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Quantitative Investment Analysis.

By: Institute, CFA.
Series: Publisher: Newark : John Wiley & Sons, Incorporated, ©2020Copyright date: ©2020Edition: 4th ed.Description: 944 p.Content type: text Media type: unmediated Carrier type: volumeISBN: 9781119743620.Genre/Form: Print books.
Contents:
Cover -- Quantitative Investment Analysis -- Title page -- Copyright page -- Contents -- Preface -- Acknowledgments -- About the CFA Institute Investment Series -- Chapter 1: The Time Value of Money -- Learning Outcomes -- 1. Introduction -- 2. Interest Rates: Interpretation -- 3. The Future Value of a Single Cash Flow -- 3.1. The Frequency of Compounding -- 3.2. Continuous Compounding -- 3.3. Stated and Effective Rates -- 4. The Future Value of a Series of Cash Flows -- 4.1. Equal Cash Flows-Ordinary Annuity -- 4.2. Unequal Cash Flows -- 5. The Present Value of a Single Cash Flow -- 5.1. Finding the Present Value of a Single Cash Flow -- 5.2. The Frequency of Compounding -- 6. The Present Value of a Series of Cash Flows -- 6.1. The Present Value of a Series of Equal Cash Flows -- 6.2. The Present Value of an Infinite Series of Equal Cash Flows-Perpetuity -- 6.3. Present Values Indexed at Times Other than t = 0 -- 6.4. The Present Value of a Series of Unequal Cash Flows -- 7. Solving for Rates, Number of Periods, or Size of Annuity Payments -- 7.1. Solving for Interest Rates and Growth Rates -- 7.2. Solving for the Number of Periods -- 7.3. Solving for the Size of Annuity Payments -- 7.4. Review of Present and Future Value Equivalence -- 7.5. The Cash Flow Additivity Principle -- 8. Summary -- Practice Problems -- Chapter 2: Organizing, Visualizing, and Describing Data -- Learning Outcomes -- 1. Introduction -- 2. Data Types -- 2.1. Numerical versus Categorical Data -- 2.2. Cross-Sectional versus Time-Series versus Panel Data -- 2.3. Structured versus Unstructured Data -- 3. Data Summarization -- 3.1. Organizing Data for Quantitative Analysis -- 3.2. Summarizing Data Using Frequency Distributions -- 3.3. Summarizing Data Using a Contingency Table -- 4. Data Visualization -- 4.1. Histogram and Frequency Polygon -- 4.2. Bar Chart -- 4.3. Tree-Map.
4.4. Word Cloud -- 4.5. Line Chart -- 4.6. Scatter Plot -- 4.7. Heat Map -- 4.8. Guide to Selecting among Visualization Types -- 5. Measures of Central Tendency -- 5.1. The Arithmetic Mean -- 5.2. The Median -- 5.3. The Mode -- 5.4. Other Concepts of Mean -- 6. Other Measures of Location: Quantiles -- 6.1. Quartiles, Quintiles, Deciles, and Percentiles -- 6.2. Quantiles in Investment Practice -- 7. Measures of Dispersion -- 7.1. The Range -- 7.2. The Mean Absolute Deviation -- 7.3. Sample Variance and Sample Standard Deviation -- 7.4. Target Downside Deviation -- 7.5. Coefficient of Variation -- 8. The Shape of the Distributions: Skewness -- 9. The Shape of the Distributions: Kurtosis -- 10. Correlation between Two Variables -- 10.1. Properties of Correlation -- 10.2. Limitations of Correlation Analysis -- 11. Summary -- Practice Problems -- Chapter 3: Probability Concepts -- Learning Outcomes -- 1. Introduction -- 2. Probability, Expected Value, and Variance -- 3. Portfolio Expected Return and Variance of Return -- 4. Topics in Probability -- 4.1. Bayes' Formula -- 4.2. Principles of Counting -- 5. Summary -- References -- Practice Problem -- Chapter 4: Common Probability Distributions -- Learning Outcomes -- 1. Introduction to Common Probability Distributions -- 2. Discrete Random Variables -- 2.1. The Discrete Uniform Distribution -- 2.2. The Binomial Distribution -- 3. Continuous Random Variables -- 3.1. Continuous Uniform Distribution -- 3.2. The Normal Distribution -- 3.3. Applications of the Normal Distribution -- 3.4. The Lognormal Distribution -- 4. Introduction to Monte Carlo Simulation -- 5. Summary -- References -- Practice Problems -- Chapter 5: Sampling and Estimation -- Learning Outcomes -- 1. Introduction -- 2. Sampling -- 2.1. Simple Random Sampling -- 2.2. Stratified Random Sampling -- 2.3. Time-Series and Cross-Sectional Data.
