For this project, you should pick an empirical finance topic, gather the required data, specify your methodology, perform quantitative analyses on the data and interpret the results of these analyses, and formulate a conclusion.

Project topics

You might select your own topic for the project, but you might also consider choosing a topic from the following list of topics:

1. Model and test weak-form information efficiency in one or more Canadian stock markets.

2. Test to find out which model—the Capital Asset Pricing Model (CAPM) or Arbitrage Pricing Theory (APT)—is superior for determining returns on risky assets in Canadian markets.

3. Measure, model, and forecast the volatility of bond returns in Canada.

4. Model and test the determinants of bond credit ratings used by ratings agencies such as Moody’s.

5. Model the long-term relationship between prices and Canada-US exchange rates.

6. Find out the optimal hedge ratio for a spot position in cattle or oil markets.

7. Test technical trading rules to find out which of them makes the most money.

8. Test hypothesis that earnings or dividend announcements have no effect on stock prices.

9. Test spot and futures markets to find out which reacts more rapidly to news.

10. Forecast the correlation between the TSX index and the NASDAQ index.

11. Model and test the effect of a firm’s capital structure decisions (i.e., debt-equity weight) on share returns.

Project tasks

1. Pick a project topic.

2. Conduct a literature review which examines at least two, and at most five, articles. For each article, prepare one-paragraph summary and one-paragraph critical review, then describe how the article relates to the topic you have selected for project.

3. Get data (contact Student Support Centre if you encounter problems accessing or obtaining data).

4. Analyze data, according to these guidelines:

• If your study involves structural analyses of cross-sectional, time-series, or panel data, do the following tasks:

a. Define null hypotheses.

b. Run the regression(s).

c. Discuss how well model fits by using goodness-of-fit measures.

d. Check for violations:

• heteroscedasticity

• iid residuals

• autocorrelation

• non-normality

• multicollinearity

• linear functional form

Test null hypotheses.

• If your study involves building time-series (ARIMA) models, perform the following tasks:

i) Test for non-stationarity in the time series.

ii) Use Box-Jenkins approach to build ARIMA models with an in-sample.

iii) Get forecasts from the ARIMA models.

iv) Select the best model based on information criteria.

v) prepare project report, that must include all of these elements:

• title page- List your name, student ID number, project title, and submission date.

• introduction- Give a broad overview of your project and what you hope to learn from it.

• literature review- Summarize and critically review selected articles.

• data descriptions- describe what, where, and how you have got data or transformed them, and the time periods involved.

• methodology- Outline whether you are using structural or time-series models, and specify models (equations) used.

• result- Discuss regression results obtained in EViews, and whether or not they are favourable to your original hypotheses.

• conclusion- Briefly describe (one to two paragraphs) what you have learned from your analyses.

• references- Provide full bibliographical information in APA format, including the articles in your literature review.

• appendices—Include discussions, tables, or graphs that do not contribute directly to the “meat” of your project prepare-up, but may nevertheless be of interest to your audience (i.e., your marker).

Use Microsoft Word or other compatible word-processing software to prepare your finished assignment. You would be using EViews extensively for the project, so your project prepare-up should include explanation of analyses performed using this software package, and your conclusions must refer to these results.