Multi-Stage Portfolio Construction with Monte Carlo

Guest post by Michael Palank (MaC Venture Capital) and Anubhav Srivastava (Tactyc)

Readers can view this post in an interactive Tactyc model here.

Earlier this year, Tactyc and MaC Venture Capital released an interactive seed-stage portfolio construction calculator, which serves as a high-level view of the factors that seed-stage funds consider when constructing portfolios and managing various construction parameters. Since then, we’ve received tremendous feedback from the venture community on how Tactyc has helped managers with the fund construction process — plus how we could make it even more useful. We’ve consistently heard that managers want the ability to:

• Flex graduation and exit rates for all stages and see impacts on returns
• Appropriately size the follow-on reserves
• Granularly run follow-on assumptions by stage and number of rounds
• Benchmark returns with real market data

To address the demand from within our community, we’ve launched a new version that has it all: Monte Carlo simulations, Power Law curves, valuation step-ups, and more. Using Tactyc’s 1-Click Template, fund managers can now:

• Automatically generate a venture portfolio based on your fund parameters
• “Graduate” and “exit” companies based on graduation and exit rates
• Use Monte Carlo simulations to forecast the likely follow-on reserve needed
• Summarize portfolio value growth over time and return metrics for LP’s and GP’s
• Benchmark return metrics with market data from Cambridge Associates

Setting Fund Parameters

To get started, set your fund and portfolio allocation parameters. Below is a quick primer on navigating the template and an overview of the various model assumptions. Note that the current default assumptions in this template use benchmarked data sourced from NVCA.

• Fund Size: Indicate the size of your fund in USD.
• Investment Period: Select the period (in years) during which your fund will be making initial investments (this template assumes a standard fund life of 10 years).
• Management Fees: List the management fee to be earned both during and after the investment period.
• Allocation Parameters: Set the dollar allocation for each stage investment (this template assumes your fund will be making investments from Pre-Seed to Series C).
• Follow-On Reserves: Set a follow-on reserve for each entry stage (i.e., the % of your allocated stage capital to be used for follow-on rounds for all investments originating from this stage).
• Follow-On Rounds: Set the number of rounds you intend to participate in (this will aid in calculating your follow-on efficiency in a later section).
• Target Ownership: Indicate the intended target ownership for each entry stage (this is used to calculate your initial and follow-on check sizes).
• Pool Dilution: Include the expected ESOP dilution for each round.
• Graduation Rate:List the likelihood of company graduation from this stage.
• Exit Rate: List the likelihood of company graduation exiting at this stage.

Key Portfolio Metrics

Next, we’ll look at some key portfolio metrics. The first chart shows the total amount available for investment, net of management fees and expenses. Again, it’s important here to recognize the importance of management fees recycling, as it aligns both the GPs and LPs by making more capital available for investment. The following two charts summarize the follow-on allocation, both in terms of dollars and number of investments.

Our default case shows a total of $70M available for investment, net of management fees and expenses. We’ve accrued a $5M benefit by recycling management fees. Tactyc has forecasted a total of 19 initial investments and 37 follow-on investments.

This section summarizes the number of investments, implied initial check sizes, follow-on check sizes, and the ownership profiles for each entry stage. The ownerships profile is highly dependent on follow-on reserves and the number of follow-on rounds for each investment. Tactyc attempts to follow on to the next round (based on what you set for the number of rounds to follow) to maintain the fund’s target ownership until the follow-on reserve is met.

In our default case, this lands us at ~$1M in Seed investments, $4.3M for Series A investments, and $7.9M for Series B investments in our default case. Our ownerships are maintained for the first few rounds (after accounting for ESOP dilution) before being diluted down to 5% for Seed, 8.3% for Series A, and 7.7% for Series C.

This is our average ownership for each entry stage as companies graduate to the next round.

Follow-On Analysis

The first chart below summarizes the total follow-on dollars allocated across all stages and compares that with the follow-on dollars needed and the follow-on dollars actually deployed. The second chart shows the % follow-on allocation, needed and deployed, and the final chart shows how these metrics look for each entry round.

In our default case, we needed $37.8M to fully participate in all intended follow-on rounds but were only allocating $29.8M was allocated (the third chart shows why this is the case). Our Seed investments demanded a $17.4M follow-on allocation, but we were only allocating $11.9M for this stage. In which case, we may want to slightly tweak in order to reduce the number of intended follow-on rounds, reduce target ownership % of Seed investments, or increase the follow-on reserve % allocation towards the Seed stage.