3. Distribution of the Sample Mean -- 3.1. The Central Limit Theorem -- 4. Point and Interval Estimates of the Population Mean -- 4.1. Point Estimators -- 4.2. Confidence Intervals for the Population Mean -- 4.3. Selection of Sample Size -- 5. More on Sampling -- 5.1. Data-Mining Bias -- 5.2. Sample Selection Bias -- 5.3. Look-Ahead Bias -- 5.4. Time-Period Bias -- 6. Summary -- References -- Practice Problems -- Chapter 6: Hypothesis Testing -- Learning Outcomes -- 1. Introduction -- 2. Hypothesis Testing -- 3. Hypothesis Tests Concerning the Mean -- 3.1. Tests Concerning a Single Mean -- 3.2. Tests Concerning Differences between Means -- 3.3. Tests Concerning Mean Differences -- 4. Hypothesis Tests Concerning Variance and Correlation -- 4.1. Tests Concerning a Single Variance -- 4.2. Tests Concerning the Equality (Inequality) of Two Variances -- 4.3. Tests Concerning Correlation -- 5. Other Issues: Nonparametric Inference -- 5.1. Nonparametric Tests Concerning Correlation: The Spearman Rank Correlation Coefficient -- 5.2. Nonparametric Inference: Summary -- 6. Summary -- References -- Practice Problems -- Chapter 7: Introduction to Linear Regression -- Learning Outcomes -- 1. Introduction -- 2. Linear Regression -- 2.1. Linear Regression with One Independent Variable -- 3. Assumptions of the Linear Regression Model -- 4. The Standard Error of Estimate -- 5. The Coefficient of Determination -- 6. Hypothesis Testing -- 7. Analysis of Variance in a Regression with One Independent Variable -- 8. Prediction Intervals -- 9. Summary -- References -- Practice Problems -- Chapter 8: Multiple Regression -- Learning Outcomes -- 1. Introduction -- 2. Multiple Linear Regression -- 2.1. Assumptions of the Multiple Linear Regression Model -- 2.2. Predicting the Dependent Variable in a Multiple Regression Model.
2.3. Testing Whether All Population Regression Coefficients Equal Zero -- 2.4. Adjusted R2 -- 3. Using Dummy Variables in Regressions -- 3.1. Defining a Dummy Variable -- 3.2. Visualizing and Interpreting Dummy Variables -- 3.3. Testing for Statistical Significance -- 4. Violations of Regression Assumptions -- 4.1. Heteroskedasticity -- 4.2. Serial Correlation -- 4.3. Multicollinearity -- 4.4. Heteroskedasticity, Serial Correlation, Multicollinearity: Summarizing the Issues -- 5. Model Specification and Errors in Specification -- 5.1. Principles of Model Specification -- 5.2. Misspecified Functional Form -- 5.3. Time-Series Misspecification (Independent Variables Correlated with Errors -- 5.4. Other Types of Time-Series Misspecification -- 6. Models with Qualitative Dependent Variables -- 6.1. Models with Qualitative Dependent Variables -- 7. Summary -- References -- Practice Problems -- Chapter 9: Time-Series Analysis -- Learning Outcomes -- 1. Introduction to Time-Series Analysis -- 2. Challenges of Working with Time Series -- 3. Trend Models -- 3.1. Linear Trend Models -- 3.2. Log-Linear Trend Models -- 3.3. Trend Models and Testing for Correlated Errors -- 4. Autoregressive (AR) Time-Series Models -- 4.1. Covariance-Stationary Series -- 4.2. Detecting Serially Correlated Errors in an Autoregressive Model -- 4.3. Mean Reversion -- 4.4. Multiperiod Forecasts and the Chain Rule of Forecasting -- 4.5. Comparing Forecast Model Performance -- 4.6. Instability of Regression Coefficients -- 5. Random Walks and Unit Roots -- 5.1. Random Walks -- 5.2. The Unit Root Test of Nonstationarity -- 6. Moving-Average Time-Series Models -- 6.1. Smoothing Past Values with an n-Period Moving Average -- 6.2. Moving-Average Time-Series Models for Forecasting -- 7. Seasonality in Time-Series Models -- 8. Autoregressive Moving-Average Models.
