Analyzing publicly traded firms

Course Project: Analyzing publically traded firms

The objective of this project is to utilize publically disclosures (i.e., annual reports) and financial data to analyze a group of firms in an industry (from a list of industries provided) and provide an investment recommendation based on valuation analysis. The analysis is up to 8 pages excluding the reference section; if you use any sources other than annual reports, please cite them and report the references in the reference section. Before your group start, please select an industry from the excel list and choose 3 largest companies in terms of sales in the fiscal year 2020 in the industry [only up to two groups are allowed to choose the same industry. If there are more two groups who want to select the same industry, the first two groups who inform the teaching assistant (Ms. Jingjing Huang, email: will be chosen].


The report must include the following parts:

  1. Analyze the industry including competition, business models, industry regulations, demands and supplies, industry trend etc.
  2. Please use python programs to download relevant financial data (annual financial statement data up to the most recent year; you may use stock price data up to the fiscal year ended date to estimate costs of capital) based on the sources we learned in AF5365 and estimate the values of the 3 largest companies in the industry by using the discounted cash flow model and ROPI model (if you think that these two models are not suitable to firms you analyzed, you can provide an explanation and use the market-based valuation models). Report your valuation analysis with sensitivity checks (the valuation date is assumed to be the most recent fiscal year ended date among the companies you analyzed). You can assume the risk premium at a 5%, and use the current 10-year US treasury yield as the risk free rate; you need to have sensitivity checks with your assumption.
  3. You may consider using the past industry sales trend and the related ratios as the starting points to make your forecasts (4-5 periods should be acceptable). However, please analyze MD&A reports in the most recent 10-Ks of these companies by using natural language processing (NLP) tools and adjust your estimation for forecasted sales and expenses etc. (You must report your forecasted sales, NOPAT, NOA, costs of capital, and terminal value etc. and the summary of NLP results).
  4. Recommend a company for potential investors with explanations. The recommended company’s strategy and business models must be discussed.
  5. Submit your python codes separately.


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