125.785 Research Methods in Finance

1. 125.785 Research Methods in Finance Seminar One Monday 17 July

2. Honest politicians make the other 95% look bad -- Mark Twain

3. Overview • Administrative Issues • Timetable • Labs • Textbook • Assessment • Aims and Objectives • Introduction • Eviews • Readings: Chapters 1-3, Chapter 16 optional

4. Administration • The general format will be for 1st half • A 2 hour seminar • A 2 hour lab in either CLQB4 or IIMS5/6 • Finish approximately 7pm. • Textbook is Studenmund Using Econometrics: A Practical Guide. 5th Ed. • 4th Edition can also be used.

5. Assessment • 1 Assignment Due September 1: 20% • Quiz 1: 31 July (10%) • Quiz 2: 14 August (10%) • Quiz 3: To Be Advised (10%) • Probably 28 August

6. Web Support • Web CT should be available for students • In the interim, the following website also will have material: • http://www.massey.ac.nz/~bjmoyle/mu/teach.html

7. Computer Labs • Your user-name is your student ID • Your password is your 4 digit pin number • You will benefit from bringing • A floppy disk OR • A USB drive (preferred) • We will use Eviews for this section of the course

8. Learning Objectives • Develop your skills at estimating economic relationships. • This skill cannot be memorised from a textbook or lectures • The textbook and seminars are to assist and guide you. • Increase your familiarity with statistical software.

9. Learning Outcome • To give you a sufficient background that you can: • Attempt a research project with some of the skills you have learned; or • Can progress on to advanced techniques used in financial econometrics without difficulty. • It is impossible to teach you all the tools you might use in the constraints of this paper.

10. The Unreliability of Textbooks • This is an applied paper, not a theory paper. • Every data set you model, will have ‘different’ problems present. • It is impossibleto memorise all the permutations of problems that you will encounter. • Skilled researchers are those with good problem-solving strategies, not recall of textbook stylised facts. • Most of this skill must be developed with practical work.

11. Introduction to Research • A research project involves three stages • Choosing a Topic • Analysis • Writing Report

12. Choosing a Topic • Ideally choose something you are interested in for motivation • Make sure you can get enough data • Make sure there is some substance to topic • Not purely descriptive • Not tautological (so obvious to be uninteresting). • E.g. does an increase in the number of bidders raise prices?

13. Analysis • Develop your theoretical model first • May involve reading literature • Specify the model • What causes what? • Hypothesise the effects you expect • This must be done before you run any models • Collect the data

14. Analysis 2 • Estimate the Equation • This should take the least amount of effort • Document the results • There must be enough information given, that someone else could replicate your results.

15. Report Writing • This is an important step • The purpose of research is often to generate information for a decision-maker. • Hopefully, a manager or policy-maker could read your report and learn something new. • A box of computer printouts, neitherinformsnor impresses.

16. Report Writing 2 • A report should not gloss over or ignore results that you did not expect. • It is a common mistake to not discuss results that contradict your prior beliefs. • Keeping a research journal can assist • Record your hypotheses, regression results, statistical tests etc.

17. Practical Advice • Use common sense and economic theory • E.g. Real variables should not be matched with nominal. • Ask the right questions • Sometimes regression problems are a consequence of the wrong specification • Know the context • Understand the problem, not just the statistics

18. Practical Advice 2 • Inspect the Data • Graphs or summary statistics can reveal missing variables, outliers or other anomalies. • Keep it sensibly simple • Complexity is not ‘good’ for its own sake • Consider Occam’s Razor. • Look long and hard at your results • Does it make sense? You have to explain this to others.

19. Practical Guide 3 • Practice data-mining with care • Exhaustive experimentation to ‘get’ the ‘right’ results shows you’re biased… • Be prepared to compromise • Trying to find the perfect model will drive you crazy. • Real data tends to throw up intractable problems.

20. Practical Guide 4 • Do not confuse statistical significance with meaningful magnitude. • Trivial variables may have very small effects, but are highly significant. • It is tempting to use statistical significance as a measure of a model’s performance. • Report a sensitivity analysis • Do results vary of you change the sample period etc?

21. We use statistical tools in this paper But it is not a course in statistics We will estimate the value of many parameters E.g. A mean (average) A regression coefficient We signal our uncertainty about the parameter with a type of ‘spread’. E.g. Variance Standard Deviation These uncertainty measures form the basis of statistical tests. Basic Stats

22. Recap • The main difference between statistics and other maths, is answers will have 2 dimensions • In normal algebra, variables combine to produce an explicit solution. • In statistics, we think in 2 dimensions • What we think the value of something is • How confident we are in that estimate

23. Quantifies the relationship between 2 variables. -1 ≤ r ≤ 1 Correlations imply Relationships or associations General tendencies Correlations do not prove causality Correlations can be shown graphically Correlation

24. Correlation between GDP and G

25. Suppose we wanted to Forecast prices for an asset. Determine causes of unemployment A regression “models” finance or economic data Regression Models can be used for several purposes. Forecasting Testing hypotheses Detecting influential variables Regression

26. We begin with the Ordinary Least Squares (OLS) regression model This generates a ‘straight line’ between 2 variables. The line ‘approximates’ the relationship between the two variables The variables are Dependent (Y) Independent or explanatory (X). Simple OLS Model

27. Regression Example • Y is GDP per capita • X is Govt spending • We ‘explain’ Y in terms of X • We can estimate Y if we know • Intercept of line- constant • Slope of line

28. Note on Regression • Researchers can (potentially) use many different regression techniques. • OLS is a convenient starting point. • But not all regression models use least-squares methods. • If certain assumptions are met, OLS is the best method to use.

29. Intuition • OLS is based on Cartesian Geometry. • The line we estimate (with intercept and slope), comes closer to all the observations than any other line. • We minimise the (sum of) distance between the line and the observations (squared). This is an idea that draws on geometry. • As a minimisation problem, it can be readily solved with differential calculus.

30. Eviews • Eviews is a popular (and powerful) econometrics program. • It is the software most students use for their graduate research reports

31. Where menu to estimate equations is located

32. Equation Estimation Menu

33. Dependent Variable Explanatory Variable Constant

34. Output

35. Readings • Studenmund • Chapter 16 Statistical Principles • Chapter 1-3 • WebCT • Guide to Eviews- Introduction