ACCTG 522 Assessment

MPAcc Fall Common Final Project

Team Deliverable (40%)

Assessment Overview

The Common Final Project which is a team based presentation focusing on the use of real-time data to support financial statement analysis for the initiation of a pairs trading strategy (one long position and one short position) for two chosen public companies. Teams select how they will narrow their analysis to two firms through the use of a screening analysis and other preliminary analysis. Teams will present in the Thursday MPAcc classes in the final week of the course. All teams are required to attend all presentations on both days. More details can be found at <a href="https://www.ashercurtis.me/teaching/mpacc/common_fall_project.htm/">the MPAcc Common Final Project page</a>.

Required Deliverables

Deliverable Due Date Canvas Submission Portal
All Final Project Materials (zip file) TBD - Coming soon Upload to Canvas (one submission per team)
Final Project Presentations Thursday, Dec. 4th No Canvas Submission Required

Deliverable Details and Hints

Further details are provided below for each required deliverable.

Required deliverable: A written report no longer than 25 pages including citations and tables/charts, audit and ESG appendices. Include links or exhibits for dashboards if needed. Submit as a pdf file. This document should include the following analysis:

  • A Description of the screening process & factors used to identify target firms (attach files/tables/input/output as appendix)
  • A brief summary of the company/industry backgrounds & competitive landscape
  • A detailed report of the Financial Statement Analysis used to support your recommendations and valuation assumptions (including quality of earnings, cash flow analysis, and key ratios)
  • A detailed description of your forecast assumptions (including descriptions of the processes and analytical tools used)
  • A discussion of the key valuation model assumptions (using a residual income model)
  • A discussion and support of the recommendation: investment thesis, catalysts, risks to each position, and potential returns
  • Appendices covering the material discussed below

  • Coming Soon

Required deliverable: A set of Appendices included in the initiation report covering the following topics:

  • Audit Appendix (1 page max): Identify and describe the Top 2–3 financial reporting risks tied to valuation drivers (e.g., revenue recognition, impairment), for each of these financial reporting risks outline what tests/assurance evidence would reduce estimation risk (one paragraph each).
  • ESG Appendix (3 pages max): Identify two material ESG areas likely to affect cash flows and/or cost of capital at one of the two companies. Estimate their impact on earnings and/or cost of capital. Identify the key metric for these critical ESG factors. Briefly detail a high level audit plan for how you would provide assurance over these metrics. Include the audit assertions and risks your audit plan/procedures would address.

  • Coming Soon

Required deliverable: An appendix or set of separate files (Excel, PowerBI, Tableau, etc.) that includes the following:

  • Support of your screening process (including data sources, factor definitions, and output) if used to identify target firms
  • Support of your forecasting assumptions and process (including data sources, variable and model definitions, and output) if used to develop your forecasts
  • Support of your scenario analysis and valuation assumptions (including data sources, tying of assumptions to parameters, and output) if used to develop valuation scenario analysis

  • Coming Soon

Required deliverable: An excel or other file that includes your residual income valuation model that includes the following:

  • Clearly labeled accounts and assumptions that tie to your initiation report

  • Coming Soon

Required deliverable: A final presentation delivered to the class and supporting materials (e.g., powerpoint and dashboard materials if applicable) that includes the following:

  • A clear discussion of your investment thesis and recommendation
  • Discussions of key findings from your financial statement analysis that support your forecast and valuation assumptions
  • Discussions of key findings from your data analytics that validate and/or challenge your forecast and valuation assumptions

  • Coming Soon

Generative AI Policy

This policy outlines expectations for the responsible and ethical use of generative AI technologies, including large language models (LLMs) such as ChatGPT, in this course. These tools can significantly enhance learning, productivity, and creativity–but must be used transparently and professionally to support a respectful and effective learning environment.

Permitted Use:

Generative AI may be used to assist with idea generation, research, document drafting, programming, editing, and other academic work, provided the output is critically reviewed, refined, and understood by the student or team. Use of AI is encouraged when it enhances the learning process.

Student Responsibility:

Students are responsible for the accuracy, relevance, and integrity of any work submitted, including content influenced or generated by AI tools. Errors introduced by generative AI–factual, analytical, or interpretive–will be treated as student errors and may result in reduced grades.

Disclosure & Ethics:

Students may be asked to disclose when and how they used generative AI tools in individual or team assignments. In cases where the use of AI significantly contributes to the submission (e.g., coding assistance, text drafting), students should include a brief statement describing the use.

Unacceptable Use:

Submitting AI–generated content without understanding it, using AI to bypass individual learning (e.g., for comprehension–based quizzes or in–class polls), or allowing AI to make up sources or misrepresent work is a violation of course expectations and academic integrity.

This policy may be updated as the role of AI in education continues to evolve.