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What Drives Venture Capital?
A Perspective From Both Sides of the Table

Paul Vroomen

Agenda

Introduction

How Venture Capital Works

The Impact of Internal Rate of Return Expectations

Case Study: Sandbridge Technologies, Inc.

A $15M Powerpoint Presentation

The Future of Venture Capital

Big Changes Coming….

Thought Experiment

You are an entrepreneur….

You have worked for 10 to 12 hours per day, often 7 days a week for the past 3 years,
You have risked your entire personal savings,
You have endangered your marriage,
You see your kids mostly just before they fall asleep,
You have questioned your own sanity,

But, you finally have a working prototype….

The Entrepreneur’s View

What??

They want 10X return on their money?
They want 65% ownership of my company?
They want a controlling vote on the Board of Directors?
They want to be paid their money first before anyone else gets anything, even me, the founder, if we sell the company?

Vulture Capitalists!!

The Venture Capitalists View

This guy has a good idea , BUT:

He has no CEO track record and has never run a company before
His executive team has significant holes (especially in marketing)
The company’s business plan is way too optimistic, especially given that it has missed critical milestones, twice
They have one significant customer, but that customer is known for collaborating with innovative start-ups and then doing their own thing

I will ensure that our term sheet enables me to protect my capital and is structured so that I can direct the CEO to correct the issues with the company or replace him with someone that can if he does not!

The Primary Reason

The Entrepreneur, by definition, is an optimist

The Venture Capitalist, by experience, is a pessimist

The partnership of the two can work, sometimes spectacularly,

if each understands what is driving the other.

How Venture Capital Firms Work

Venture

Capital

Firm

Managed by

General Partners

Limited Partners

All Accredited

Investors

Investor 1

Investor 2

Investor n

$xM

$yM

$zM

Company 1

Company 2

Company k

Venture Fund

Committed Capital

$(x+y+…+z)

Private Equity

Portfolio

$aM

$bM

$uM

Base+(1-c).Surplus

c.Surplus

Liquidity Event

C = “Carry” = 20% - 35%

Mgt. Fee ~2%p.a.

EBO and VC Historical Performance

Source: US Venture Capital Index and Selected Benchmark Statistics, June 2014, Cambridge Associates, LLC

IRR: Net cash on cash returns to Limited Partners (after deduction of management fees and carry percentages)

AVERAGE IRR (1999-2009)

Electronics: -0.54%

Financial Services: 14.42%

BioTech: 16.04%

AVERAGE IRR (1999 -2009)

Information Technology: 24.23%

- Internet-Business: 23.2%

- Internet-Commerce: 37.8%

Implications of IRR Expectations

Required capital growth to achieve IRR:

To achieve 33% IRR, a $1 investment needs to grow to $7.40 in 7 years.

Time from investment:	5 Years	7 Years	10 Years
IRR: 25%	3.0X	4.8X	9.3X
IRR: 33%	4.2X	7.4X	17.3X
IRR: 50%	7.6X	17.1X	57.7X

Typical VC Fund Performance

Ten year VC fund that returned 3X net to limited partners from a portfolio of 20 companies:

Received proposals from >1,000 companies per year
Agreed to view presentations from 300+ companies per year
Performed Due Diligence on 30 companies per year
Invested in 3 - 4 companies per year (during first 5 years of fund)
10 companies were shut down within 3 years of initial investment
6 companies were acquired within 3-7 years and returned sufficient to recover capital
3 companies were acquired within 3-7 years and returned 1.5X to 5X
1 company returned >38X in 8 years (IPO - Home Run!)

Sandbridge Technologies, Inc.

Company/Team:

- Fabless semiconductor company based in White Plains, NY

- Founded in 2002 by 2 veteran IBM TJ Watson Research Center engineers

- Team of 55 experienced semiconductor and software engineers

Product:

- Multi-threaded, multi-CPU DSP chip for mobile phone baseband applications

- Automatically adapts to the protocol in which the phone was operating.

Customers:

- Multi-million dollar contractual partnership with one of the world’s top three cell

phone makers.

Intellectual Property:

- 25 granted patents and 20 pending or provisional patents

By late 2008, Sandbridge had consumed $53M in 3 VC funding rounds and needed a further $15M for the commercialization phase
The $15M Presentation…

Sandbridge Technologies, Inc.

