A core text for courses in Financial Research Methods or Financial Statistics, and a reference to be used throughout an entire graduate or undergraduate financial program. Unique in both content and approach, this text/reference provides a welcome solution to a problem all to often encountered by finance students at both the undergraduate and graduate levels: they take a statistics course, leave the class thinking they know statistics, but when asked to carry out the methodology in a research setting, they invariably cannot. Unlike other financial statistics texts (which simply explain how a procedure should be done), this text focuses on key financial procedures and breaks them down to a level truly accessible to students by showing them in detail what to do at each step in the proceduremuch like following a recipe in a cookbook. And, because raw financial datasets are used, each method is related directly to finance and to the specific problems financial data presents. Students also become adept at using statistical procedures related exclusively to finance (often tersely described only in academic journals). This text will also serve students well as a reference to be used throughout their entire graduate or undergraduate financial program.
Features + Benefits
A focus on showing rather than telling-Presents each methodological procedure step-by-step; no detail is left out.
Allows instructors to spend more time teaching finance and less time explaining how to actually carry out the statistical procedures.
Screen captures in various computer programs-Demonstrates each step by presenting the corresponding action in Excel, SPSS, or EViews, whichever is appropriate, through a series of screen captures.
Allows students to match examples in the book with their computer screens at home.
Raw datasets-Included and used in each example.
Allows students to gain first-hand experience using the types of data they will actually encounter in their own research projects.
A text and reference.
Provides students with a solid reference tool for several finance courses they'll take throughout their program.
I. GETTING STARTED.
1. Understanding Your Data.
2. Preparing Your Data for Analysis.
II. BASIC FINANCIAL STATISTICS/METHODOLOGIES.
3. Correlation.
4. Autocorrelation.
5. Partial Autocorrelation.
6. Autocorrelation for Nonparametric Data (Wald-Wolfowitz Runs Test).
7. T-test.
8. Analysis of Variance.
9. Regression.
10. Factor Analysis.
11. Calculating a Stock's Beta.
12. Predictive Ability.
III. ADVANCED FINANCIAL TECHNIQUES/METHODOLOGIES.
13. Event Studies.
14. Unit Root Test.
15. Granger Causality.
16. Cointegration.
17. Vector Autoregression.
18. Vector Error Correction.
19. ARCH/GARCH.
20. Programming a Binomial Option Pricing Model.
21. Programming a Black-Scholes Option Pricing Model.
IV. WRITING A FINANCIAL STUDY.
22. Sections in a Financial Study.
23. Bringing Output into Microsoft Word.
Appendix - Dataset Descriptions.
Features + Benefits
A focus on showing rather than telling-Presents each methodological procedure step-by-step; no detail is left out.
Allows instructors to spend more time teaching finance and less time explaining how to actually carry out the statistical procedures.
Screen captures in various computer programs-Demonstrates each step by presenting the corresponding action in Excel, SPSS, or EViews, whichever is appropriate, through a series of screen captures.
Allows students to match examples in the book with their computer screens at home.
Raw datasets-Included and used in each example.
Allows students to gain first-hand experience using the types of data they will actually encounter in their own research projects.
A text and reference.
Provides students with a solid reference tool for several finance courses they'll take throughout their program.
I. GETTING STARTED.
1. Understanding Your Data.
2. Preparing Your Data for Analysis.
II. BASIC FINANCIAL STATISTICS/METHODOLOGIES.
3. Correlation.
4. Autocorrelation.
5. Partial Autocorrelation.
6. Autocorrelation for Nonparametric Data (Wald-Wolfowitz Runs Test).
7. T-test.
8. Analysis of Variance.
9. Regression.
10. Factor Analysis.
11. Calculating a Stock's Beta.
12. Predictive Ability.
III. ADVANCED FINANCIAL TECHNIQUES/METHODOLOGIES.
13. Event Studies.
14. Unit Root Test.
15. Granger Causality.
16. Cointegration.
17. Vector Autoregression.
18. Vector Error Correction.
19. ARCH/GARCH.
20. Programming a Binomial Option Pricing Model.
21. Programming a Black-Scholes Option Pricing Model.
IV. WRITING A FINANCIAL STUDY.
22. Sections in a Financial Study.
23. Bringing Output into Microsoft Word.
Appendix - Dataset Descriptions.