Founder Friendly Fund Simulator

What returns might a founder friendly venture capital fund generate?

Written by Eisaiah Engel on June 24, 2019.

The book, Grays Sports Almanac for Venture Capital, proposes that a venture fund could combine optionality with the Founder Friendly Standard to travel in time, purchasing equity in companies at early day prices after their investment cases become robust.

This Microsoft Excel Model (uses macros), called GSAVC Fund Simulator version 1H 2019-6-24, is a step towards a financial model for such a fund. Six independently simulated variables drive the outcome of each iteration in the simulation.

This model differs from the fund in the book because it invests a fixed $10,000 each time and is missing the main feature described in the book: a warrant to buy more equity at early prices after the winning companies emerge.

The fund in this model is made up of microfunds that share 50% of their portfolios. To make the model as conservative as possible, portfolio company valuations do not go above $1B.


This document contains a list of definitions for the terms used in GSAVC Fund Simulator designed based on Grays Sports Almanac for Venture Capital.

Field color legend:

  • Blue are fields you can edit.
  • Green are controlled by the macro simulation.
  • Black are calculated with formulas.

Number of businesses that start each year in the USA

This is a count of the new businesses that start in the United States that have 1+ employees: 552,000.

…anywhere from 125 to 250 companies per year that are founded in the United States (out of roughly 552,000 new employer firms that open each year) reach $100 million in revenues in a reasonable timeframe.

Source: Kedrosky, P. (2013). The Constant: Companies That Matter. [ebook] Kansas City, MO: Ewing Marion Kauffman Foundation, p.5. Available at: https://www.kauffman.org/what-we-do/research/2013/05/the- constant-companies-that-matter [Accessed 14 May 2018].

Fundable businesses

This is a count of how many businesses in the USA are funded each year. The model assumes we purchase equity in companies that are fundable: 60,000 companies (the higher number in David S. Rose’s estimate below). This number narrows down the odds by assuming that Liquidity Scenarios 1, 2, and 3 only happen within this pool of 60,000 and that we will only invest in companies within this pool of 60,000.

In very general terms, in the US roughly 1,500 startups get funded by venture capitalists, and 50,000 by angel investors. VCs look at around 400 companies for every one in which they invest; angels look at 40… Looked at that way, of the 50,000-60,000 deals that get funded each year 30,000 of them should not have been funded (let alone the other few million who wanted funding)…so therefore there no really great people with really great ideas who go unfunded.

Source: Rose, David S. “How Many Start-Ups in the US Get Seed/VC Funding per Year?” Quora, 21 Apr. 2012, www.quora.com/How-many-start-ups-in-the-US-get-seed-VC-funding-per-year.

Liquidity Scenarios

This is a collection of the four different valuation scenarios we model at the moment of liquidity.

Liquidity Scenario 1 – Achieves 100M+ in yearly revenue

Divide 552,000 new US employer firms by 250 and 125 respectively to find the lower bounds of 2,208 and the upper bounds of 4,416 in the odds calculation.

…anywhere from 125 to 250 companies per year that are founded in the United States (out of roughly 552,000 new employer firms that open each year) reach $100 million in revenues in a reasonable timeframe.

Source: Kedrosky, P. (2013). The Constant: Companies That Matter. [ebook] Kansas City, MO: Ewing Marion Kauffman Foundation, p.5. Available at: https://www.kauffman.org/what-we-do/research/2013/05/the- constant-companies-that-matter [Accessed 14 May 2018].

Liquidity Scenario 2 – Achieves “High-Growth Company” status

To find the “1 in X Businesses that start” number used in the model, divide 100,000 by each metro area’s High-Growth Density metric. This is done on this spreadsheet and the data on the spreadsheet was taken from the metro area rankings of the Kauffman Index of Growth Entrepreneurship.

High-Growth Company Density represents the prevalence of fast-growing private companies that have at least $2 million in annual revenue and 20 percent annualized growth over a three-year period, which compounds to 72.8 percent after the three years.

For example, if the High-Growth Company Density for a metropolitan area were 306.7, it would mean that for every 100,000 employer businesses in that metro area, there were 306.7 high-growth firms.

Source: Morelix, Arnobio, and Josh Russell-Fritch. 2017 Kauffman Index of Growth Entrepreneurship. Ewing Marion Kauffman Foundation, pp. 10 www.kauffman.org/kauffman-index/reporting/-/media/e37f4200462347dbb0d385e01e656be2.ashx.

Using the example above, Washington DC has a High-Growth Company Density value of 306.7 in 100,000 companies or 1 out of every 326 companies. The top cities in the 90th percentile are Washington DC, Austin, Atlanta, Columbus, and Nashville. Check out the odds for each city here.

Liquidity Scenario 3 – Returns capital

We model capital being returned 25% of the time (often less than the full amount of principal invested):

About three-quarters of venture-backed firms in the U.S. don’t return investors’ capital, according to recent research by Shikhar Ghosh, a senior lecturer at Harvard Business School.

