具体描述
《实验设计和分析》主要内容包括：Principles and Techniques、Design: Basic Principles and Techniques、The Art of Experimentation、Replication、Blocking、Randomization、Analysis: Basic Principles and Techniques、Planning Experiments、A Checklist for Planning Experiments、Real Experiment——Cotton-Spinning Experiment等等。
深入探索现代金融市场的奥秘：一本面向实践的投资策略指南 书名： 现代投资组合理论与实践：从量化分析到资产配置的系统路径 内容简介： 在全球经济一体化和金融科技飞速发展的今天，投资决策的复杂性达到了前所未有的高度。传统的经验主义方法已难以应对市场的快速变化与信息过载的挑战。本书《现代投资组合理论与实践：从量化分析到资产配置的系统路径》，旨在为严肃的投资者、金融专业人士以及高阶商学院学生提供一套严谨、系统且高度实用的投资决策框架。 本书的核心目标是弥合学术理论与真实市场应用之间的鸿沟。我们摒弃晦涩难懂的纯数学推导，转而聚焦于如何将最前沿的金融工程工具和统计学方法，转化为可操作的投资策略和风险管理流程。全书结构清晰，层层递进，确保读者能够构建起从基础认知到高级策略部署的完整知识体系。 第一部分：投资理论的基石与现代框架重构 本部分首先回顾了经典资本资产定价模型（CAPM）的局限性，并迅速过渡到更为稳健的多因素模型（如Fama-French三因子及五因子模型）的应用。我们详细剖析了这些模型如何解释不同资产类别（如价值股、成长股、小盘股）的超额回报差异，并强调了构建稳健因子暴露的重要性。 关键章节聚焦： 1. 风险度量的演进： 不仅限于标准差，深入探讨了条件风险价值（CVaR）、极值理论（EVT）在极端市场事件中的应用，以及如何利用非正态性指标（如偏度和峰度）来校准风险模型。 2. 行为金融学的实战意义： 分析了认知偏差（如锚定效应、损失厌恶）如何系统性地影响市场定价，并探讨了如何利用这些偏差来识别潜在的套利机会，而非仅仅作为理论上的解释。 第二部分：量化分析与数据驱动的选股策略 在信息爆炸的时代，数据分析能力是区分优秀投资者和普通投资者的关键。本书的第二部分完全聚焦于实证量化技术，指导读者如何利用现代统计软件和编程语言（如Python/R）处理金融时间序列数据。 核心技术解析： 时间序列分析的深度挖掘： 讲解了协整检验（Cointegration）在配对交易中的应用，以及如何利用GARCH族模型（如EGARCH, GJR-GARCH）对波动率进行精确预测，这对于期权定价和风险预算至关重要。 因子模型的构建与检验： 提供了构建特质回报（Idiosyncratic Return）模型的步骤，帮助投资者剥离特定资产的非系统性风险，从而更纯粹地捕捉市场因子收益。我们详细演示了如何使用机器学习技术（如随机森林、梯度提升机）来筛选出最具预测能力的财务和市场指标，而非盲目依赖传统回归。 高频数据处理基础： 介绍了处理高频交易数据时需要注意的微观结构问题（如买卖价差、订单簿深度），为未来探索更高频率策略打下基础。 第三部分：动态资产配置与投资组合构建的艺术 构建一个初始投资组合只是第一步，如何在市场波动中维持最优的风险回报特征，才是长期成功的关键。第三部分系统阐述了动态资产配置（Dynamic Asset Allocation）的理论与工程实践。 重点内容涵盖： 1. 均值-方差模型的局限与超越： 详细介绍了Black-Litterman模型，该模型如何巧妙地融合了投资者的主观观点（Views）与市场均衡的客观信息，生成更具可实施性的目标权重。我们提供了详尽的步骤指导，包括如何量化和输入投资者的信念。 2. 风险平价（Risk Parity）的深度剖析： 不仅介绍其概念，更深入探讨了在不同宏观经济情景下（如通胀上升期、经济衰退期）如何调整风险平价模型中的资产相关性输入，以及利用杠杆的审慎管理。 3. 基于约束的优化技术： 介绍了凸优化在投资组合构建中的实际应用，如引入交易成本、流动性约束和税务效率约束，以确保模型输出的组合在真实交易环境中是高效可行的。 第四部分：绩效评估、归因与风险管理系统 一个完善的投资系统必须包含严格的绩效评估和风险反馈机制。本书最后一部分强调结果的可解释性和系统的健壮性。 绩效归因的精细化： 超越传统的简单布雷克-特雷诺指标，讲解了如何使用信息系数（IC）和信息比率（IR）来评估选股能力的持续性，并利用归因模型将组合的超额回报分解为选股、择时和资产配置的贡献度。 压力测试与情景分析： 阐述了如何构建历史回溯（Historical Backtesting）和蒙特卡洛模拟（Monte Carlo Simulation）的严谨流程，特别强调了“前视偏差”（Look-ahead Bias）和“过度拟合”（Overfitting）的规避策略，确保回测结果的可靠性。 尾部风险的实时监控： 介绍了如何建立一个实时风险仪表板，用于监控当前组合相对于预设的风险预算（如VaR敞口、特定行业集中度）的偏离情况，并触发预警机制。 本书特色： 本书的每一章都配有真实世界的案例分析（如2008年金融危机期间的因子表现、特定年份的宏观因子轮动），并提供了可供实践的伪代码和数据处理逻辑，使读者不仅“知其然”，更能“知其所以然”，最终掌握一套能够适应未来市场变化、实现长期资本增值的科学方法论。它面向的是那些希望将金融知识转化为实实在在投资回报的专业人士。
