Financial Statements Analysis
Python Project for Data Analysis
The goal of this Python project for data analysis is to build a simple valuation model for same sector stocks listed in the S&P 500 index. The program will have to read the second quarter 2022 public financial statements, calculate multiple ratios such us ROE, Book value or Quick ratio and yield an investment recommendation (“buy”,”hold”, “sell”, etc) based on a provided model. Although the project is focused on the Energy industry, the program must work with any other sector/equity.
The data required for the program is real: The official financial statements (10-Q. 10-K, 11-K, etc) all listed companies in the US must provide every quarter to the Securities Exchange Commission. Therefore, is an excellent exercise to learn and practice how to analyse, manipulate and automate spreadsheets with Python and Pandas. The figures necessary to calculate the ratios are available in different sheets of each Excel file (income statement, balance sheet, etc). You will have to select them among several tables and implement the defined formulas (instructions pdf) through functions, conditional statements and “for” loops. Once you have coded for one company it will have to run for all the companies at the same time.
Not all the data required to calculate the ratios is available in the spreadsheets, so you will have to combine another source of information in the program to get specific data like the current market price. It is therefore a fantastic challenge for Finance or MBA students willing to learn financial analysis using Python or any other profile interested in the stock market.
DIGITAL DOWNLOAD / CONTENT
You will receive an email with a ZIP file. The download is also available on your account.
The ZIP file includes:
1) INSTRUCTIONS: A PDF with the project description. It includes a short guidelines section, and a screenshot of the expected result so you can visualize what you have to build.
2) DATA: 17 public Excels files (available at sec.com) with the financial statements of 17 companies listed in the S&P 500.
- A “.py” file with the source code (290 lines). The code provided in the solutions has been written by a senior developer, so it is clean and easy to understand. A great way to learn and adopt right habits to create quality software.
- A PDF with detailed explanations of each chunk of code (30 pages). A Python project tutorial where the instructor explains step by step what the code does and why.
WHAT YOU WILL PRACTICE
Cheng Li is a quantitative developer based in Singapore. He has experience in the insurance industry and he currently works as freelancer.
This Python project for data analysis is highly recommended for intermediate level students who have attended Python courses and feel comfortable with core Python concepts like loops and functions definition. It is a great challenge to practice data wrangling and learn how to automate Excel with Python.
If you need additional information, do not hesitate to contact us.