Equitybee's VPO model simulation results

Our proprietary Venture Portfolio Optimizer (VPO) model leverages an extensive dataset covering over 10,500 VC-backed exit events and over 4,600 unique startups, covering nearly two-thirds of the U.S. VC-backed ecosystem from 1999 to 2022.

Upon running the various simulations described later in this document, the VPO Model has generated the following outputs:

Median Net IRR¹
of 25.4%

Net IRR > 15%
99% probability

Net IRR > 20%
91% probability

MOIC³ of at least 1.0x
99%+ probability

¹ Net IRR is the annual internal rate of return, net of standard fees
² Based on Equitybee’s proprietary Monte Carlo-based model using 20,000 iterations
³ Net Multiple on Invested Capital
‍
The model performance shown is back-tested and hypothetical, and does not reflect the returns of any actual investment fund(s) or portfolio(s). Past performance is no indication of future performance. Back-tested hypothetical performance is subject to significant limitations; see Important Information for more information.

Simulation Results

Below are the results of the Monte Carlo simulation. These results represent the model portfolio’s return distributions and dispersions after generating 20,000 randomized portfolios using the aforementioned model inputs.

All else being equal, larger portfolios tend to: (1) increase the likelihood of generating net IRRs of 20%+, and (2) decrease the dispersion of returns. As seen below, a model portfolio of 20 companies yields a ~65% probability of exceeding a 20% net IRR with a ~13% standard deviation.

However, once the model portfolio exceeds 100 holdings, its probability of exceeding a 20% net IRR increases to ~84% and above, while its standard deviation of returns decreases to ~4% and below.

Net IRR Distribution

The histogram below represents the model portfolio’s net IRR distribution at year five. For instance, according to the simulations, the model generates net IRRs greater than 20% about 91.9% of the time, with the median net IRR being 25.4%. Additionally, the model portfolio’s median time to achieve a 1.0x DPI ratio (Distributed Paid-In-Capital) is ~3.4 years.

Net IRR

Median

25.4%

Min

13.0%

Max

37.3%

Standard Deviation

3.0%

Probability IRR > 20%

99.6%

Probability IRR > 20%

91.9%

⁴ The optimal Portfolio Mix is defined as one that maximizes returns and minimizes dispersion (i.e., standard deviation of returns).
⁵ Source: Equitybee research based on Crunchbase data

Sensitivity Analysis on VPO Model

Assumptions

To address any potential concerns about our model inputs, we ran a sensitivity analysis. The matrix below represents varying premiums applied to both the historical median time to exit (vertical) and the different probabilities of a 0.0x exit (horizontal) across companies stages. Each intersection shows the potential net IRR under that particular set of conditions.

For instance, by increasing both the probabilities of a 0.0x exit and the median time to exit by 50% across companies stages, the Model still returns a robust median net IRR of 11.1%. Even upon doubling the premiums applied to those two inputs, the Model still generates a positive median net IRR.

³ The Optimal Portfolio Mix is defined as one that maximizes returns and minimizes dispersion (i.e., standard deviation of returns).

Hypothetical Model Portfolio Summary⁶

The charts below represent deal-level performance. As expected, the bulk of the hypothetical portfolio’s returns are driven by later-stage companies, since the optimal portfolio is overweight such companies.

Cash Flows

The chart below shows the hypothetical portfolio’s cumulative cash flows over time. Note that the hypothetical portfolio starts making distributions between years 1-2 and, eventually, can return its original invested capital (i.e., $25M in this case).

The hypothetical portfolio then continues to make distributions throughout the life of the hypothetical portfolio, when fully liquidated, generating a total of ~$43M in total distributions over its lifetime.

Portfolio Composition

The charts below show how the hypothetical portfolio’s asset mix evolves. Since the optimal portfolio is overweight later-stage companies, most of these companies exit before the hypothetical portfolio is liquidated at the end of year five.

As time progresses, the model outcomes show how the earlier-stage companies are expected to take longer to have an exit event and will have a higher allocation in the portfolio composition.

⁶ For illustration purposes only.

The VPO is provided by Equitybee Advisors, LLC. Equitybee Advisors is an exempt reporting adviser, providing investment advisory services solely to private funds. Investing involves risk, including the risk of loss. Indexes and the Venture Portfolio Optimizer are unmanaged; one cannot invest directly in an index or model. Return data represent past performance, are not a guarantee of future performance, and are not indicative of any specific investment.

Any model performance included herein is back-tested and hypothetical, and does not reflect the returns of any actual investment fund(s) or portfolio(s). Past performance is no indication of future performance. The model’s returns represent the hypothetical back-test of the criteria of the strategy, do not reflect actual investments offered or managed by Equitybee and do not represent the actual performance achieved by any Equitybee fund.

Hypothetical back-tested performance results are subject to significant limitations; it is assumed that the securities used in the hypothetical back-tested results were available during the time period presented. In addition, back-testing assumes Equitybee offers were available at the assumed terms. The data are based on criteria that has been applied retroactively with the benefit of hindsight; these criteria cannot account for all financial risk that may affect the actual performance of a fund or strategy. Back-tested hypothetical returns are dependent on the market and economic conditions that existed during the period. Future market or economic conditions can adversely affect the performance of any investment fund or strategy which relies on the model or its assumptions.