作者简介
目录信息
Preface
1. Principles and Techniques
1.1. Design: Basic Principles and Techniques
1.1.1. The Art of Experimentation
1.1.2. Replication
1.1.3. Blocking
1.1.4. Randomization
1.2. Analysis: Basic Principles and Techniques
2. Planning Experiments
2.1. Introduction
2.2. A Checklist for Planning Experiments
2.3. A Real Experiment——Cotton-Spinning Experiment
2.4. Some Standard Experimental Designs
2.4.1. Completely Randomized Designs
2.4.2. Block Designs
2.4.3. Designs with Two or More Blocking Factors
2.4.4. Split-Plot Designs
2.5. More Real Experiments
2.5.1. Soap Experiment
2.5.2. Battery Experiment
2.5.3. Cake-Baking Experiment
Exercises
3. Designs with One Source of Variation
3.1. Introduction
3.2. Randomization
3.3. Model for a Completely Randomized Design
3.4. Estimation of Parameters
3.4.1. Estimable Functions of Parameters
3.4.2. Notation
3.4.3. Obtaining Least Squares Estimates
3.4.4. Properties of Least Squares Estimators
3.4.5. Estimation ofo2
3.4.6. Confidence Bound for ~r2
3.5. One-Way Analysis of Variance
3.5.1. Testing Equality of Treatment Effects
3.5.2. Use of p-Values
3.6. Sample Sizes
3.6.1. Expected Mean Squares for Treatments
3.6.2. Sample Sizes Using Power of a Test
3.7. A Real Experiment——-Soap Experiment, Continued
3.7.1. Checklist, Continued
3.7.2. Data Collection and Analysis
3.7.3. Discussion by the Experimenter
3.7.4. Further Observations by the Experimenter
3.8. Using SAS Software
3.8.1. Randomization
3.8.2. Analysis of Variance
Exercises
4. Inferences for Contrasts and Treatment Means
4.1. Introduction
4.2. Contrasts
4.2.1. Pairwise Comparisons
4.2.2. Treatment Versus Control
4.2.3. Difference of Averages
4.2.4. Trends
4.3. Individual Contrasts and Treatment Means
4.3.1. Confidence Interval for a Single Contrast
4.3.2. Confidence Interval for a Single Treatment Mean
4.3.3. Hypothesis Test for a Single Contrast or Treatment Mean
4.4. Methods of Multiple Comparisons
4.4.1. Multiple Confidence Intervals
4.4.2. Bonferroni Method for Preplanned Comparisons
4.4.3. Scheff6 Method of Multiple Comparisons
4.4.4. Tukey Method for All Pairwise Comparisons
4.4.5. Dunnett Method for Treatment-Versus-Control Comparisons
4.4.6. Hsu Method for Multiple Comparisons with the Best
reatment
4.4.7. Combination of Methods
4.4.8. Methods Not Controlling Experimentwise Error Rate
4.5. Sample Sizes
4.6. Using SAS Software
4.6.1. Inferences on Individual Contrasts
4.6.2. Multiple Comparisons
Exercises
5. Checking Model Assumptions
5.1. Introduction
5.2. Strategy for Checking Model Assumptions
5.2.1. Residuals
5.2.2. Residual Plots
5.3. Checking the Fit of the Model
5.4. Checking for Outliers
5.5. Checking Independence of the Error Terms
5.6. Checking the Equal Variance Assumption
5.6.1. Detection of Unequal Variances
5.6.2. Data Transformations to Equalize Variances
5.6.3. Analysis with Unequal Error Variances