9. Autoregressive Conditional Heteroskedasticity Models -- 10. Regressions with More than One Time Series -- 11. Other Issues in Time Series -- 12. Suggested Steps in Time-Series Forecasting -- 13. Summary -- References -- Practice Problems -- Chapter 10: Machine Learning -- Learning Outcomes -- 1. Introduction -- 2. Machine Learning and Investment Management -- 3. What is Machine Learning? -- 3.1. Defining Machine Learning -- 3.2. Supervised Learning -- 3.3. Unsupervised Learning -- 3.4. Deep Learning and Reinforcement Learning -- 3.5. Summary of ML Algorithms and How to Choose among Them -- 4. Overview of Evaluating ML Algorithm Performance -- 4.1. Generalization and Overfitting -- 4.2. Errors and Overfitting -- 4.3. Preventing Overfitting in Supervised Machine Learning -- 5. Supervised Machine Learning Algorithms -- 5.1. Penalized Regression -- 5.2. Support Vector Machine -- 5.3. K-Nearest Neighbor -- 5.4. Classification and Regression Tree -- 5.5. Ensemble Learning and Random Forest -- 6. Unsupervised Machine Learning Algorithms -- 6.1. Principal Components Analysis -- 6.2. Clustering -- 7. Neural Networks, Deep Learning Nets, and Reinforcement Learning -- 7.1. Neural Networks -- 7.2. Deep Learning Neural Networks -- 7.3. Reinforcement Learning -- 8. Choosing an Appropriate ML Algorithm -- 9. Summary -- References -- Practice Problems -- Chapter 11: Big Data Projects -- Learning Outcomes -- 1. Introduction -- 2. Big Data in Investment Management -- 3. Steps in Executing a Data Analysis Project: Financial Forecasting with Big Data -- 4. Data Preparation and Wrangling -- 4.1. Structured Data -- 4.2. Unstructured (Text) Data -- 5. Data Exploration Objectives and Methods -- 5.1. Structured Data -- 5.2. Unstructured Data: Text Exploration -- 6. Model Training -- 6.1. Structured and Unstructured Data.
7. Financial Forecasting Project: Classifying and Predicting Sentiment for Stocks.
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Cover -- Quantitative Investment Analysis -- Title page -- Copyright page -- Contents -- Preface -- Acknowledgments -- About the CFA Institute Investment Series -- Chapter 1: The Time Value of Money -- Learning Outcomes -- 1. Introduction -- 2. Interest Rates: Interpretation -- 3. The Future Value of a Single Cash Flow -- 3.1. The Frequency of Compounding -- 3.2. Continuous Compounding -- 3.3. Stated and Effective Rates -- 4. The Future Value of a Series of Cash Flows -- 4.1. Equal Cash Flows-Ordinary Annuity -- 4.2. Unequal Cash Flows -- 5. The Present Value of a Single Cash Flow -- 5.1. Finding the Present Value of a Single Cash Flow -- 5.2. The Frequency of Compounding -- 6. The Present Value of a Series of Cash Flows -- 6.1. The Present Value of a Series of Equal Cash Flows -- 6.2. The Present Value of an Infinite Series of Equal Cash Flows-Perpetuity -- 6.3. Present Values Indexed at Times Other than t = 0 -- 6.4. The Present Value of a Series of Unequal Cash Flows -- 7. Solving for Rates, Number of Periods, or Size of Annuity Payments -- 7.1. Solving for Interest Rates and Growth Rates -- 7.2. Solving for the Number of Periods -- 7.3. Solving for the Size of Annuity Payments -- 7.4. Review of Present and Future Value Equivalence -- 7.5. The Cash Flow Additivity Principle -- 8. Summary -- Practice Problems -- Chapter 2: Organizing, Visualizing, and Describing Data -- Learning Outcomes -- 1. Introduction -- 2. Data Types -- 2.1. Numerical versus Categorical Data -- 2.2. Cross-Sectional versus Time-Series versus Panel Data -- 2.3. Structured versus Unstructured Data -- 3. Data Summarization -- 3.1. Organizing Data for Quantitative Analysis -- 3.2. Summarizing Data Using Frequency Distributions -- 3.3. Summarizing Data Using a Contingency Table -- 4. Data Visualization -- 4.1. Histogram and Frequency Polygon -- 4.2. Bar Chart -- 4.3. Tree-Map.

