(EBook PDF) Quantitative Investment Analysis 4th Edition by Cfa Institute 1119743648 9781119743644 full chapters

Quantitative Investment Analysis 4th Edition by Cfa Institute – Ebook PDF Instant Download/DeliveryISBN:  1119743648,  9781119743644 

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Product details:

ISBN-10 :  1119743648 

ISBN-13 : 9781119743644

Author: Cfa Institute 

Part of the CFA Institute Investment Series, this authoritative guide is relevant the world over and will facilitate your mastery of quantitative methods and their application in today’s investment process.

Quantitative Investment Analysis 4th Table of contents:

CHAPTER 1: THE TIME VALUE OF MONEY
LEARNING OUTCOMES
1. INTRODUCTION
2. INTEREST RATES: INTERPRETATION
3. THE FUTURE VALUE OF A SINGLE CASH FLOW
4. THE FUTURE VALUE OF A SERIES OF CASH FLOWS
5. THE PRESENT VALUE OF A SINGLE CASH FLOW
6. THE PRESENT VALUE OF A SERIES OF CASH FLOWS
7. SOLVING FOR RATES, NUMBER OF PERIODS, OR SIZE OF ANNUITY PAYMENTS
8. SUMMARY
PRACTICE PROBLEMS
CHAPTER 2: ORGANIZING, VISUALIZING, AND DESCRIBING DATA
LEARNING OUTCOMES
1. INTRODUCTION
2. DATA TYPES
3. DATA SUMMARIZATION
4. DATA VISUALIZATION
5. MEASURES OF CENTRAL TENDENCY
6. OTHER MEASURES OF LOCATION: QUANTILES
7. MEASURES OF DISPERSION
8. THE SHAPE OF THE DISTRIBUTIONS: SKEWNESS
9. THE SHAPE OF THE DISTRIBUTIONS: KURTOSIS
10. CORRELATION BETWEEN TWO VARIABLES
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
5. SUMMARY
REFERENCES
PRACTICE PROBLEMS
CHAPTER 4: COMMON PROBABILITY DISTRIBUTIONS
LEARNING OUTCOMES
1. INTRODUCTION TO COMMON PROBABILITY DISTRIBUTIONS
2. DISCRETE RANDOM VARIABLES
3. CONTINUOUS RANDOM VARIABLES
4. INTRODUCTION TO MONTE CARLO SIMULATION
5. SUMMARY
REFERENCES
PRACTICE PROBLEMS
CHAPTER 5: SAMPLING AND ESTIMATION
LEARNING OUTCOMES
1. INTRODUCTION
2. SAMPLING
3. DISTRIBUTION OF THE SAMPLE MEAN
4. POINT AND INTERVAL ESTIMATES OF THE POPULATION MEAN
5. MORE ON SAMPLING
6. SUMMARY
REFERENCES
PRACTICE PROBLEMS
CHAPTER 6: HYPOTHESIS TESTING
LEARNING OUTCOMES
1. INTRODUCTION
2. HYPOTHESIS TESTING
3. HYPOTHESIS TESTS CONCERNING THE MEAN
4. HYPOTHESIS TESTS CONCERNING VARIANCE AND CORRELATION
5. OTHER ISSUES: NONPARAMETRIC INFERENCE
6. SUMMARY
REFERENCES
PRACTICE PROBLEMS
CHAPTER 7: INTRODUCTION TO LINEAR REGRESSION
LEARNING OUTCOMES
1. INTRODUCTION
2. LINEAR REGRESSION
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
3. USING DUMMY VARIABLES IN REGRESSIONS
4. VIOLATIONS OF REGRESSION ASSUMPTIONS
5. MODEL SPECIFICATION AND ERRORS IN SPECIFICATION
6. 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
4. AUTOREGRESSIVE (AR) TIME-SERIES MODELS
5. RANDOM WALKS AND UNIT ROOTS
6. MOVING-AVERAGE TIME-SERIES MODELS
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
SUMMARY
REFERENCES
PRACTICE PROBLEMS
CHAPTER 10: MACHINE LEARNING
LEARNING OUTCOMES
1. INTRODUCTION
2. MACHINE LEARNING AND INVESTMENT MANAGEMENT
3. WHAT IS MACHINE LEARNING?
4. OVERVIEW OF EVALUATING ML ALGORITHM PERFORMANCE
5. SUPERVISED MACHINE LEARNING ALGORITHMS
6. UNSUPERVISED MACHINE LEARNING ALGORITHMS
7. NEURAL NETWORKS, DEEP LEARNING NETS, AND 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
5. DATA EXPLORATION OBJECTIVES AND METHODS
6. MODEL TRAINING
7. FINANCIAL FORECASTING PROJECT: CLASSIFYING AND PREDICTING SENTIMENT FOR STOCKS
8. SUMMARY
PRACTICE PROBLEMS
CHAPTER 12: USING MULTIFACTOR MODELS
LEARNING OUTCOMES
1. INTRODUCTION
2. MULTIFACTOR MODELS AND MODERN PORTFOLIO THEORY
3. ARBITRAGE PRICING THEORY
4. MULTIFACTOR MODELS: TYPES
5. MULTIFACTOR MODELS: SELECTED APPLICATIONS
6. SUMMARY
REFERENCES
PRACTICE PROBLEMS
CHAPTER 13: MEASURING AND MANAGING MARKET RISK
LEARNING OUTCOMES
1. INTRODUCTION
2. UNDERSTANDING VALUE AT RISK
3. OTHER KEY RISK MEASURES—SENSITIVITY AND SCENARIO MEASURES
4. USING CONSTRAINTS IN MARKET RISK MANAGEMENT
5. APPLICATIONS OF RISK MEASURES
6. SUMMARY
REFERENCE
PRACTICE PROBLEMS
CHAPTER 14: BACKTESTING AND SIMULATION
LEARNING OUTCOMES
1. INTRODUCTION
2. THE OBJECTIVES OF BACKTESTING
3. THE BACKTESTING PROCESS
4. METRICS AND VISUALS USED IN BACKTESTING
5. COMMON PROBLEMS IN BACKTESTING
6. BACKTESTING FACTOR ALLOCATION STRATEGIES
7. COMPARING METHODS OF MODELING RANDOMNESS
8. SCENARIO ANALYSIS
9. HISTORICAL SIMULATION VERSUS MONTE CARLO SIMULATION
10. HISTORICAL SIMULATION
11. MONTE CARLO SIMULATION
12. SENSITIVITY ANALYSIS
13. SUMMARY

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