Empirical Assignment Investment Analysis - autumn 2025
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The assignment is a group assignment. Your group must consist of a minimum of 3 or a
maximum of 4 students. You will have to register in your group via Canvas.
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This assignment determines 20% of the final grade of the course (it will also count for a
possible resit exam). Therefore, it is not allowed to cooperate with or copy from other
groups. Any discovery of such action will be notified to the exam committee. All students,
included repeaters have to make this assignment. Not handing in the assignment leads to
a failing the course
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The answers should be concise and to the point, providing sufficient motivation and
explanation where needed. Explain clearly what you did and why. You need to write a
report, not only giving short answers to the questions. You are free to use any software of
your choice. Do not include extensive program output but include the important results (in
tables) in the text.
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For the report you will have to use font ‘times (new) roman’ – 12 pts, using a line spacing
of 1.15 and margins of 2.5cm (similar to the setup of this document).
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The written report should not contain a title page. On the top of the first page, you should
list your group number, the names of the group members and their student number (which
consists out of 7 numbers). This should also include the AI statement (you can find
information about this in the syllabus). Use the example layout that is on the next page.
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The report – in word format – must be uploaded to Canvas. The deadline is 23h55 on
Tuesday October 7, 2025.
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The maximum size of the report is 10 pages (not including appendices), so be concise and
to the point.
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When handing in the report too late, points will be deducted for every 24 hours you
hand it in too late.
In case you have any questions, you can send a mail the assignment TA: Pietro Pocari
(P.Porcari@tilburguniversity.edu).
Formulate your question concise and to the point, which enables Pietro to answer quickly. You
should mention your group number when asking questions and you are allowed to send up to 3
emails per group. In case Pietro cannot answer your question via mail he might arrange an
online meeting to answer your question. Deadline for asking questions is Thursday October
2 23h55.
Good luck!
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First page layout of the report
------------------------------------------------------------------Groupnumber xx
Name student 1 – student number student 1
Name student 2 – student number student 2
....
Name student 3/4 – student number student 3/4
During the preparation of this work, I/We used [NAME TOOL / SERVICE / VERSION OF
AI TOOL] in order to [REASON]. The following parts of the assignment were
affected/generated by AI tool usage: [INTRODUCTION / METHODS / xxx,
DISCUSSION]. After using this tool/service, [NAME STUDENT(S)] evaluated the validity
of the tool’s outputs, including the sources that generative AI tools have used, and edited the
content as needed. As a consequence, [NAME STUDENT (S)] take(s) full responsibility for
the content of their work.
Note 1 – you will have to the text in bold capital letters with the requested information.
Note 2 – As stated in the AI policy in the syllabus: never enter sensitive or confidential data
into AI platforms, as these may not comply with Tilburg University’s or external organizations’
privacy guidelines. This implies that you are not allowed to upload any data to the AI tool you
might be using.
Question 1
Answer question 1…..
Etc.
--------------------------------------------------------------------
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Introduction
For this assignment you must collect US data using WRDS. Details how to create an WRDS
account to access the system can be found on Canvas, but it is also listed on the last page for
completeness.
Download monthly returns for 75 stocks (of companies) for the period 1987:01- 2025:06. Make
sure that you have at least 30 return observations for each firm in your sample (and use only
share codes 10 and 111). Among the 75 stocks you MUST include the following:
- Nike
(NKE)
- Microsoft
(MSFT)
- Apple
(AAPL)
- Home Depot (HD)
- Oracle
(ORCL)
- Adobe
(ADBE)
- Amgen
(AMGN)
- Intel
(INTC)
- Walmart
(WMT)
- Costco
(COST)
Also, please think carefully about which other firms you include in your sample (e.g., not only
big stocks or only small stocks, and sufficient trading volume). Take a stance on negative prices
(those are averages of bid and ask prices provided by the broker when there is no trading for
that stock).
Stock prices can be obtained in CRSP using the security files. Please use PERMNOs to identify
firms (firms can have multiple CUSIPs over time).
In case you think a specific assumption must be made, mention this clearly in your report!
For your dataset, answer the questions stated below. Read the questions carefully before you
start answering them. In addition, make the assignment in a group and do not split up the work
among group members.
Questions
1. Suppose you are a US investor who invests in a market-value weighted portfolio that
contains stocks that you collected. Calculate the market-value weighted portfolio. What is
the average monthly return, what is the risk as measured by the standard deviation and what
is the annual 99% Value at Risk for an investment of €200,000? Finally, calculate the
monthly 10% GUISE (using geometric mean) as has been explained during class.