But things change quickly! By late 2009:

The company’s primary customer and software partner had withdrawn from the partnership – it had quietly built its own chip in parallel with the partnership

The software team was behind schedule in delivering the broadband communications software stack; PC dongle and femtocell markets were well below forecast

The company was scrambling to identify and negotiate partnership agreements with software partners for cellular protocol software development

To generate short term revenue the company was negotiating license agreements with several interested parties.

The Board of Directors had decided that the company should focus on identifying potential M&A transactions

Sandbridge Technologies, Inc.

Conclusion

Company was sold for an aggregate of $55M in several transactions in late 2009 and early 2010.

All employees were hired by two licensees of the company’s technology

One licensee also acquired the right to deploy the original chip in electronic systems in China and is using it in “home gateway” applications

Management received a “carve out” from the proceeds of the sale

C Round Investors received a 1.6X return on their investment of $35M in 3 years, for an IRR of 16%; Venture Funds in the Electronics industry returned (4.9%) in 2010.

Everyone was happy.

Big Changes Are Coming to the Private Equity Investment World….

Security Token Offerings (STO) create a new asset class in private equity:
Crowd Funding (CF)

Why?

Crowd Funding Growing Rapidly

Source: coinschedule.com/stats

Crypto-token (ICO + STO) Crowd Funding

2015	2016	2017	2018 (to 9/21)
No. of ICOs/STOs	0	50	371	785
Amount Raised ($B)	-	0.098	6.24	20.033

Why ECF/ICO Growth?

REACH:
Online – investors and issuers worldwide can participate
Not limited to accredited investors – access to larger amount of capital
LIQUIDITY:
ECF smart contracts/ICO tokens can be bought & sold at will (for now)
JOBS Act mandates 1 year holding period
TRANSACTION COST
Standardized terms – reduces legal expense
Smart contracts – reduces investor management expense
Transaction fees lower than traditional financing cost
TIME TO MONEY
Financing campaign can be completed in days, even hours.

Private Equity Model Changing

Private

Equity

Firm

Managed by

General Partners

Limited Partners

All Accredited

Investors

Investor 1

Investor 2

Investor n

$xM

$yM

$zM

Company 1

Company 2

Company k

Venture Fund

Committed Capital

$(x+y+…+z)

Private Equity

Portfolio

$aM

$bM

$uM

Base+(1-c).Surplus

c.Surplus

Liquidity Event

C = “Carry” = 20% - 35%

Mgt. Fee ~2%p.a.

Dividends

ICO/ECF

Web

Platform

Investors

Accredited and

Non Accredited

Investors

Investor

Group 1

Investor

Group 2

Investor

Group n

$xM

$yM

$zM

Issuer 1

Issuer 2

Issuer k

Online Intermediary

Portfolio

$xM - TF

$yM - TF

$zM - TF

Base + Surplus

c.Surplus

Liquidity Event

c = “Intermediary Commission %”

Intelligent

Decision

Support

System

Transaction Fee

The New Private Equity Investment Model

The Problem

How to enable a large, diverse set of non-expert investors to identify investment opportunities that are likely to succeed, from a large, diverse set of privately held companies of different size, stage and quality?

A Solution

Create an Intelligent Decision Support System (IDSS) drawing on:
Private Equity Investment Practice
Finance Theory
Statistical Learning Algorithms & Processes
Decision Support Systems Engineering

Deploy the IDSS as a tool to support investors and issuers on a web based Crowd Funding Platform (CFP)

IDSS Overview

Can the enterprise yield an acceptable rate of return?

Does the enterprise have the team, market and products to credibly achieve the rate of return?

Revise/Reject

Yes

Stage 1

Evaluate

Stage 2

Verify

What is an “Acceptable Rate of Return”?

Apply:
Finance Theories
Modern portfolio theory
The theory of efficient markets
Statistics:
The central limit theorem
Historical IRR data for large datasets of: Equity Funds, VC Funds (Cambridge Associates), Angel Investments (AIPP)
To Determine:
The optimum risk-reward “frontier” for the Private Equity market
The efficient frontier
The target IRR for an efficient CF portfolio and consequently, individual CFF investments

Efficient Frontier: Equity Market

At 10% higher risk for ECF, the expected IRR target for an efficient portfolio of ECF assets is >= 28% with 99% confidence

CF Investment Risk

Angel Investments

are Risky….

