avaialable from (https://rpubs.com/staszkiewicz/FM_OE)
Format: liknk to published rpubs
Form of transfer: only through Niezbednik, section send letter to leading - please past and copy link to published page
Team: a maximum of five people, each person from a different country. On the front of the report: the names of the authors with an indication of the sections of which they were the main performers.
Statements
Authors’ statement of non-infringement of others’ copyrights:
Authors’ statement as to how the AI tool was used during the assignment.
Authors’ statements on the use of the task under a public access license.
Deadline for submission of the project:
| Date | Activity | Main output |
| 5 Oct | Case launch | Team allocation and case briefing |
| 12 Oct | IPO structure, prospectus and information requirements | Research plan |
| 19 Oct | Business model, equity story and due diligence | Team development |
| 26 Oct | PHASE 1 | IPO Strategy Presentations |
| 2 Nov | Financial analysis and forecasting | Forecast framework |
| 9 Nov | DCF and comparable-company valuation | Preliminary valuation |
| 16 Nov | Risk, governance and due diligence | Risk register |
| 23 Nov | IPO structure and pricing | Preliminary IPO proposal |
| 30 Nov | PHASE 2 | Executive Summaries |
| 7 Dec | Cross-team valuation challenge | Challenge session |
| 14 Dec | Final report preparation | Drafting |
| 21 Dec | FINAL REPORT SUBMISSION TO TEAMS | Final reports uploaded |
| 28 Dec | Independent peer review | Review of opposing report |
| 4 Jan | PEER DUE DILIGENCE | Written questions + answers |
| 11 Jan | FINAL PRESENTATIONS | Final defence |
| 18 Jan | IPO DAY | Market outcome + final scoring |
Beginning of the school term: assume October 1, 2026
End of the school term: assume January 30, 2026
Length of the study no more than: 24,000 words
Data: All codes and data embedded in the document ie. , that for each standalone code execution section, the data must either be downloaded from a public repository or generated inside the task. If you have any questions, please consult during class.
Projects, given after the deadline, are not graded, and have a status of “failed”.
Plase use the ‘code chunck’ to show your workings in R.
Author1 Name, country
Author2 Name, country
Author3 Name, country
Date of the project:
Date of the submission:
Date of the revision:
Date of acceptance:
OpenAI IPO: Valuation, Due Diligence and Market Pricing
Course: Financial Markets
Semester: 5 October 2026 – 18 January 2027
Class meetings: Mondays
Number of students: 15
Teams: 2
Maximum score: 50 points
AI Use Policy for the Semester Case
A1. General principle
The use of Artificial Intelligence (AI) tools is permitted and encouraged where it supports learning, analysis, research, writing, data processing, or other legitimate work on the semester case. However, students remain fully responsible for the content, accuracy, originality, interpretation, calculations, and conclusions of their work.
AI must be treated as a supporting tool rather than as an undisclosed author of the submitted work. The purpose of this policy is not to prohibit AI use, but to ensure transparency, individual accountability, and academic integrity.
A2. Registration of AI use
Each student must register their use of AI in the designated AI-use database. The registration must indicate, at a minimum:
which AI tools were used;
the approximate extent of their use;
the purpose for which AI was used (e.g. brainstorming, literature search, data analysis, coding, translation, editing, drafting, checking, or other purposes);
the student’s assessment of the extent to which AI contributed to the submitted work.
The detailed format and access procedure for the database will be provided during class.
A3. AI disclosure in the submitted report
Every submitted report must contain an AI disclosure based on the CRediT-style contribution principle. The disclosure must identify:
the AI tools used;
the purpose for which each tool was used;
the specific contribution made by the tool;
the extent to which the student or team relied on the output;
how the AI-generated or AI-assisted material was subsequently verified or modified.
Students must also retain and provide a log of AI interactions/prompts relevant to the submitted work. The log should make it possible to understand how AI was used and how its outputs contributed to the final report.
AI-generated content must not be presented as independently produced student analysis without disclosure.
A4. Verification and reverse engineering
The teaching team reserves the right to verify the declared use of AI by conducting a reverse-engineering or consistency analysis of submitted work.
