Returns in Venture Capital (VC) are characterized by extreme outliers and a notable number of defaulted transactions. Building a well-diversified portfolio is pivotal for investors to achieve sufficient downside protection without disproportionately trimming upside potential. This report simulates VC returns to understand the impact of portfolio size on risk and return potential. The simulations are based on the semicontinuous log-normal model of Juergens et al. (2022).
The analysis is conducted with 10,000 Monte Carlo simulations, each with a universe of 900 VC funds in total, which are equally distributed in early-stage (pre-seed and seed), mid-stage (Series A to Series B), and later-stage (Series C or later). That is 300 funds per stage focus and 18,000 transactions in total across all funds. The portfolio composition assumes equally-weighted portfolios of funds with 30 transactions in early-stage funds, 20 transactions in mid-stage funds, and 10 transactions in later-stage funds.
The parameters of the return distributions are calibrated such that the overall return distribution of all transactions closely resembles the statistics of Brown et al. (2020). Assumptions about cross-sectional heterogeneity of fund returns are motivated by Juergens et al. (2022) and adjusted for VC. The simulations assume no access restrictions, i.e., the hypothetical investor can deliberately choose from the entire universe of VC funds. Hence, FOF scenarios illustrate return distributions when randomly picking from the entire universe of VC funds (uninformed). The simulations include a strategy that is highly concentrated on early stage (pure early) and a second strategy with slightly more exposure to mid-stage funds (early mixed).
Assuming an uninformed investor, which invests in early-stage companies or funds, the results show that the downside risk or the chance of losing money is relatively quickly diversified away. However, after more than 500 (350) portfolio companies (or around 15 funds) in the pure early (early mixed) scenario, the upside potential appears disproportionately trimmed compared to the downside protection, which further diversification may offer. Smaller and more concentrated portfolios with less than 350 or 250 portfolio companies (or around 12 funds) in the pure early and early mixed scenario, respectively, offer a slightly higher chance to yield extreme outcomes but at the cost of considerably higher risk. The larger the portfolio, the greater the reversion to the average VC market return and the lower the potential for superior performance. Hence, with 12 to 15 funds or 350 to 500 portfolio companies, investors can expect robust downside protection with comparatively high upside potential. With more informed investment decisions, the upside potential grows notably compared to the uninformed case.
Figure 1. Return Distributions per Stage Focus. This figure shows the return distributions resulting from the simulations of VC transactions for early-stage, mid-stage, and later-stage transactions in Panels A, B, and C, respectively. The descriptive statistics of the return distributions of the bottom quartile, median, and top quartile fund are shown in the tables below each Panel.
Figure 2. Diversification Effects. This figure shows 200 simulations of the portfolio return of a hypothetical uninformed investor in VC funds contingent on the number of portfolio companies.
Center of Entrepreneurial Finance (CEFS), Technical University of Munich (TUM); equation AG