5.7. Checking the Normality Assumption
5.8. Using SAS Software
5.8.1. Using SAS to Generate Residual Plots
5.8.2. Transforming the Data
Exercises
6. Experiments with Two Crossed Treatment Factors
6.1. Introduction
6.2. Models and Factorial Effects
6.2.1. The Meaning of Interaction
6.2.2. Models for Two Treatment Factors
6.2.3. Checking the Assumptions on the Model
6.3. Contrasts
6.3.1. Contrasts for Main Effects and Interactions
6.3.2. Writing Contrasts as Coefficient Lists
6.4. Analysis of the Two-Way Complete Model
6.4.1. Least Squares Estimators for the Two-Way Complete Model
6.4.2. Estimation ofo~ for the Two-Way Complete Model
6.4.3. Multiple Comparisons for the Complete Model
6.4.4. Analysis of Variance for the Complete Model
6.5. Analysis of the Two-Way Main-Effects Model
6.5.1. Least Squares Estimators for the Main-Effects Model
6.5.2. Estimation ofa2 in the Main-Effects Model
6.5.3. Multiple Comparisons for the Main-Effects Model
6.5.4. Unequal Variances
6.5.5. Analysis of Variance for Equal Sample Sizes
6.5.6. Model Building
6.6. Calculating Sample Sizes
6.7. Small Experiments
6.7.1. One Observation per Cell
6.7.2. Analysis Based on Orthogonal Contrasts
6.7.3. Tukey's Test for Additivity
6.7.4. A Real Experiment——Air Velocity Experiment
6.8. Using SAS Software
6.8.1. Contrasts and Multiple Comparisons
6.8.2. Plots
6.8.3. One Observation per Cell
Exercises
7. Several Crossed Treatment Factors
7.1. Introduction
7.2. Models and Factorial Effects
7.2.1. Models
7.2.2. The Meaning of Interaction
7.2.3. Separability of Factorial Effects
7.2.4. Estimation of Factorial Contrasts
7.3. Analysis——Equal Sample Sizes
7.4. A Real Experiment——Popcorn-Microwave Experiment
7.5. One Observation per Cell
7.5.1. Analysis Assuming That Certain Interaction Effects Are egligible
7.5.2. Analysis Using Normal Probability Plot of Effect Estimates
7.5.3. Analysis Using Confidence Intervals
7.6. Design for the Control of Noise Variability
7.6.1. Analysis of Design-by-Noise Interactions
7.6.2. Analyzing the Effects of Design Factors on Variability .
7.7. Using SAS Software
7.7.1. Normal Probability Plots of Contrast Estimates
7.7.2. Voss-Wang Confidence Interval Method
7.7.3. Identification of Robust Factor Settings
7.7.4. Experiments with Empty Cells
Exercises
8. Polynomial Regression
8.1. Introduction
8.2. Models
8.3. Least Squares Estimation （Optional）
8.3.1. Normal Equations
……
9. Analysis of Covariance
10. Complete Block Designs
11. Incomplete Block Designs
12. Designs with Two Blocking Factors
13. Confounded Two-Level Factorial Experiments
14. Confounding in General Factorial Experiments
15. Fractional Factorial Experiments
16. esponse Surface Methodology
17. andom Effects and Variance Components
18. estde Models
19. plit-Plot Designs
A. ables
Bibliography
Index of Authors
Index of Experiments
Index of Subjects
· · · · · · (收起)
1. Principles and Techniques