For this purpose, a sample corresponding to approximately 10% of the submitted report may be selected either randomly or purposively. The selected material may be examined using appropriate analytical methods, including reconstruction of the likely AI-assisted production process and comparison with the student’s declared AI-use record.
If the analysis establishes a consistency level above 80% with AI-generated material or an AI-generation process, the submitted work will be treated as non-compliant with this policy and the case submission will be disqualified.
The 80% threshold applies to the verification procedure and does not mean that ordinary, properly disclosed use of AI is prohibited. The decisive requirement is transparent declaration and the ability to account for the submitted work.
A5. Authorship and individual accountability
The report must list all students who contributed to the case as authors.
A student who contributed to the work but is not identified as an author cannot subsequently claim individual credit for the submitted case. No individual points for the case will be awarded to a student whose authorship is not identified in the submitted report.
Listing a student as an author implies that the student accepts responsibility for their contribution and is able to explain and defend it.
A6. Finality of submission
Once the report has been submitted through the designated system, no corrections, replacements, additions, or other modifications are permitted.
The version submitted through the system is the version that will be assessed. Students should therefore verify the completeness of the report, authorship information, AI disclosure, supporting materials, and required attachments before final submission.
A7. Individual submission
In addition to the team submission, each student must submit the complete final report and all required accompanying materials individually as their response to the semester case.
This requirement is intended to establish an individual record of participation and to ensure that every student is accountable for the work submitted under their name.
The detailed submission procedure, required attachments, AI-use database, and format of the AI interaction log will be explained during the relevant class.
A8. Core principle
The guiding principle is simple:
AI use is permitted. Undisclosed AI use is not.
Students are expected to use AI responsibly, document its use, verify its outputs, and remain able to explain and defend the analysis submitted under their name.
1. Case Concept
During the semester, students will simulate an IPO of OpenAI.
The class will be divided into two teams.
Team A — OpenAI / Owner Advisory Team
The team represents the owners and management of OpenAI and advises them on how to prepare and structure an IPO.
Team B — Investment Banking / Due Diligence Team
The team represents the investment banks considering whether and on what terms to underwrite the IPO.
The teams work independently during the first part of the semester. Later, each team receives access to the other team’s work and must critically review it.
This creates a two-sided capital-market process:
Issuer → Prospectus → Due diligence → Valuation → IPO pricing → Market outcome
The case is deliberately designed so that the two teams have different interests.
The issuer team seeks to maximise the value obtained from the IPO while maintaining a credible investment proposition.
The investment banking team seeks to establish a defensible valuation and pricing range and to identify risks that could make the proposed IPO difficult to execute.
2. The Three Phases
The semester case consists of three active phases.
Phase 1 — IPO Strategy
5 October – 26 October
Students develop their initial strategy and valuation approach.
Deliverable:
IPO Strategy Presentation
Phase 2 — Executive Summary and Preliminary Valuation
2 November – 30 November
Students develop their financial analysis, valuation and IPO structure.
Deliverable:
IPO Executive Summary
Phase 3 — Full IPO Report, Peer Due Diligence and IPO
7 December – 18 January
Students complete the final report, review the other team’s report, formulate written questions and defend their own analysis.
Deliverables:
Final IPO Report
Peer Due Diligence
Written Questions
Written Answers
Final Presentation
IPO Market Simulation
3. Revised Semester Timeline
| Date | Activity | Main output |
| 5 Oct | Case launch | Team allocation and case briefing |
| 12 Oct | IPO structure, prospectus and information requirements | Research plan |
| 19 Oct | Business model, equity story and due diligence | Team development |
| 26 Oct | PHASE 1 | IPO Strategy Presentations |
| 2 Nov | Financial analysis and forecasting | Forecast framework |
| 9 Nov | DCF and comparable-company valuation | Preliminary valuation |
| 16 Nov | Risk, governance and due diligence | Risk register |
| 23 Nov | IPO structure and pricing | Preliminary IPO proposal |
| 30 Nov | PHASE 2 | Executive Summaries |
| 7 Dec | Cross-team valuation challenge | Challenge session |
| 14 Dec | Final report preparation | Drafting |
| 21 Dec | FINAL REPORT SUBMISSION TO TEAMS | Final reports uploaded |
| 28 Dec | Independent peer review | Review of opposing report |
| 4 Jan | PEER DUE DILIGENCE | Written questions + answers |
| 11 Jan | FINAL PRESENTATIONS | Final defence |
| 18 Jan | IPO DAY | Market outcome + final scoring |
4. Phase 1 — IPO Strategy Presentation
26 October
Each team receives:
10 minutes presentation + 5 minutes questions
Team A — OpenAI / Owner Advisory Team
The team should explain:
