Valuation, Risk, and Strategic Dynamics in Venture Capital

• VC-backed cohort
• CVC-backed cohort

1Introduction

The venture capital landscape went through a fundamental recalibration in 2022–2025 as central banks tightened monetary policy aggressively, ending the era of near-zero interest rates that had fueled record startup valuations. This shift created a natural experiment in how different funding models respond to capital constraints. Corporate venture capital and traditional venture capital represent distinct institutional approaches to financing innovation, each with its own incentive structures, time horizons, and success metrics.

This analysis examines how the two models diverge on three dimensions: valuation methodology, risk management, and strategic positioning. Using Monte Carlo simulation, a data package of investor-level PitchBook and Preqin records, and published benchmarks, I identify how these differences show up in investment outcomes and portfolio performance. The findings point to an equilibrium where neither model clearly outperforms the other on IRR, though each has advantages under specific conditions and the CVC model creates more absolute value per dollar.

The paper contributes in three ways. It quantifies previously theoretical distinctions between CVC and VC. It ties each model input to evidence and calibrates the inputs the evidence contradicts. And it offers a data-driven framework for founders choosing between investor types, along with an account of how market cycles change the relative advantages of each model.

2Theoretical framework: valuation approaches

Valuing high-growth ventures with uncertain cash flows and probabilistic outcomes is one of finance's hardest problems. Traditional VCs and corporate investors approach it through different frameworks.

2.1Traditional VC valuation

Traditional VCs use an outcome-driven method that works backward from potential exit scenarios, combining comparables with probability-weighted expected returns:

Here r is the required return (typically 20–30%) and t is the expected time to exit. The high discount rate reflects both the opportunity cost of capital and the large idiosyncratic risk in venture investments. Data from 2022–2023 shows how this approach responds to rate shifts: as rates rose, exit multiples contracted and over 70% of venture exits occurred below total invested capital (PitchBook, 2023).

VCs put this model into practice through ownership targets rather than explicit DCF calculations. A Series A investor typically seeks 20–30% ownership, which implicitly sets the valuation regardless of founder aspirations. This is consistent with capital asset pricing theory, which implies startups should carry very high risk premiums; empirical estimates put venture betas at 1.9–2.8 (Rödl & Partner).

2.2Corporate VC valuation

CVCs weigh strategic considerations alongside financial returns, which expands the traditional NPV formula:

Corporate venture investments generate both direct financial returns and strategic benefits for the parent: access to new technologies, market intelligence, ecosystem positioning, and potential acquisition targets.

Real options theory supplies the foundation. Tong and Li (2010) show that corporations prefer minority stakes over full acquisitions in uncertain environments, in effect buying an option to expand involvement if the technology proves valuable. The data is consistent with this behavior: corporations increasingly target earlier stages, with 26% of U.S. CVC deals at seed or pre-seed by 2024, up from about 5.6% in 2009 (Gao, 2025), which secures those options at lower entry prices.

CVCs appear willing to accept lower expected financial IRRs when strategic returns compensate. In practice, CVCs sometimes pay premium valuations for startups with strong strategic alignment; Gompers and Lerner (2000) find that corporate investors pay more than other investors on average. In effect they operate on a modified efficient frontier that counts both financial and strategic returns.

2.3Evidence of valuation divergence

The simulation quantifies what these approaches imply. I assume a strategic premium on CVC-backed exits (median IPO $250M vs. $200M; median M&A $60M vs. $50M), and Section 3 shows this premium is supported, and probably understated, by the data. With that premium, the median successful CVC-backed exit returns 6.5× the entry valuation against 5.3× for VC-backed exits.

The premium comes with a tradeoff. CVC-backed companies hold longer before exiting (6.1 years on average vs. 5.1), which compresses IRR despite larger outcomes. By year 5, 59% of eventual VC-backed exits have happened, compared with 39% for CVC-backed companies.

3Model design and calibration

3.1How the model works

The model follows two cohorts of 10,000 U.S. startups each, one VC-backed and one CVC-backed, for ten years. Every company starts at a $10M post-money valuation. In each year a surviving company either fails, exits, or continues, according to year-specific probabilities. A company that exits becomes an IPO or an M&A sale according to a fixed split, and its exit value is drawn from a log-normal distribution with a median set for that exit type and cohort. Companies still private at year 10 count as returning nothing within the horizon. Returns are measured at the company level: the cohort "invests" $10M per company at time zero and receives each exit value in its exit year. That makes the portfolio IRR a measure of value creation before dilution, fees, and carry, not a fund's net return to its limited partners.