1,137 AIPP exited angel investments:

IRR = -100%: 46.7%

-100%<IRR <= 0%: 20.1%

2 out of 3 angel investments lose money
1 out of 2 angel investments written off

CF Investments are riskier than Angel Investments:

Reduced Control – “micro” shareholders
Reduced Visibility – information asymmetry
Possible (Likely?) Regulatory Constraints – JOBS Act
Increased Moral Hazard

Implications

Bio-Sciences Start-Up

Accelerated Growth Plan

Seed Round ($0.5M in Q1)
2 VC rounds ($2.5M Q3, $5M Q6; 1X liquidation preference)
Target portfolio IRR is 35%; 45% productive => target investment IRR = 58%
Valuation: 12x rolling average of future 4 quarter cash flow

Demonstration: Stage 1

IDSS Overview

Can the enterprise yield an acceptable rate of return?

Does the enterprise have the team, market and products to credibly achieve the rate of return?

Revise/Reject

Yes

Stage 1

Evaluate

Stage 2

Verify

Classifier

Database

IDSS Functional overview

Issuer Business Plan

Calculate IRR

Industry

Database

Issuer Database:

4 +year revenue, cash flow forecast
20 attributes

Adjust Plan?

YES

Modified Business Plan

Classifier Attributes

Classify

Optimal Classifier

NO, Yellow

Modified Attributes

YES

Issuer

Database

YES

Industry Metrics

Adjust

Attributes?

Stage 1

Evaluate

Stage 2

Verify

Issuer

Database

Industry Database:

Public company valuation data
Industry TAM, ratios

Classifier Database:

Training and test set

Reject

Review

Feasible?

Reject

Green

Red,

Yellow

NO, Red

Statistical Learning Process

Attribute Selection

Create preliminary set based on domain knowledge (15 attributes)

Interview domain experts (3 VC General Partners, 3 CFOs (1 large, 2 small companies) and add if not identified in 1. (18 attributes)

Extract attributes from the research literature and add if not identified in 1, 2. (31 attributes)

Identify research papers from peer reviewed journals containing statistically significant correlation between attributes and success

Rank research papers by citation rate (citations/year)

Starting at highest ranked article extract attributes if not already identified in 1,2

Repeat until no new attributes are found

Analyze attributes to eliminate redundancy or correlation (21 attributes)

Attribute set

Attribute Quantification

Parameter	Description	Measure	Quantify
Team: EXP	Demonstrated leadership ability in the past	Measures number of years each team member has been in same position, or had equivalent title/responsibility in prior career as in new venture	Years in same position divided by total work experience of founding team, in years
Product: PPL	Level of product development planning detail	Measures the degree of thoroughness of the product/service development plan, specifically time granularity, staffing, critical path, dependencies and key milestones	At least weekly granularity =1; each task staffed =1; critical path identified=1; dependencies identified=1; key milestones identified=1; Score=sum of the above/5
Market: MGR	The target market enjoys a significant growth rate	Determines whether the Compound Annual Growth Rate (CAGR) of the Market that can be served with the product(s)/service(s) that generate plan revenue (SAM), lies within predefined thresholds	If CAGR>25% score =1; if CAGR>20% score =0.8; if CAGR>15% score =0.6; if CAGR>10% score =0.4; if CAGR>5% score=0.2; else score =0

Performance

Error Rate

Number of Attributes

Sample Size

Room for performance improvement with more data

At ~ 60 instances, synthetic model error rate approaches Bayes error within 5% (current dataset has 17 balanced instances)

Learning curves for kNN algorithm

(synthetic model, uncorrelated attributes)

Accuracy (true positives plus false yellows): 64%

Vroomen Capital, Inc. Proprietary & Confidential

(17,20)

Vroomen Capital, Inc. Proprietary & Confidential

Key Outcomes

Accuracy (true positives plus false yellows): 64%

Accuracy will improve with a larger training set – learning curve

94.5% confidence that IDSS performs better than random selection

Stage 1: 99% confidence that IRR target for CF assets is >= 28% at 10% higher risk

Stage 2: >98% confidence kNN (k=1) performs best for current data set

Conclusion

The private equity industry is on the verge of facing the same disruption that the Internet has brought to the music industry, the airline industry, the hotel industry, the car rental industry,…...

Relate to Hypothesis; describe how this helps both expert and non-expert decisions – removing emotion. Establish the need for the KE system.

