Financials: Interactive Database for Public Company

Posted on Nov 2, 2019
The skills I demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.


There is a scarcity of free quality financial data accessible to the average investor. Most large financial firms are equipped with expensive software (i.e. S&P Capital IQ, Factset, Bloomberg) and large research teams to carry out analysis on company financials.

Given the stock market is an environment where professionals and average investors can play together, average investors should be able to access inexpensive quality data and level the playing field to where there is less information asymmetry.  The following application aims to provide an interactive database of public company financials to observe trends for a specific company overtime and compare with its respective industry metrics. 

Shiny App can be found here :

The Application

I built this application in order to better visualize company financials and metrics over time. You will be able to see quarterly and annual data since the company’s inception as well as the cumulative return of a dollar invested since inception.

After assessing the company’s fundamentals, it will be interesting to compare industry metrics and see how whether your company is undervalued or overvalued, underachieving or overachieving in their respective industry average metrics.

Apple’s annual return on equity from 1996 -2008

Amazon’s quarterly cash from operating activities from 1997-2019.

$1 invested in Apple stock from 1995 to today would have return 128x your original investment.


Average net margin of industries of the 765 companies in the dataset. Real Estate and (Banking, Asset Management, Financial Companies/Services) have highest margins followed by pharmaceuticals/medical devices. Lower margin industries have higher risk but will probably already be reflected in the stock price.

The Data

The data was collected/scraped from from 765 company financials. Most companies are larger companies (i.e. S&P 100) as well as some mid-capitalization companies. This is a static dataset that contains information from the latest quarter.

Future Work

This app has much more potential for future work. There could be a tab for portfolio summary based on the companies you want to select for your portfolio. Additionally, I wanted to create a company by company comparison on their metrics as well as their respective industry metrics to come up with a scoring algorithm based on their rankings.

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