ACCTG 522 Assessment

Team Preliminary Proposals

Team Deliverable (15%)

Assessment Overview

Teams will present their preliminary proposals for the joint ACCTG 521/522 Capstone Project in class 10/28. Teams will present only to the instructor, and are required to only attend their time slot.

Required Deliverables

Deliverable Due Date Canvas Submission Portal
Preliminary Proposal Memo 10/28 (8:30AM) Upload to Canvas (one submission per team)

Deliverable Details and Hints

Further details are provided below for each required deliverable.

Required deliverable: A written 1 page memo with an Appendix that provides expected project milestones and a meeting with the instructor to discuss project feasibility and expectations.The memo should consist of three sections, with two additional sections in the Appendix:

  • The objective of the project
  • An Overview of Expected Data Extraction
  • An Overview of the Analysis Planned
  • Appendix 1: Project timeline (with milestones)
  • Questions for the Instructor

  • You should use sub-headings to keep your memo organized around the required components. Additional subheadings may be required if you are planning on extracting data from many different sources and wish to provide additional structure to these different sources of data.
  • Do not exceed the single page requirement for the memo.
  • The key API data sources from the SEC's EDGAR are the Company Facts API and the Company Submissions API, se SEC API overview.
  • Data extracted from the Company Facts API is in the form of XBRL tags and dollar values for all accounts reported by the entity. The data is returned as a JSON file and will require deserialization and identification of correct time-periods for either quarterly or annual analysis. This file contains many years of data, and can be used to generate trends for various ratios and accounts.
  • Data extracted from the Company Submissions API is in the form of dates, filings and accession numbers for all filings made by the entity. They are returned in reverse chronological order (i.e., newest filings are first) and it is updated in real-time. The data is also returned as a JSON file and requires deserialization and matching of filings form-types (i.e., 10-K) and accession numbers to allow for the downloading of these forms in html. The class example was to return the most recent 10-K in html form, the use of the submissions API to return forms will require subsequent processing of the HTML filing.
  • Additional uses of the submissions API is to identify the types of filings made by the company and their frequency. For example, trends in Form-4 filings to examine patterns of in to identify trends in insider transactions (e.g., insider sales and purchases).
  • There are many other examples of real-time data that can be obtained using APIs. In general, most APIs require an API key or a way to access the data. Our in class example was using the AlphaVantage API to obtain stock return data.
  • At this stage the analysis expected to be performed can be at a high-level (i.e., ratio analysis, or sentiment analysis etc.), however, teams should try and narrow down their analysis to specific ratios by the project check-in meeting.

Generative AI Policy

This policy establishes the guidelines and expectations for the responsible and ethical use of generative AI, including Large Language Models (LLMs) in the graduate accounting programs. Generative AI can be a valuable tool for enhancing learning, productivity and creativity, but it must be used responsibly to maintain a productive and respectful learning environment. Generative AI should primarily be used for educational purposes, such as assisting with research, generating ideas, or editing documents. Students are responsible for the work that they submit, any errors introduced by the use of Generative AI will reduce the grades on individual or team submissions.