1.1. Design: Basic Principles and Techniques
1.1.1. The Art of Experimentation
1.1.2. Replication
1.1.3. Blocking
1.1.4. Randomization
1.2. Analysis: Basic Principles and Techniques
2. Planning Experiments
2.1. Introduction
2.2. A Checklist for Planning Experiments
2.3. A Real Experiment——Cotton-Spinning Experiment
2.4. Some Standard Experimental Designs
2.4.1. Completely Randomized Designs
2.4.2. Block Designs
2.4.3. Designs with Two or More Blocking Factors
2.4.4. Split-Plot Designs
2.5. More Real Experiments
2.5.1. Soap Experiment
2.5.2. Battery Experiment
2.5.3. Cake-Baking Experiment
Exercises
3. Designs with One Source of Variation
3.1. Introduction
3.2. Randomization
3.3. Model for a Completely Randomized Design
3.4. Estimation of Parameters
3.4.1. Estimable Functions of Parameters
3.4.2. Notation
3.4.3. Obtaining Least Squares Estimates
3.4.4. Properties of Least Squares Estimators
3.4.5. Estimation ofo2
3.4.6. Confidence Bound for ~r2
3.5. One-Way Analysis of Variance
3.5.1. Testing Equality of Treatment Effects
3.5.2. Use of p-Values
3.6. Sample Sizes
3.6.1. Expected Mean Squares for Treatments
3.6.2. Sample Sizes Using Power of a Test
3.7. A Real Experiment——-Soap Experiment, Continued
3.7.1. Checklist, Continued
3.7.2. Data Collection and Analysis
3.7.3. Discussion by the Experimenter
3.7.4. Further Observations by the Experimenter
3.8. Using SAS Software
3.8.1. Randomization
3.8.2. Analysis of Variance
Exercises
4. Inferences for Contrasts and Treatment Means
4.1. Introduction
4.2. Contrasts
4.2.1. Pairwise Comparisons
4.2.2. Treatment Versus Control
4.2.3. Difference of Averages
4.2.4. Trends
4.3. Individual Contrasts and Treatment Means
4.3.1. Confidence Interval for a Single Contrast
4.3.2. Confidence Interval for a Single Treatment Mean
4.3.3. Hypothesis Test for a Single Contrast or Treatment Mean
4.4. Methods of Multiple Comparisons
4.4.1. Multiple Confidence Intervals
4.4.2. Bonferroni Method for Preplanned Comparisons
4.4.3. Scheff6 Method of Multiple Comparisons
4.4.4. Tukey Method for All Pairwise Comparisons
4.4.5. Dunnett Method for Treatment-Versus-Control Comparisons
4.4.6. Hsu Method for Multiple Comparisons with the Best
reatment
4.4.7. Combination of Methods
4.4.8. Methods Not Controlling Experimentwise Error Rate
4.5. Sample Sizes
4.6. Using SAS Software
4.6.1. Inferences on Individual Contrasts
4.6.2. Multiple Comparisons
Exercises
5. Checking Model Assumptions
5.1. Introduction
5.2. Strategy for Checking Model Assumptions
5.2.1. Residuals
5.2.2. Residual Plots
5.3. Checking the Fit of the Model
5.4. Checking for Outliers
5.5. Checking Independence of the Error Terms
5.6. Checking the Equal Variance Assumption
5.6.1. Detection of Unequal Variances
5.6.2. Data Transformations to Equalize Variances
5.6.3. Analysis with Unequal Error Variances
5.7. Checking the Normality Assumption