1. Why should OpenAI consider an IPO?
2. What is the proposed equity story?
3. Who are the target investors?
4. What information should be disclosed?
5. What are the principal risks?
6. Which valuation methods will be used?
7. What financial information is required?
8. What is the preliminary valuation range?
9. What IPO structure is proposed?
The presentation must conclude with:
Preliminary valuation range: USD X–Y billion
Team B — Investment Banking / Due Diligence Team
The team should explain:
1. What information must be independently verified?
2. What are the principal business risks?
3. What are the key financial risks?
4. Which comparable companies are relevant?
5. Which valuation methods will be used?
6. Which assumptions require particular scrutiny?
7. What information should be disclosed in the prospectus?
8. What could cause the bank to reject the proposed valuation?
9. What is the preliminary valuation range?
The presentation must conclude with:
Preliminary underwriting valuation range: USD X–Y billion
5. Phase 2 — Executive Summary
30 November
Each team submits a maximum 7-page Executive Summary, excluding appendices.
The document should contain:
1. Investment proposition
2. Business model
3. Market and competitive environment
4. Financial outlook
5. Capital requirements
6. Key risks
7. Valuation
8. Sensitivity analysis
9. Proposed IPO structure
10. Proposed IPO price/range
The valuation must include at least:
DCF;
comparable-company analysis;
scenario or sensitivity analysis.
6. Phase 3 — Final IPO Report
The final report is the principal analytical deliverable.
Team A
OpenAI IPO Prospectus & Valuation Report
The report should cover:
1. Executive Summary
2. Company and Business Model
3. Market and Competition
4. Equity Story
5. Revenue Model
6. Historical and Forecast Financials
7. Capital Requirements
8. Risk Factors
9. Governance
10. IPO Structure
11. Valuation
12. Sensitivity Analysis
13. Proposed IPO Price
14. Use of Proceeds
15. Investor Targeting
16. Key Prospectus Disclosures
17. Conclusion
Team B
OpenAI IPO Due Diligence & Underwriting Report
The report should cover:
1. Executive Summary
2. Business Due Diligence
3. Financial Due Diligence
4. Commercial Due Diligence
5. Technology and AI Risks
6. Regulatory and Legal Risks
7. Governance
8. Capital Requirements
9. Financial Forecast Challenge
10. Comparable Companies
11. DCF Valuation
12. Scenario Analysis
13. IPO Pricing
14. Underwriting Risks
15. Required Prospectus Disclosures
16. Underwriting Recommendation
17. Conclusion
7. Early Report Submission and Teams Repository
The final reports must be submitted to the common Microsoft Teams course repository by 21 December.
The reports will be made available to both teams simultaneously.
The purpose of the early submission is important.
Students must not treat the final report as the end of the exercise.
Instead:
One team’s report becomes the due-diligence material for the other team.
Each team must therefore read the opposing report as if it were reviewing a real IPO transaction.
8. Role Reversal — Peer Due Diligence
After the reports are uploaded, the teams temporarily change perspective.
Team A becomes the reviewer
Team A reads Team B’s investment banking report as if it were reviewing the work of an external adviser/auditor.
Team A should ask:
Are the assumptions supported?
Are the calculations internally consistent?
Are the comparables appropriate?
Is the DCF logically constructed?
Are risks adequately identified?
Are conclusions supported by evidence?
Are there contradictions between the narrative and the valuation?
Is important information missing?
Team B becomes the reviewer
Team B reads Team A’s IPO report as if it were conducting due diligence on the issuer.
Team B should ask:
Are the financial forecasts credible?
Are revenue assumptions supported?
Are margins justified?
Are capital requirements adequately considered?
Are risks properly disclosed?
Are valuation assumptions consistent with the business model?