4.4. Word Cloud -- 4.5. Line Chart -- 4.6. Scatter Plot -- 4.7. Heat Map -- 4.8. Guide to Selecting among Visualization Types -- 5. Measures of Central Tendency -- 5.1. The Arithmetic Mean -- 5.2. The Median -- 5.3. The Mode -- 5.4. Other Concepts of Mean -- 6. Other Measures of Location: Quantiles -- 6.1. Quartiles, Quintiles, Deciles, and Percentiles -- 6.2. Quantiles in Investment Practice -- 7. Measures of Dispersion -- 7.1. The Range -- 7.2. The Mean Absolute Deviation -- 7.3. Sample Variance and Sample Standard Deviation -- 7.4. Target Downside Deviation -- 7.5. Coefficient of Variation -- 8. The Shape of the Distributions: Skewness -- 9. The Shape of the Distributions: Kurtosis -- 10. Correlation between Two Variables -- 10.1. Properties of Correlation -- 10.2. Limitations of Correlation Analysis -- 11. Summary -- Practice Problems -- Chapter 3: Probability Concepts -- Learning Outcomes -- 1. Introduction -- 2. Probability, Expected Value, and Variance -- 3. Portfolio Expected Return and Variance of Return -- 4. Topics in Probability -- 4.1. Bayes' Formula -- 4.2. Principles of Counting -- 5. Summary -- References -- Practice Problem -- Chapter 4: Common Probability Distributions -- Learning Outcomes -- 1. Introduction to Common Probability Distributions -- 2. Discrete Random Variables -- 2.1. The Discrete Uniform Distribution -- 2.2. The Binomial Distribution -- 3. Continuous Random Variables -- 3.1. Continuous Uniform Distribution -- 3.2. The Normal Distribution -- 3.3. Applications of the Normal Distribution -- 3.4. The Lognormal Distribution -- 4. Introduction to Monte Carlo Simulation -- 5. Summary -- References -- Practice Problems -- Chapter 5: Sampling and Estimation -- Learning Outcomes -- 1. Introduction -- 2. Sampling -- 2.1. Simple Random Sampling -- 2.2. Stratified Random Sampling -- 2.3. Time-Series and Cross-Sectional Data.

3. Distribution of the Sample Mean -- 3.1. The Central Limit Theorem -- 4. Point and Interval Estimates of the Population Mean -- 4.1. Point Estimators -- 4.2. Confidence Intervals for the Population Mean -- 4.3. Selection of Sample Size -- 5. More on Sampling -- 5.1. Data-Mining Bias -- 5.2. Sample Selection Bias -- 5.3. Look-Ahead Bias -- 5.4. Time-Period Bias -- 6. Summary -- References -- Practice Problems -- Chapter 6: Hypothesis Testing -- Learning Outcomes -- 1. Introduction -- 2. Hypothesis Testing -- 3. Hypothesis Tests Concerning the Mean -- 3.1. Tests Concerning a Single Mean -- 3.2. Tests Concerning Differences between Means -- 3.3. Tests Concerning Mean Differences -- 4. Hypothesis Tests Concerning Variance and Correlation -- 4.1. Tests Concerning a Single Variance -- 4.2. Tests Concerning the Equality (Inequality) of Two Variances -- 4.3. Tests Concerning Correlation -- 5. Other Issues: Nonparametric Inference -- 5.1. Nonparametric Tests Concerning Correlation: The Spearman Rank Correlation Coefficient -- 5.2. Nonparametric Inference: Summary -- 6. Summary -- References -- Practice Problems -- Chapter 7: Introduction to Linear Regression -- Learning Outcomes -- 1. Introduction -- 2. Linear Regression -- 2.1. Linear Regression with One Independent Variable -- 3. Assumptions of the Linear Regression Model -- 4. The Standard Error of Estimate -- 5. The Coefficient of Determination -- 6. Hypothesis Testing -- 7. Analysis of Variance in a Regression with One Independent Variable -- 8. Prediction Intervals -- 9. Summary -- References -- Practice Problems -- Chapter 8: Multiple Regression -- Learning Outcomes -- 1. Introduction -- 2. Multiple Linear Regression -- 2.1. Assumptions of the Multiple Linear Regression Model -- 2.2. Predicting the Dependent Variable in a Multiple Regression Model.