The model is not fitted to a single historical dataset. Its probabilities and valuation distributions draw on three sources, and each input is checked against them:

1. Literature review. Academic work on CVC performance (Gompers & Lerner, 2000; Ivanov & Xie, 2010; Tong & Li, 2010; Chemmanur, Loutskina & Tian, 2014) and industry research from PitchBook-NVCA, Counterpart Ventures, and Carta.
2. Primary data collection. Investor-level records pulled in April 2025 for five of the most active corporate venture investors since 2015. This includes PitchBook investor profiles (deal counts, exit counts by type, median exit sizes), Preqin deal exports covering 5,642 transactions and 3,285 portfolio-company positions, and PrivCo investor tearsheets.
3. Market benchmarks. The Preqin 2025 Global Venture Capital Report datapack (fund returns by vintage, exit counts), the WilmerHale Venture Capital Report (median acquisition prices, pre-IPO valuations, and time to exit for 2012–2021), Correlation Ventures' realized-deal outcomes, and Carta's shutdown data.

3.2Input assumptions and their basis

Evidence for each input

Sources: PitchBook and Preqin downloads (April 2025); WilmerHale 2022 Venture Capital Report; PitchBook-NVCA Venture Monitor Q4 2024; Correlation Ventures realized deals 2003–2012 (via LifeSciVC, 2013); Carta (2024); studies cited in References.

3.3What the calibration implies

Most inputs line up with observed data, and the CVC-specific premiums (lower early failure, higher exit values) are, if anything, smaller than the evidence suggests. Two judgment calls shape the results.

IPO frequency. Bringing the IPO share down from 25–30% to 8% is the largest single change from the earlier version of the model. Since IPOs carry four times the median value of M&A exits, this roughly halves portfolio IRRs, from 26–27% to 14–16%. The calibrated figures fall within Preqin's range of median venture fund returns; the original figures sat near the top-quartile boundary.

CVC exit delay. The later CVC exit peak is supported by the literature on fund structure but not confirmed by my Preqin sample. I keep it because it works against the CVC cohort: if CVC-backed companies actually exit on the VC schedule, the CVC advantage is larger than reported here (Section 6.7).

Figure 1When exits happen: model vs. observed CVC holding periods

• Model, VC cohort
• Model, CVC cohort
• Observed, CVC sample (Preqin)

Show data

Years after entry	Model VC	Model CVC	Observed CVC
Year 1	5.7%	2.6%	8.6%
Year 2	7.5%	3.4%	19.4%
Year 3	11.3%	8.1%	15.9%
Year 4	15.1%	10.7%	11.8%
Year 5	19.4%	14.0%	13.4%
Year 6	16.6%	16.2%	6.7%
Year 7	9.9%	14.4%	6.1%
Year 8	7.1%	13.3%	6.7%
Year 9	5.0%	10.2%	3.2%
Year 10	2.4%	7.1%	1.9%
10+	0.0%	0.0%	6.4%

4Risk management: portfolio construction and exit strategy

4.1Traditional VC risk management

Traditional VC firms manage risk mainly through diversification and staged investment. A typical fund invests in 20–30 startups to capture the power-law distribution of returns, where a small share of investments generates most of the profit. In the simulation, the top 5% of companies produce about 60% of all exit value in both cohorts.

VC funds face structural constraints that shape how they handle risk. They run on fixed fund lives, typically 10 years with possible extensions, which creates pressure to reach liquidity. In capital-constrained periods like 2022–2023, that pressure shows up as secondary transactions to return at least partial capital while IPO windows are closed.

VCs manage exit timing to optimize IRR, which weights early cash flows heavily. That biases them toward faster exits even when holding longer might produce more dollars. The simulation reflects this, with VC-backed exits peaking in years 5–6.

4.2Corporate VC risk management

Corporate venture arms manage risk through strategic alignment and operational support rather than broad diversification. Without fixed fund lives or outside LP pressure, CVCs can hold longer and ride out downturns. That patience is a valuable option when valuations are depressed or IPO windows are shut.

Corporate backing does not change the headline odds much. In the simulation, about 78% of companies in both cohorts return nothing within ten years: roughly 47% fail outright and another 32% are still private at year 10, a "zombie" category that a fixed-life fund would have to write down or sell. CVC-backed startups do show lower early failure (8–9% a year in years 1–3 vs. 10%), reflecting the stabilizing effect of corporate partnerships in the vulnerable early years. That input is deliberately conservative; Counterpart Ventures reports CVC-backed failure rates closer to half those of peers.