Appendix

Initial Coin Offerings: Background

Issuer offers a pre-determined quantity of its own crypto-currency “tokens” (mostly on Ethereum) – these are NOT share certificates

Investors offer to buy a fixed number of tokens at a fixed price in a Dutch auction

Transaction is recorded on a blockchain (again, mostly the Ethereum blockchain) – significantly reduces transaction cost

The tokens are liquid and so can be bought and sold directly between sellers and buyers

So far ICOs not regulated by JOBS Act or SEC in the US (but see: https ://www.nytimes.com/2017/07/25/business/sec-issues-warning-on-initial-coin- offerings.html)

ICOs experiencing rapid growth: >$1.2B raised so far in 2017 (>10X entire 2016)

JOBS Act, Title III: Background

Passed in both Houses of Congress in early 2012 with large bipartisan and private equity industry support
Signed into law by President Obama on April 5, 2012
Recorded in Federal Register on January 30, 2016

Enables privately held companies to publicly solicit up to $1M per year in equity financing by issuing securities to an unlimited number of investors.

Opens up private equity investment to non-accredited investors
Accredited investor: Net worth >$1M; Annual Income >$300K
Only 2.6% of the US population qualify as accredited investors

Caps the amounts that non-accredited investors can invest
$5,000 per year if Net Worth <$100K; Annual Income <$100K
$10,000 per year if Net Worth >$100K per year; Annual Income >$100K per year

Requires ECF transactions to be conducted by a web based Intermediary that ensures transactions conform to the requirements of the Act and the SEC regulations governing such transactions.

Biography – Paul Vroomen

MSEE, MBA, PhD (Technology & Information Management, UCSC)
Chip Designer - first 7 years out of college
Business Unit VP/GM - 3 Public Companies

Zilog, Inc. – Microprocessors; VLSI Technology, Inc. – Satellite TV.; Oak Technology, Inc. – HDTV/DVD.

President/CEO - 3 Venture funded Start-ups

SandCraft, Inc. - Datacom; Connex Inc. - HDTV; Sandbridge Inc. – Mobile Phone

Executive-In-Residence

Tallwood Venture Capital, LLP

Raised $73M in Venture Capital funding
Co-Board and Board member at 7 companies
Currently working on next start-up – Fintech (ICO/Equity Crowdfunding Platform); Member of Sand Hill Angels

Cumulative Cash Generated/Consumed

-15

-10

-5

012345678910

Years from Initial investment

$Million

Entrepreneur's ViewVenture Capitalist's View

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

IRR <= 0%

0% <IR R< =2 5%

25 %< IRR <= 50 %

50 %< IRR <= 75 %

75 %< IRR <= 10 0%

10 0% <IR R< =1 25 %

12 5% <IR R< =1 50 %

17 5% <IR R< =2 00 %

20 0% <IR R< =2 25 %

22 5% <IR R< =2 50 %

25 0% <IR R< =2 75 %

27 5% <IR R< =3 00 %( no te 1)

30 0% <IR R< =3 25 %

32 5% <IR R< =3 50 %

35 0% <IR R< =3 75 %

37 5% <IR R< =4 00 %

% o f I nv es tm

en ts

Distribu1on of Returns: AIPP

Raw Data Distribu>on Pareto (Calc)

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

(

)

Distribuon of Returns: AIPP

Raw Data Distribuon Pareto (Calc)

-60%

-40%

-20%

20%

40%

60%

80%

100%

10 11 12 13 14 15 16 17 18 19 20

IR R (%

)

Quarters from First Investment

10% Equity 15% Equity 20% Equity 25% Equity

-60%

-40%

-20%

20%

40%

60%

80%

100%

10 11 12 13 14 15 16 17 18 19 20

(

)

Quarters from First Investment

10% Equity 15% Equity 20% Equity 25% Equity

-60%

-40%

-20%

20%

40%

60%

80%

100%

10 11 12 13 14 15 16 17 18 19 20

IR R (%

)

Quarters from First Investment

Baseline 90% of Baseline QoQ Revenue Growth

-60%

-40%

-20%

20%

40%

60%

80%

100%

10 11 12 13 14 15 16 17 18 19 20

(

)

Quarters from First Investment

Baseline 90% of Baseline QoQ Revenue Growth

-10

-5

0 2 4 6 8 10 12 14 16 18 20

Quarters

Quarterly Revenue Cumulative Investment Cumulative Cash Flow 90% Baseline Revenue

-10

-5

0 2 4 6 8 10 12 14 16 18 20

Quarters

Quarterly Revenue Cumulative Investment

Cumulative Cash Flow 90% Baseline Revenue