The Capstone Project – ECON 5105
The capstone of this course is analyzing a publicly traded company. The goal is to demonstrate your understanding of data analysis, as presented. You do this project with your partner. Create a PowerPoint deck, that is, a set of slides that contain the following sections1 . The presentation should be a maximum of 15 slides (and hopefully less). Don't write anything in the notes section. If it is important enough to say, it is important enough to have on the actual slide.
Part 1 - Introduction: In the first part of the introduction, explain to the reader the situation. Are you an investment advisor recommending someone should buy or sell stock in this company? Are you a CEO/CFO/CIO analyzing your own company? Are you a takeover specialist thinking of purchasing the entire company? These are only a few suggestions. Feel free to be creative. Being creative improves your grade. Once you decide, use the situation to make the whole report flow together in an exciting way. Even the professor does not want to see just page after page of regressions! Give the report context and meaning. Keep the audience interested. Make them want to read all the way through.
The second part of the introduction should describe the company. It need not be a USA company, but it MUST be publicly traded. Avoid Starbucks, Amazon, Twitter, and Apple. Generally, avoid recent start-ups. In a few paragraphs, please describe what the company does and why it is of interest. Include where the stock is traded, its ticker symbol and any other information your intended audience wants to know about the company. Do NOT cut and paste this from a financial website. Write it in your own words.
Part 2 - Return: The capital asset pricing model asserts that an asset's (share price, portfolio, etc.) expected return is related to investments that could be made in a risk-free asset or a market basket of assets. The formula is
E(Return – risk-free rate) = α + β (Market basket return – risk-free rate) β describes how the asset varies with the market basket. The market basket is a broadbased collection of diversified assets. For example, the S&P 500 index represents such a market basket. β is negative, its returns move in the opposite direction of the market basket's return. For example, the returns on utility shares often vary countercyclically to the market's performance. If β is positive, then the asset's return follows that of the market. If | β | > 1, the asset's returns exhibit greater volatility than the market. α measures the asset's unique return regardless of how the market basket performs. It is not uncommon to find that an asset's α is not statistically different from zero.
In this section of the presentation, you will calculate the parameters of the simple version of the capital asset pricing model (CAPM), calculating alpha and beta. In this section of the presentation, you need to run at least one alpha/beta regression and possibly more. The goal is to demonstrate that you understand how to run the CAPM model and explain the choices you made and what the regression(s) mean. If you decide that the data do not support the classic approach to such calculations, explain why. There are many choices to be made when doing an alpha/beta regression, such as the data frequency (daily, weekly, monthly), the length of time covered (six months, one year, three years, five years, etc.), and the type of stock market index used (NASDAQ, Wilshire 5000, Standard and Poor's). Explain clearly to the reader which choices you made and why these choices are appropriate. There are many ways to calculate returns in R. If you have daily share prices stored in the variable "price," then logreturns <- diff(log(price)) calculates log(pt-1/pt), that is, the log-returns on the share. You can also change the lag in the diff() function. For example, logwkreturns <- diff(log(price), lag = 5) gives you a running weekly return rate. Remember, you lose as many observations as your lag. The lm() won't include missing values in the calculation. You can also use R's [] capability to eliminate the missing values.
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