A distinctive CVC advantage appears in contractions. When outside capital is scarce, the corporate parent can provide bridge financing or act as acquirer of last resort. The model does not encode these mechanisms directly. Its "tight money" scenario (Section 6.5) shows a narrower point: because CVC-backed exits are larger, the CVC cohort stays above break-even when exit rates and valuations fall for everyone, while the VC cohort does not.

4.3Exit path divergence

IPOs are rare for both cohorts. In the calibrated model, 1.7% of VC-backed and 1.8% of CVC-backed companies go public within ten years, and roughly 20% of each are acquired. The literature suggests corporate backing can improve IPO readiness, and Gompers and Lerner (2000) find CVC investments with strategic fit are at least as likely as independent VC investments to go public. My collected data does not show a higher IPO rate for CVC-backed companies, so the calibrated model uses the same rate for both. Where corporate backing shows up in the data is exit value, not exit route.

Despite the theoretical option value of a corporate acquisition, parents rarely buy their own portfolio companies. Fewer than 4% of U.S. startups with CVC funding are later acquired by that same investor (Gao, 2025). The PitchBook profiles tell the same story: three of the five investors each record only one or two M&A deals of their own against hundreds of portfolio exits. This "lack of pathway" points to frequent misalignment between corporate venture and corporate development teams, with most CVC-backed companies exiting through ordinary channels.

In a high-rate environment, CVC patience becomes more valuable as exit timelines stretch, while traditional VCs keep an edge in IRR through quick exits when windows open. In the calibrated model these effects largely net out in IRR terms, while the CVC cohort creates more total value.

5Strategic synergies and value creation

5.1Mechanisms of strategic value creation

A defining feature of CVC investment is its potential to create value beyond capital. Corporate investors can speed portfolio company growth through several channels:

1. Customer and distribution access. Corporate parents can become major customers or introduce enterprise clients, accelerating revenue and traction.
2. Technical resources and infrastructure. Corporations can share R&D, regulatory expertise, or manufacturing capacity that de-risks product development. Chemmanur et al. (2014) find CVC-backed companies produce 26.9% more patents in the three years before IPO than VC-backed peers, with the effect strongest where the startup is close to the parent's technology.
3. Market validation and credibility. A recognized corporate backer raises a startup's credibility with customers, partners, and later investors.
4. Talent access and development. Startups can draw on corporate expertise for specialized hiring or training.

The simulation represents these effects as a 20% premium on median M&A value and a 25% premium on median IPO value for CVC-backed companies. That premium is an input, not a finding. What the model shows is that a premium of this size more than offsets the IRR cost of holding a year longer, and raises the CVC cohort's multiple on invested capital from 1.9× to 2.4×. Remove the premium and the CVC cohort falls behind (Section 6.7).

5.2Quantifying strategic returns

Strategic return on investment is hard to quantify but increasingly important for corporate governance. Forward-thinking CVCs track:

• New product development accelerated through startup partnerships
• Revenue from joint go-to-market initiatives
• Cost savings from startup technologies
• Market intelligence and competitive positioning

These measures help justify CVC investment when financial returns alone fall short of corporate hurdle rates, which matters more when high rates make other uses of corporate capital more attractive.

5.3Limitations and constraints

Strategic synergies are not guaranteed. Common failure modes include:

• Corporate bureaucracy. Slow decisions or long approval chains keep startups from the promised resources.
• Strategic misalignment. Changes in corporate strategy or leadership can end support for investments that were once strategic. The top-CVC league table in my data includes programs that have gone quiet: one made its last recorded investment in March 2021, and two others recorded none after November 2023.
• Narrow focus. Tying too closely to one corporate partner can limit a startup's broader market.
• Opportunity cost. Custom development for a corporate partner can divert resources from the core product.

Well-managed strategic relationships appear to raise startup performance and exit values. Poorly executed ones can become a liability.

6Simulation results

All results below come from the calibrated model: 10,000 simulated startups per cohort over ten years, using the inputs in Table 1 and random seed 42 unless stated otherwise. Section 6.8 shows how much results move across seeds.

6.1Where the cohorts end up

Overall outcomes are close. About 21–22% of companies in each cohort reach a successful exit within ten years, almost all through acquisition. Nearly half fail outright, and about a third are still private at the end of the horizon.

Figure 2Ten-year outcomes by cohort

• VC-backed
• CVC-backed

Show data

Outcome	VC-backed	CVC-backed
IPO	1.7%	1.8%
M&A	20.0%	19.4%
Failed	46.6%	46.4%
Still private at year 10	31.7%	32.5%

6.2Exit timing

The two cohorts reach the same destination on different schedules. VC-backed exits peak in year 5 and fall off quickly. CVC-backed exits build more slowly, peak in year 6, and stay elevated through year 9. Survival curves show the same story from the other side: by year 10 both cohorts have about a third of companies still private.

