Monthly Archives: November 2014

Uber Valuation

Preliminary Models, Working Draft

In his analysis on Uber’s valuationAswath Damodaran develops a TAM model which defines Uber’s total addressable market as the yellow cab industry. The two primary levers affecting Uber’s valuation include the following:

  1. Potential Market Size
  2. Target Market

Aswath’s Model (also recreated here):


A primary limiting factor of his model is such that Uber’s potential market does not only include the taxi industry.

I take a population based approach to developing a valuation framework for Uber. First, I logged each city which Uber has a presence (from their website). Then, I recorded the population for each of the 225 cities Uber has a presence. The total potential market for Uber to access (so far) is 650MM people. Based on their financials leaked about a year ago, Uber has about 400K active users at a time. This represents less than a tenth of a percentage market penetration (or, 0.068%).

Uber Leaked Financials + Preliminary Analysis


I derived key drivers from Uber’s leaked financials that are fundamental aspects of the new model I propose which include

  • Completed rides per active client
  • Revenue per ride

Preliminary Valuation Model – Revenue Build


Key Considerations:

  • I assume that Uber will be able to reach .712% market penetration by 2024; which implies 7.4mm active clients of a 1bn person addressable market (growing from 400K active users of a 650mm addressable market 1 year ago)
  • Completed Requests per Active Client – users will continue to access the app with greater frequency as ease of access improves (shorter wait time due to greater supply). Additionally, 96 requests per active client per year is based on the assumption that a user requests 2 rides per week (annualized). This value will need to be modified to consider 1. users that share rides with friends 2. A straight line 52x multiplier is not sufficient to explain usage trends
  • Revenue per Ride – drivers will begin to earn greater revenue per ride due to increased operational efficiency (less stop time with greater usage)

Crunchbase – Tool

Crunchbase Project

About a month ago, I assembled a little sheet that allows a user to navigate companies within the Crunchbase open source data set, which provides information about startup activity on companies in various industries.

While sources like Indeed do a good job of aggregating job postings at a single point in time, it paints an incomplete picture of the number of job vacancies that could be open at that point in time. I wanted to address another issue – what companies within different industries resided in what regions? Hiring needs at different companies change on a rather random basis, so I thought it would be helpful to help my friends create a “watch list” of companies within a specific industry so that they could

  • Discover Companies within specific industries
  • Explore what Companies are doing within different industries (links to websites are included in the sheet)
  • Locate Companies in varying regions

I also thought it would be beneficial for my friends because I’ve learned that a company’s funding history (date since last funding round + amount raised), can be indicative of a company’s hiring strategy. Tools like LinkedIn are great means by which to expand one’s professional network. Helping friends discover and explore different companies is one step closer to helping them connect with future coworkers/ companies.

I started to play with the data, and I started to work on scaling the project to include the entire dataset, but got bogged down with obligations for work. So so far, the dataset includes only Companies located in California. My sheet features two separate search fields that enable a user to

  1. Search for Companies by Industry
  2. Search for Companies by Region

The sheet autopopulates this data based on the search criteria listed above.The file is available for download if you click here (2mb XLS file). I’ll make periodic updates, but am making it available for now.


The primary focus of the dataset is to highlight companies by industry and region. In cell F6 in tab titled “menu,” companies are listed in order of frequency they occur in the database. The top 25 industries by representation in the database are as follows:


There are a total of 517 different industries represented in the dataset. To view a full list, click here.

User Interface


Two primary cells control the interface of the sheet, each via dropdown menu:

  • Cell F6 Controls Industry Navigation
  • Cell N7 Controls Region Navigation

Left Side Pane: Industry Summary

  • Number of Companies by Region
  • Median Funding
  • Average Funding

Middle Pane: Funding League Table

  • Top 20 Companies within each Industry Ranked by Amount Funded
  • Date of Last Funding Round
  • Link to Company Website


Right Pane: All Companies within Industry in Chosen Region

  • Company Website
  • Total Funding
  • Status (Acquired, Operating)
  • Headquarters
  • Number of Funding Rounds
  • Year Founded
  • First Funding Date
  • Latest Funding Date

McClatchy (MNI) Earnings

The McClatchy Company

Balance Sheet 

  • Sold 25.6% interest in Classified Ventures to Gannet; resulted in proceeds of $631.8MM; expect after tax proceeds to be around $406MM
  • Pro Forma cash including after-tax proceeds from transaction is $596MM (225.1MM pre)
  • Provide Co. with the liquidity to execute on digital transformation, reduce debt, and fund other corporate uses


  • Poor print retail environment performance, substantial decline in national advertising
  • Growth in revenues in digital advertising and audience revenues; digital only revenues grew 9.1% excluding the impact of the sales of
  • Non-print advertising accounted for 64% of revenue and grew by 0.5%

McClatchy Capitalization and Operating Trends

Financial Summary

  • LTM Revenue: $1.25bn
  • LTM EBITDA: $243mm (19.8% margin)
  • LTM FCF: $92mm (6% of total debt)
  • Total Debt: $1.56bn
  • LTM net total leverage: 5.3x
  • Total Liquidity: $300mm; $265mm cash on hand


Market Comparables


Revenue and EBITDA Margin


Follow Up

  • Credit Profile Over Time
    • Debt Payoff Projections
    • Total Debt / EBITDA Projections

EDGAR – Raw Data

Preliminary Analysis

Log Part 1 – Normal Distribution Follow Up


  1. Define “significant” form 4 filings and determine if there is a relationship between the number of Form 4 filings (on a daily basis) and price impact
  2. Observe top 3 gains + losses days and count proximity from Form 4 Filing
  3. Eliminate confounded companies (poor earnings; negative media attention)
  4. Adjust for price changes relative to SP500

Log Part 2 – Qualitative Analysis, “Tails”


  1. Frequency of media coverage relative to filing
  2. Catalysts for filing


EDGAR – Volume

Introducing volume as a parameter of analysis to define significant insider activity

Form 4 is filed

Calculate % trading volume exceeds average daily trading volume (ADTV )


  • Rank trading volume excess and observe vs
    • Price Reaction