis of Season 118
2.6.3. Heterogeneity Analysis of Industry 122
2.7. Discussion 124
2.8. Conclusion 125
References 127
Appendix 137
국문요약 142
Table 1.4.1. Definitions and descriptions of the key variables. 37
Table 1.4.2. The coefficients' meaning of DID. 39
Table 1.4.3. Statistical summarization of key variables. 40
Table 1.5.1. Benchmark regression results. 41
Table 1.6.1. Placebo test: change the policy timing. 45
Table 1.6.2. Robustness test: exclude disruptive policies. 47
Table 1.6.3. Robustness test: change the explained variable. 49
Table 1.7.1. Regression results by property ownership grouping. 50
Table 1.7.2. Regression results by entrepreneur's size grouping. 52
Table 1.7.3. Regression results by industry pollution degree grouping. 54
Table 1.8.1. Substantive vs. symbolic innovation. 57
Table 1.8.2. Independent vs. collaborative innovation. 59
Table 2.3.1. Correlation analysis of PCA. 92
Table 2.3.2. Explanatory power of each principal component under the PCA method. 93
Table 2.3.3. Coefficients of each principal component under the PCA method. 93
Table 2.3.4. Rotated factor loadings and unique variances. 93
Table 2.3.5. Descriptive statistics of market-based indicators of investor sentiment. 95
Table 2.3.6. Example of online financial news and stock forums and message boards statistics about sample stocks on trading days. 99
Table 2.3.7. Descriptive statistics of news-based and media-based investor sentiment. 100
Table 2.4.1. Variable definition and source. 106
Table 2.4.2. Descriptive statistics. 107
Table 2.4.3. Correlation analysis of variables. 109
Table 2.4.4. Benchmark regression results. 110
Table 2.5.1. Robustness test: Investor sentiment lagged one period. 111
Table 2.5.2. Robustness test: The quadratic investor sentiment. 113
Table 2.5.3. Robustness test: U-shaped test between investor sentiment and stock returns. 114
Table 2.5.4. Robustness test: Add more control variables. 115
Table 2.6.1. Heterogeneity test: Turnover rate. 116
Table 2.6.2. Heterogeneity test: Seasonal effect. 119
Table 2.6.3. Heterogeneity test: Industry. 123
Figure 1.6.1. Parallel trend testing. 43
Figure 1.6.2. The distribution results of placebo test estimation coefficients. 46
Figure 2.3.1. CSI300 performance and trend. 83
Figure 2.3.2. Investor sentiment in trading markets-000002SZ. 94
Figure 2.3.3. Investor sentiment in network news and social media-000002SZ. 98