Source: Gage, Deborah. “The Venture Capital Secret: 3 Out of 4 Start-Ups Fail.” The Wall Street Journal, 20 Sept. 2012, www.wsj.com/articles/SB10000872396390443720204578004980476429190.

Liquidity Scenario 4 – Loses all value

This is a catch-all category that returns $0 in capital back.

Calculating investment terms

A collection of the inputs and calculations used to fill in the blanks for the investment terms.

Founders’ last full-time salaries

The model assumes that the company has no assets that can be used as collateral. To establish a value for the company, we start with the sum of all the founders’ last-known salaries when they had jobs (salary or contract labor). This is simulated as a linear variable between $50,000 and $250,000. These are the recommended minimum and maximum inputs when investing.

Last 12 months gross sales

Then, we add up the last 12 months of gross sales. Because we are using gross sales and not accounting for discounts, returns, or cost of goods sold, there is a cap of $500,000. This is simulated as a linear variable between $0 and $500,000.

Multiple to establish company valuation

Then, we multiply by 2X the sum of the Founders’ last full-time salaries and the Last 12 months gross sales to establish a valuation for the company.

Company Valuation

The company valuation is how much the company is worth at the time of investment.

Fully Diluted Shares Outstanding

Our model assumes the company has a total of 10M shares and that all dilutive effects are accounted for including issued and outstanding options, promised options, and unissued option pool.

Share Price

This establishes the price per share.

Purchase Amount

This is the total amount invested. This is fixed at $10,000 in all scenarios in our model.  

Shares Purchased

These are the total number of shares in the company purchased for $10,000.

Minimum Liquidity Valuation

This number determines how much the company is worth at the time of Investment. This is a random variable that is normally distributed with the mean at the Company Valuation for the investment round and standard deviation of 20%.

Valuation outcomes

The model simulates these valuation scenarios.

Valuation Multiple (of Revenue)

This is used when Column C has a value of True, which happens when liquidity scenarios 1 or 2 are chosen. A RevMultiple will be chosen from the Percentile, RevMultiple, Revenue, and Age table. The source of this data is this table, which was derived from this data.

Table: Revenue Multiples Per Industry

This is a simulated random variable that is distributed with the probabilities from Liquidity Scenario 2.

Liquidity Scenario 1 – Achieves 100M+ in yearly revenue

If the model choses scenario 1, the revenue is a constant $100M, even though the Kedrosky research says it can be more than $100M. The $100M will be multiplied by the Valuation multiple of revenue to establish a valuation.

Liquidity Scenario 2 – Achieves “High Growth Company” status

If the model choses scenario 2, it mixes and matches the RevMultiple, Revenue, and Age from this table. Although these numbers are visualized together in rows, the model chooses RevMultiple and Revenue separately. For example, the model can choose a multiple of 5x and revenue of $3,000,000. The age is always associated with the revenue number in the table below.

PercentileRevMultipleRevenueAge
0%
1.0 x1,000,0001 Year
10%1.2 x3,000,0001 Year
25%1.3 x5,000,0002 Years
50%1.9 x10,000,0005 Years
75%2.4 x26,000,00010 Years
90%3.0 x65,000,00016 Years
100%5.0 x100,000,00016 Years

Source: Combination of Tables 1, 2, and 3 from this spreadsheet.

Liquidity Scenario 3 – Returns capital

If the model choses scenario 3, the Minimum Liquidity Valuation is chosen. 

Liquidity Probability – randomly generated

This is a simulated variable distributed based on odds defined in Liquidity Scenarios above.

Valuation at Liquidity

This field is a copy of the valuation in the chosen Liquidity scenario. 

Timing – No of Years to Liquidity

If the model chooses Liquidity Scenario is 1 or 2, then the number of years is chosen from the age field in the Percentile, RevMultiple, Revenue, and Age table. This is always associated with the revenue field from the table in the link above. If the Liquidity Scenario is 3, then this value is three years. 

Investment Cash Flow

This collection of fields calculates the return generated by the investment.

Sale Price Per Share

This is the company’s valuation at the time of liquidity divided by the total number of fully diluted shares outstanding (always assumed to be 10M shares).

Sale Price

This is the company’s share price multiplied by Shares Purchased

Net Gain (Loss)

This is the difference of the Sale Price minus the Purchase Amount.

Limit of Liability/Disclaimer of Warranty: Eisaiah Engel (“the author”) is not providing any financial, economic, legal, accounting, or tax advice or recommendations in this post. The author is not a lawyer. The information contained in this post was prepared for general information purposes only, does not constitute research, advice, or a recommendation from the author to the reader and is not a substitute for personalized financial or legal advice. Neither the author nor any of his affiliates make any representation or warranty as to the accuracy or completeness of the statements contained in this post. The author and his affiliates expressly disclaim any liability (including any direct, indirect, or consequential loss or damages) for all posts and their content.