2.3. Testing Whether All Population Regression Coefficients Equal Zero -- 2.4. Adjusted R2 -- 3. Using Dummy Variables in Regressions -- 3.1. Defining a Dummy Variable -- 3.2. Visualizing and Interpreting Dummy Variables -- 3.3. Testing for Statistical Significance -- 4. Violations of Regression Assumptions -- 4.1. Heteroskedasticity -- 4.2. Serial Correlation -- 4.3. Multicollinearity -- 4.4. Heteroskedasticity, Serial Correlation, Multicollinearity: Summarizing the Issues -- 5. Model Specification and Errors in Specification -- 5.1. Principles of Model Specification -- 5.2. Misspecified Functional Form -- 5.3. Time-Series Misspecification (Independent Variables Correlated with Errors -- 5.4. Other Types of Time-Series Misspecification -- 6. Models with Qualitative Dependent Variables -- 6.1. Models with Qualitative Dependent Variables -- 7. Summary -- References -- Practice Problems -- Chapter 9: Time-Series Analysis -- Learning Outcomes -- 1. Introduction to Time-Series Analysis -- 2. Challenges of Working with Time Series -- 3. Trend Models -- 3.1. Linear Trend Models -- 3.2. Log-Linear Trend Models -- 3.3. Trend Models and Testing for Correlated Errors -- 4. Autoregressive (AR) Time-Series Models -- 4.1. Covariance-Stationary Series -- 4.2. Detecting Serially Correlated Errors in an Autoregressive Model -- 4.3. Mean Reversion -- 4.4. Multiperiod Forecasts and the Chain Rule of Forecasting -- 4.5. Comparing Forecast Model Performance -- 4.6. Instability of Regression Coefficients -- 5. Random Walks and Unit Roots -- 5.1. Random Walks -- 5.2. The Unit Root Test of Nonstationarity -- 6. Moving-Average Time-Series Models -- 6.1. Smoothing Past Values with an n-Period Moving Average -- 6.2. Moving-Average Time-Series Models for Forecasting -- 7. Seasonality in Time-Series Models -- 8. Autoregressive Moving-Average Models.

9. Autoregressive Conditional Heteroskedasticity Models -- 10. Regressions with More than One Time Series -- 11. Other Issues in Time Series -- 12. Suggested Steps in Time-Series Forecasting -- 13. Summary -- References -- Practice Problems -- Chapter 10: Machine Learning -- Learning Outcomes -- 1. Introduction -- 2. Machine Learning and Investment Management -- 3. What is Machine Learning? -- 3.1. Defining Machine Learning -- 3.2. Supervised Learning -- 3.3. Unsupervised Learning -- 3.4. Deep Learning and Reinforcement Learning -- 3.5. Summary of ML Algorithms and How to Choose among Them -- 4. Overview of Evaluating ML Algorithm Performance -- 4.1. Generalization and Overfitting -- 4.2. Errors and Overfitting -- 4.3. Preventing Overfitting in Supervised Machine Learning -- 5. Supervised Machine Learning Algorithms -- 5.1. Penalized Regression -- 5.2. Support Vector Machine -- 5.3. K-Nearest Neighbor -- 5.4. Classification and Regression Tree -- 5.5. Ensemble Learning and Random Forest -- 6. Unsupervised Machine Learning Algorithms -- 6.1. Principal Components Analysis -- 6.2. Clustering -- 7. Neural Networks, Deep Learning Nets, and Reinforcement Learning -- 7.1. Neural Networks -- 7.2. Deep Learning Neural Networks -- 7.3. Reinforcement Learning -- 8. Choosing an Appropriate ML Algorithm -- 9. Summary -- References -- Practice Problems -- Chapter 11: Big Data Projects -- Learning Outcomes -- 1. Introduction -- 2. Big Data in Investment Management -- 3. Steps in Executing a Data Analysis Project: Financial Forecasting with Big Data -- 4. Data Preparation and Wrangling -- 4.1. Structured Data -- 4.2. Unstructured (Text) Data -- 5. Data Exploration Objectives and Methods -- 5.1. Structured Data -- 5.2. Unstructured Data: Text Exploration -- 6. Model Training -- 6.1. Structured and Unstructured Data.

7. Financial Forecasting Project: Classifying and Predicting Sentiment for Stocks.

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