MPAcc Fall Common Final Project

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.

• The audit appendix should be brief and include only the most critical financial reporting risks that would affect your valuation assumptions for each company.
• Remember that the risks on the long side should be discussed differently to the risks on the short side. For example, if your short selection has elevated misstatement risk, or concerns with revenue recognition, these risks are positive from a short perspective as they could trigger future price declines.

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

• There are many ways to present this information, including dashboards, excel files, and other visualizations. The goal is to clearly document your data sources, definitions, and outputs so that a third party could understand how you used data analytics to support your analysis.
• At a minimum, understanding the financial data used as an input into your valuation is important in this section. For example, if you used XBRL, then you need to explain how you aggregated various line items. If you use the data aggregated by eVal, document and explain any changes or corrections your team undertook.

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

• Teams can organize how they share presentation duties (including how the data analytics is used) how they feel best suits their team's strengths.
• Combinations of slides and dashboards are encouraged to help communicate your analysis effectively. This is not a requirement, however, as long as the analysis is communicated effectively.
• The bulk of the presentation should be aimed at covering the main talking points about the FSA project, including the specific recommendations and assumptions and integrated into this discussion is the use of data analytics to improve the valuation estimates.
• The talking points will vary per group depending on the firms covered and the focus of the FSA. Below are a various ideas on material that your group could cover, this list is not comprehensive, and it shouldn't be considered as what has to be covered as we are subject to time constraints and I don't expect them to be relevant to all groups.
• Discussion of key findings from your financial statement analysis that support your forecast and valuation assumptions. For example, did you identify any quality of earnings issues that affected your revenue or expense forecasts?
• Any applicable ratio analysis that supports your forecast and valuation assumptions. For example, did you use peer analysis to benchmark your key ratios and growth rates?
• Discussion of key forecast assumptions and data used to improve them. For example, did you use disaggregated revenue streams to improve your revenue forecasts? Did you use macroeconomic data to improve your growth rate estimates?
• A briefer discussion of some of the key valuation assumptions, discount rates and growth rates and any scenario or sensitivity analysis.
• Discussion of any additional analyses performed to validate or challenge your valuation assumptions. For example, did you use textual analytics for any elements of the project?