Are the proposed comparables appropriate?
Are there inconsistencies between the prospectus narrative and the financial model?
What information would an investment bank require before underwriting the transaction?
The reviewer is not expected to rewrite the other team’s report.
The task is to identify material weaknesses.
9. Written Questions to the Opposing Team
Each team must prepare a set of written questions based specifically on the opposing team’s report.
The questions must be submitted before the opposing team receives them.
Each question must:
1. identify a specific statement, assumption, calculation or conclusion;
2. explain why it may be problematic;
3. ask for clarification, evidence or correction.
Weak question:
Why is your valuation too high?
Strong question:
Your report assumes revenue growth of 45% for the next three years, while your comparable-company analysis uses substantially lower growth rates. Please explain the basis for the difference and indicate how the valuation changes if the forecast is aligned with the comparable-company growth range.
10. The Four-Point Challenge
The written question-and-answer stage is worth:
4 points
Each team receives 2 points for the quality of its questions and 2 points for the quality of its answers.
Question points
A team receives points for questions that:
identify a genuine analytical weakness;
refer precisely to the opposing report;
concern a material assumption;
require substantive financial reasoning;
cannot be answered merely by repeating a sentence from the report.
Answer points
The responding team receives points for answers that:
directly address the question;
provide evidence;
explain the assumption;
correct an error where necessary;
quantify the effect where possible.
If the responding team cannot adequately defend a material assumption, the questioning team may receive the relevant challenge point.
If the responding team provides a strong, evidence-based response, the responding team receives the corresponding point.
The purpose is not to reward aggressive questioning.
The purpose is to test whether the teams can:
identify, defend and revise financial-market analysis.
11. Suggested Four Questions
Each team should normally submit four substantive questions.
The instructor should select the strongest questions if more than four are submitted.
The questions should preferably cover different areas:
1. Financial forecast
2. Valuation
3. Risk
4. IPO structure / market assumptions
12. Final Presentation
11 January
Each team receives:
12 minutes presentation + 8 minutes questions
The presentation should not simply repeat the report.
It should answer:
What is our final recommendation and why should the market believe us?
The final presentation must clearly state:
valuation;
IPO price;
IPO structure;
principal assumptions;
principal risks;
what changed during the semester.
13. IPO Day
18 January
The final class is the simulated IPO.
Each team submits its final position confidentially.
Team A submits:
final valuation;
IPO price;
number of shares issued;
primary proceeds;
secondary proceeds.
Team B submits:
independent valuation;
recommended IPO price;
underwriting recommendation;
maximum acceptable valuation.
The instructor then determines the simulated market outcome using the pre-announced market benchmark and information set.
The purpose is to evaluate how effectively each team priced the transaction under uncertainty.
14. Assessment — 50 Points
Group Points — 30
| Activity | Points |
| Phase 1 — IPO Strategy Presentation | 6 |
| Phase 2 — Executive Summary | 7 |
| Final IPO Report | 7 |
| Final Presentation | 4 |
| IPO Day / Market Outcome | 2 |
| Peer Due Diligence & Written Challenge | 4 |
| Total Group | 30 |
The 4 points for peer due diligence are deliberately separated because the activity requires students to demonstrate that they can evaluate financial analysis produced by others, not merely produce their own analysis.
Individual Points — 20
| Activity | Points |
| Individual preparation and active participation | 6 |
| Individual IPO Analyst Log | 5 |
| Individual analytical contribution | 4 |
| Questions and cross-examination | 2 |
| Peer assessment | 3 |
| Total Individual | 20 |
Total
30 Group + 20 Individual = 50 points
15. Continuous Individual Assessment
Each student maintains an IPO Analyst Log throughout the semester.
Short entries should be submitted after major stages.
Each entry should answer:
1. What did I analyse?
2. What evidence did I find?
3. What assumption did I challenge?
4. What did I contribute to the team’s decision?
5. What did I learn from the opposing team?
6. Did my view of OpenAI’s valuation change?
The log should demonstrate an analytical process rather than simply describe activities.
16. Instructor Guide
The instructor should act as:
Transaction Coordinator and Market Regulator
The instructor should not provide the students with a “correct” valuation.