5.8. Using SAS Software
5.8.1. Using SAS to Generate Residual Plots
5.8.2. Transforming the Data
Exercises
6. Experiments with Two Crossed Treatment Factors
6.1. Introduction
6.2. Models and Factorial Effects
6.2.1. The Meaning of Interaction
6.2.2. Models for Two Treatment Factors
6.2.3. Checking the Assumptions on the Model
6.3. Contrasts
6.3.1. Contrasts for Main Effects and Interactions
6.3.2. Writing Contrasts as Coefficient Lists
6.4. Analysis of the Two-Way Complete Model
6.4.1. Least Squares Estimators for the Two-Way Complete Model
6.4.2. Estimation ofo~ for the Two-Way Complete Model
6.4.3. Multiple Comparisons for the Complete Model
6.4.4. Analysis of Variance for the Complete Model
6.5. Analysis of the Two-Way Main-Effects Model
6.5.1. Least Squares Estimators for the Main-Effects Model
6.5.2. Estimation ofa2 in the Main-Effects Model
6.5.3. Multiple Comparisons for the Main-Effects Model
6.5.4. Unequal Variances
6.5.5. Analysis of Variance for Equal Sample Sizes
6.5.6. Model Building
6.6. Calculating Sample Sizes
6.7. Small Experiments
6.7.1. One Observation per Cell
6.7.2. Analysis Based on Orthogonal Contrasts
6.7.3. Tukey's Test for Additivity
6.7.4. A Real Experiment——Air Velocity Experiment
6.8. Using SAS Software
6.8.1. Contrasts and Multiple Comparisons
6.8.2. Plots
6.8.3. One Observation per Cell
Exercises
7. Several Crossed Treatment Factors
7.1. Introduction
7.2. Models and Factorial Effects
7.2.1. Models
7.2.2. The Meaning of Interaction
7.2.3. Separability of Factorial Effects
7.2.4. Estimation of Factorial Contrasts
7.3. Analysis——Equal Sample Sizes
7.4. A Real Experiment——Popcorn-Microwave Experiment
7.5. One Observation per Cell
7.5.1. Analysis Assuming That Certain Interaction Effects Are egligible
7.5.2. Analysis Using Normal Probability Plot of Effect Estimates
7.5.3. Analysis Using Confidence Intervals
7.6. Design for the Control of Noise Variability
7.6.1. Analysis of Design-by-Noise Interactions
7.6.2. Analyzing the Effects of Design Factors on Variability .
7.7. Using SAS Software
7.7.1. Normal Probability Plots of Contrast Estimates
7.7.2. Voss-Wang Confidence Interval Method
7.7.3. Identification of Robust Factor Settings
7.7.4. Experiments with Empty Cells
Exercises
8. Polynomial Regression
8.1. Introduction
8.2. Models
8.3. Least Squares Estimation （Optional）
8.3.1. Normal Equations
……
9. Analysis of Covariance
10. Complete Block Designs
11. Incomplete Block Designs
12. Designs with Two Blocking Factors
13. Confounded Two-Level Factorial Experiments
14. Confounding in General Factorial Experiments
15. Fractional Factorial Experiments
16. esponse Surface Methodology
17. andom Effects and Variance Components
18. estde Models
19. plit-Plot Designs
A. ables
Bibliography
Index of Authors
Index of Experiments
Index of Subjects
· · · · · · (收起)
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