The Principles of Quantitative Finance

Chapter 1 Introduction
Introduction
Why Statistics in Finance?
From Data to Financial Decision Making
Population, Parameter, and Statistical Experiment
Parameter
Statistic (or Sample Statistic)
What Is a Sampling Distribution?
Standard Error
Descriptive Statistics versus Inferential Statistics
Descriptive Statistics
Inferential Statistics
Estimation of the Population Variance
Excel Application
End-of-Chapter Questions
Case

Chapter 2 Review of Basic Statistics
Introduction
Measures of Central Tendency, Dispersion, and Shape
Measures of Central Tendency Summaries
Mean
Median
Mode
Criteria for Selecting Among the Mean, Median, and Mode
Measures of Return (Central Tendency)
Quartiles and Percentiles
Quartiles
Percentiles
Measures of Dispersion
Measures of Risk (Variation of Measures of Dispersion)
Range
Variance
Standard Deviation
Coefficient of Variation
Sharpe Ratio
The Normal Distribution
Properties of the Normal Distribution
Empirical Rule
Chebyshev’s Theorem
Skewness and Kurtosis
Skewness
Kurtosis
Numerical Example & Discussions
Comments about Variability
Central Limit Theorem
Z-Score and Normal Probability
Confidence Intervals
Covariance and Correlation
Beta Risk
Excel Functions
Problems
Cases

Chapter 3 Hypothesis Testing
Introduction
The Null and Alternative Hypotheses
Z-Test of Hypothesis for the Mean (s Known)
Risks in Decision Making Using Hypothesis-Testing Methodology
The Level of Significance
The Confidence Coefficient
The b Risk
The Power of a Test
Regions of Rejection and Nonrejection
Compute Z-Test Statistic
The r-Value Approach to Hypothesis Testing
A Connection between Confidence Interval Estimation and Hypothesis Testing
One-tailed Tests
The Critical Value Approach
T-Test of Hypothesis for the Mean (s Unknown)
Two-Sample T-Test 70 c2 Test of Hypothesis for the Variance or Standard Deviation
Comment – Checking the Assumptions of the c2 Test for the Variance or Standard Deviation
F-Test—Testing for Multiple Variances
Using Excel for Hypothesis Testing
Problems
Cases

Chapter 4 Managing Data Patterns
Introduction
Time Series Analyses
Two Basic Forecasting Methods
Time Series Components
Lags
Autocorrelation
Autocorrelation Analysis
Main Questions to Determine the Pattern of Data
Are the Data Random?
Do the Data have a Trend?
Autocorrelation for Different Types of Time-series Data
Measuring Forecasting Error
Methods to Evaluate the Forecasting Errors
Problems
Cases
Consumer Credit Outstanding Revolving 2006–2009 (billions)

Chapter 5 Financial Forecasting Techniques
Introduction
Naïve Models
Rate of Change, or a Percentage Value
Naïve Models and Seasonal Variations
Averaging Methods
Simple Averages
Moving Averages
Double Moving Averages
Exponential Smoothing Methods
Data Series with Linear Trend
Exponential Smoothing Adjusted for Trend—Holt’s Method
Exponential Smoothing Adjusted for Trend and Seasonal Variation: Winter Model
Problems
Cases

Chapter 6 Simple Regression and Finance
Finding the Sample Simple Regression Model
Exports versus Imports—Example
Interpretation of the Coefficients and Model
Testing the Sample Coefficient
Correlation of the Simple Regression Model
Coefficient of Determination for Simple Regression
Summary of the Export and Import Example
Siriaco Mutual Fund and DJIA Returns—Example
Walt Disney Linear Simple Regression—Example
Predicting Mutual Fund with Gold—Example
Assumptions Required to Use a Linear Regression
Problems

Chapter 7 Multiple Regression and Finance
Introduction
Start with Simple Regression
Qualitative Financial Analysis of Multiple Regression
Testing the Overall Model—The F-Distribution
Test for the Model Correlation
Test for Coefficient of Determination
Test for Partial Significance for Each Independent Variable
Final Predicting Regression Model
Predicting Home Heating Gas Example
Collinearity (Multicollinearity)
Collinearity Example Using Disney’s Data
Detecting Collinearity Using Correlation Matrix
Detecting Collinearity Using VIF (Variance Inflationary Factor)
Using Subsets Model to Predict a Model
Serial Correlation and Error Variance
Testing Serial Correlation
Durbin–Watson Analysis of Serial Correlation
Durbin–Watson Test Criteria
Solutions to Serial Correlation Problems
Problems

Chapter 8 Event Study Application
The Five Steps in an Event Study
Select an Event Study
Identify the Event Date Window
Collect and Analyze the Sample Event Data
Divide the Data between Pre- and Post-Event Periods
Testing for Normal and Abnormal Returns
Conclusion
Cumulative Abnormal Returns Test

Appendix
Appendix A: Standard Normal Distribution
Appendix B: Student’s T-Distribution
Appendix C: Chi-Square Distribution
Appendix D: F-Distribution Table (Right-hand tail is .05)
F-Distribution Table (Right-hand tail is .025)
F-Distribution Table (Right-hand tail is .01)
F-Distribution Table (Right-hand tail is .005)