The aim is to create a realistic analytical environment in which the valuation emerges from competing assumptions, evidence and market expectations.
17. Weekly Teaching Pattern
A useful structure for most Monday sessions is:
10–15 min — Financial Markets concept
10 min — Case development / new information
15–25 min — Team work
10 min — Cross-team challenge
5 min — Instructor debrief
The exact timing can be adapted to the rest of the course.
18. Instructor Intervention — Phase 1
During the first three weeks, the instructor should prevent students from moving too quickly to a DCF.
Ask:
What information do you need before forecasting?
What exactly generates OpenAI’s revenue?
Which costs are economically variable?
Which risks affect cash flows?
Which risks affect the discount rate?
What would a public-market investor want to know?
The objective is:
Information → assumptions → forecasts → valuation
rather than:
Excel → valuation → justification
19. Instructor Intervention — Phase 2
Challenge the teams with questions such as:
What evidence supports this growth rate?
Why is this company an appropriate comparable?
Why should this risk affect WACC rather than cash flows?
What happens if your terminal growth rate falls by one percentage point?
What happens if margins are five percentage points lower?
Which assumption has the largest effect on valuation?
The instructor should reward students who identify uncertainty rather than students who simply defend their original position.
20. 21 December — Report Exchange
This is a major transition point.
The instructor should ensure that:
1. both reports are uploaded to Teams;
2. both teams receive access at approximately the same time;
3. neither team has access to the other team’s report before submission;
4. the deadline is strictly enforced.
From this point onward, students are no longer only analysts.
They become:
reviewers of the opposing team’s financial analysis.
21. 28 December – 4 January — Peer Due Diligence
The instructor should not tell students which weaknesses to identify.
Instead, provide the following instruction:
“Read the opposing team’s report as if you were responsible for signing off on the quality of the analysis before an IPO transaction could proceed.”
Students should distinguish between:
minor weakness
and
material weakness.
A material weakness is one that could reasonably affect:
valuation;
IPO pricing;
investor understanding;
financial forecasts;
risk assessment;
underwriting decision.
22. Instructor Evaluation of the Questions
A good question should be:
Specific + Evidence-based + Material + Analytical
A poor question is:
General + subjective + unsupported + non-material
For example:
Poor:
“Why do you think OpenAI is worth so much?”
Strong:
“Your DCF assumes a terminal growth rate of 5%. Given the long-term growth assumptions used in your forecast and the maturity of the underlying market, please explain the basis for this rate and quantify its effect on equity value.”
23. Instructor Evaluation of the Answers
A strong answer should:
1. recognise the exact issue;
2. refer to evidence;
3. explain the underlying assumption;
4. quantify the impact where possible;
5. acknowledge limitations.
Students should not lose points merely because their original assumption was challenged.
They should lose points when they:
avoid the question;
contradict their own report;
cannot support an important assumption;
ignore material evidence;
make an unsupported assertion.
24. Important Pedagogical Principle
The case should reward revision of judgement, not stubborn defence of the initial position.
A student may say:
“Our original assumption was too optimistic. After reviewing the opposing team’s analysis, we reduced our revenue growth assumption from 40% to 32%.”
That can be a sign of strong analytical work.
The important question is:
Why did you change your mind?
25. Final Debrief
After the IPO simulation, the instructor should compare:
1. Team A’s initial valuation;
2. Team B’s initial valuation;
3. Phase 2 valuations;
4. final valuations;
5. IPO price;
6. simulated market value;
7. principal valuation errors.
The final discussion should focus on:
Which assumptions mattered most?
Which information was missing?
What did the opposing team identify that you had missed?
Did the IPO price reflect the information available at the time?
What is the role of an investment bank in reducing information asymmetry?
The final conceptual takeaway should be:
Financial markets do not eliminate uncertainty. They create mechanisms through which information, valuation, risk and capital interact.
26. Final Case Logic
The entire semester follows one continuous process:
Research
↓
Form a valuation view
↓
Defend the valuation
↓
Develop the IPO
↓
Publish the report
↓
Review the opposing team’s report
↓
Ask difficult questions
↓
Defend or revise assumptions
↓
Set the final IPO price
↓
Test the price against the simulated market
↓
Evaluate what was learned