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See also https://quant.stackexchange.com/questions/39963/what-does-share-codes-in-fama-and-french-2015exactly-mean
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Now, for questions 2 and 3, you need to use 6 companies out of the list of your downloaded
sample. You must include the following companies: Microsoft (MSFT), Oracle (ORCL) and
Walmart (WMT), thus pick 3 additional companies from your sample (for a total of 6).
2. Draw the mean-variance frontier based on the 6 assets when short sales are allowed. Next,
in the same figure, draw the mean-variance frontier when short sales are NOT allowed.
Moreover, put these 6 assets and the value-weighted portfolio (note – the value of each firm
can change every month) of those assets in the figure as well (note that these are single
points.).2 Explain the results, especially how the MV frontier is constructed and estimated.
3. Repeat question 2, but now use only the last 10 years of (monthly) data. Can you explain
the differences with the previous question if there are any?
For the remainder of this assignment use the complete dataset and do not forget to use excess
returns (in excess of the risk-free rate). Therefore, you will need the risk-free rate. Check out
WRDS again, or perhaps use the Kenneth French dataset webpage.
4. Estimate betas for all the firms in your sample, using a CAPM regression (time series).
Make a graph/plot of the cross-sectional distribution of the betas and discuss. Use a bar
graph and define intervals (e.g. 0.4-0.6, 0.6-0.8, etc.). Compute the number of firms in each
beta interval. Also, think carefully about the market. For example, if you only select
S&P500 firms, use the S&P500 as the market. If you select firms using the broader
NYSE/AMEX/NASDAQ indexes (check the exchange code), define the market as the
value-weighted average of all NYSE/AMEX/NASDAQ firms.
5. Provide a scatter plot with average returns per firm (y-axis) and beta per firm, for all firms
(x-axis) in your sample. Provide comments explaining the results of the graph.
6. Estimate a security market line for your sample (do not forget to provide a table with the
estimation results), include the SML in the scatter plot of the previous question. Next,
discuss the statistical significance and economic implications of your findings.
7. Run a cross-sectional regression of average returns on market beta, firm size and book-tomarket value (please provide the regression equations and the model estimates and tstatistics). Next, discuss the statistical significance and economic implications of your
findings. [hint: Compute for each firm the market capitalization (shares outstanding times
share price) and book-to-market value and link these to the returns using PERMNOs. Book
values can be obtained using CRSP-COMPUSTAT-merged, fundamentals annual
(BKVLPS).]
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You can use the solver-function in Excel to obtain the points on the mean-variance frontier, but you are free to
use any other program.
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WRDS class account information
Dear Students,
For the empirical assignment you will have to download data using the WRDS data system.
This requires you to create a WRDS account. Below you can find information from WRDS
how to create an account and how to enroll for the class
"323060 Investment Analysis - MSc finance Tilburg University"
You can also use this account to access WRDS for all your other (elective) courses and
your MSc. thesis if you need WRDS data.
---- WRDS registration information ---Use the following link to access the WRDS Registration form at
https://wrds-www.wharton.upenn.edu/register/?user_type=class-student
1. Follow the directions on the Registration form to enter your identifying information.
2. For the Subscriber, select your school's name from the drop-down list.
3. Your User type, Class - Students with Code, has been selected by default.
4. Enter the class code 50822cfd.
5. Click the Register for WRDS button.
WRDS accounts require two-factor authentication. We recommend you use a smartphone
for the verification process. First, install the Duo Mobile app on your phone. This free app
can be downloaded through your device’s app store. Follow the directions at How to Log
into WRDS to register your smartphone and use Duo two-factor authentication to set up
your WRDS account.
If you have any difficulty setting up your account please contact WRDS Support
at: https://wrds-www.wharton.upenn.edu/contact-support/. When opening your support
ticket you must use the email associated with your existing WRDS account or the email you
are using to set up the account.
Already Have a WRDS Account?
After you have logged into WRDS, use the following steps to enroll in our class account.
You will need the Class Code above to enroll.
1. In the top right corner of the screen, select Your Account > Your Account Info.
2. Scroll down until you see the Your Classes table and click the Enroll in a
Class button.
3. Enter the Class Code and click Submit.
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If your account has expired, follow these steps:
1. Enter your Username and Password under the Sign In page.
2. You will see a message pop up letting you know your original class has expired.
Enter your new Class Code in the box below the message.
3. Confirm the name of the class is correct and select the "Yes! Enroll me in this Class"
button.
If you have any difficulty setting up your account please contact WRDS Support
at: https://wrds-www.wharton.upenn.edu/contact-support/. When opening your support
ticket you must use the email associated with your existing WRDS account.
---- WRDS registration information ----
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