What is an Ideal Follow-On Reserve?

Setting an appropriate follow-on reserve is an entire article in and of itself. In short, you don’t want to under-allocate (and miss out on future follow-ons) or over-allocate (and miss out on initial investments). Fortunately, the Tactyc template comes armed with a Monte Carlo module, which you see in action here.

We’ve run a Monte Carlo simulation for 1,000 iterations and assigned a normal distribution probability function to the graduation and exit rates. Tactyc will run 1,000 versions of this model. Each version will have different companies exiting or graduating — we can then see what the distribution is for follow-on capital needed across all those 1000 versions and decide the appropriate amount for our fund.

In our default case, the mean across all these 1,000 simulations for follow-on reserve was $37.7M, closely tying with our current allocation of $37.8M. Thus, there is a 44% probability that we might need more follow-on reserves. In addition, considering the data above, we might look to increase this cushion slightly to account for higher than expected graduation rates in our portfolio.

Valuation Step-Ups

A key driver of venture returns is the compounding effect of valuation multiples as we step up the FMV for each subsequent round. The first chart shows the FMV at each subsequent round, and the second chart shows the valuation step-ups in terms of multiples. The third chart shows the compounding effect of the valuation multiples by showing the implied return on our initial tranche of investment. The final chart shows the blended return multiple, including follow-on investments.

In our default case, our initial investment tranche steps up to 38.2x the initial investment. While our blended return would be somewhat lower given the follow-on investments we’ve made since, the compounding effect shows the power of entering early into successful investments.

Return Metrics

Finally, let’s take a look at some of the return metrics. The first chart shows the fund’s total value at exit, followed by the gross and net LP return multiples and IRR.

Benchmarking with Market Data

How realistic is your fund’s projected performance compared to real market data? Using our template, quickly and easily compare return metrics with market data sourced from Cambridge Associates.

The Power Law Illustrated

VC funds do not follow a normal distribution; they follow a Power Law Curve (i.e., a small % of firms capture the majority of the returns). We can see the Power Law in effect here on our portfolio. How did we get here? We’ve simply implied it based on our graduation and exit rates; by setting graduation and exiting rates, you’re effectively building the Power Law curve for your fund. The chart below shows this distribution of exit multiples and compares the curve for each entry stage.

In our default case, 68% of our Seed Investments only return 0–1x (failures or break-evens). As expected, this risk reduces as we look at Series A investments and Series B investments — but overall, the curve profile is still skewed mostly towards lower multiples.

Cash Flow Summary

The final two charts show LP and GP cash flows — both comparing in-period distributions and cumulative net cash flow.

Closing Thoughts

Effective portfolio construction requires dealing with uncertainties and quantifying risk where possible. The Monte Carlo simulation used above to size our follow-on reserve is an example of using quantitative methods to guide a key construction parameter. Similarly, finding which graduation rate is a hurdle can inform our follow-on strategy. As a final point, it’s important to stress-test this portfolio model by reducing graduation and exit rates for a “downside case” and evaluate if the manager is still comfortable underwriting these “downside” returns.

Interested in setting this up for your fund?

• Clone this template to customize new scenarios, save notes, and add team logos.
• Reach out to us at venturecapital@tactyc.io, and we can onboard your current spreadsheet model into Tactyc or build a custom interactive model for you.
• If you are interested in a more “bottoms-up” build that forecasts each company over time, we also have a Multi-Stage VC Bottoms-Up Portfolio Construction template that forecasts each investment and lets you granularly run scenarios for each investment.

We’re constantly looking for ways to improve this template for managers and serve our community. Please share feedback with us by reaching venturecapital@tactyc.io.

About Tactyc

Tactyc is a no-code platform that transforms spreadsheet models into interactive web apps that enable smarter scenario analysis. Powered by the Tactyc Spreadsheet Engine that translates spreadsheet logic automatically into code, Tactyc enables users to create interactive presentations, extract model insights and build embeddable calculators in seconds. Tactyc was founded in 2020 by Anubhav Srivastava and is based in Los Angeles, CA.

About MaC Venture Capital

MaC Venture Capital is a seed-stage venture capital firm that invests in technology startups leveraging shifts in cultural trends and behaviors. Our diverse backgrounds in technology, business, government, entertainment, and finance allow us to accelerate entrepreneurs on the verge of their breakthrough moments. We provide hands-on support crucial for building and scaling category-leading companies, including operations strategy, brand building, recruiting, and mission-critical introductions.

Originally published at https://www.kauffmanfellows.org on July 23, 2021.