Figure 3Successful exits by year

• VC-backed
• CVC-backed

Show data

Year	VC-backed	CVC-backed
Year 1	1.24%	0.56%
Year 2	1.63%	0.72%
Year 3	2.46%	1.71%
Year 4	3.27%	2.26%
Year 5	4.21%	2.97%
Year 6	3.61%	3.43%
Year 7	2.15%	3.04%
Year 8	1.53%	2.82%
Year 9	1.08%	2.15%
Year 10	0.52%	1.51%

Figure 4Companies still private, and cumulative exits, by year

• VC-backed
• CVC-backed

Show data

Year	VC still private	CVC still private	VC cum. exits	CVC cum. exits
Year 0	100.0%	100.0%	0.0%	0.0%
Year 1	89.0%	91.5%	1.0%	0.5%
Year 2	78.3%	82.3%	2.8%	1.4%
Year 3	68.1%	73.3%	5.1%	3.1%
Year 4	59.3%	65.2%	8.5%	5.3%
Year 5	51.0%	57.4%	12.7%	8.5%
Year 6	44.4%	50.5%	16.3%	12.0%
Year 7	39.9%	45.0%	18.5%	15.0%
Year 8	36.3%	40.0%	20.1%	17.7%
Year 9	33.8%	36.0%	21.2%	19.7%
Year 10	31.8%	32.8%	21.8%	21.1%

6.3The distribution of exit multiples

Among successful exits, multiples follow a heavy right tail. The CVC distribution is shifted right throughout: 32% of CVC-backed exits return 10× or more, against 23% for VC-backed exits. Very few modeled exits return less than 1×, while Correlation Ventures finds that 29% of realized venture deals exit below cost. The model treats almost every exit as a win, which flatters both cohorts' success-case figures.

Figure 5Exit multiples among successful exits

• VC-backed
• CVC-backed

Show data

Exit multiple	VC-backed	CVC-backed
<1×	1.8%	0.9%
1–2×	9.9%	6.5%
2–5×	35.6%	30.7%
5–10×	29.9%	30.2%
10–20×	15.7%	20.3%
20–50×	5.4%	8.7%
50×+	1.8%	2.7%

6.4Portfolio returns: similar IRRs, different value

The calibrated model gives portfolio IRRs of 13.8% for VC and 16.4% for CVC with seed 42. That gap is partly sampling noise: across 40 random seeds the median gap is 1.0 point, and the CVC cohort comes out ahead in 65% of runs (Table 5). The timing penalty and the valuation premium nearly offset in IRR terms because:

• VC-backed startups exit faster, which raises IRR through earlier cash flows.
• CVC-backed startups exit at higher values, which raises absolute returns.
• Both cohorts have similar success rates, about 21–22%.

Multiples do not converge. The CVC cohort returns 2.42× its invested capital against 1.86× for VC, about 30% more value from the same starting point, and this gap holds in every seed. An investor who cares about IRR sees two roughly equivalent strategies. An investor who cares about total value created, or who has no pressing need to recycle capital, sees a clear CVC advantage.

The calibrated IRRs also line up with real-world fund performance. Preqin's median net IRR for venture funds of 2010–2018 vintages ranges from 14.3% to 19.6%. Model IRRs are before dilution, fees, and carry, so they should run somewhat above net fund returns; landing in the same range suggests the calibrated model is, if anything, still slightly optimistic.

6.5Interest rates and market conditions

In the model, the interest rate affects only present values; it does not change which companies exit or for how much. Figure 6 isolates that effect. Raising the discount rate from 2% to 12% cuts the VC cohort's present-value multiple by 36% and the CVC cohort's by 42%. The CVC cohort is more rate-sensitive because its cash arrives later, much as a longer-duration bond loses more value when rates rise. At a 12% discount rate, the VC cohort's present-value multiple falls to 1.08×.

Figure 6Present-value multiple by discount rate

• VC-backed
• CVC-backed

Show data

Discount rate	VC-backed	CVC-backed
2%	1.68×	2.14×
3%	1.60×	2.01×
5%	1.46×	1.79×
8%	1.27×	1.52×
10%	1.17×	1.37×
12%	1.08×	1.23×

To test sensitivity to market conditions, I examined two contrasting scenarios:

1. "Boom" scenario: Simulating an environment of abundant liquidity and low capital costs.
2. "Tight" scenario: Modeling constrained capital markets with elevated discount rates.

Figure 7Portfolio IRR by market scenario

• VC-backed
• CVC-backed

Show data

Scenario	VC-backed	CVC-backed
Boom	25.6%	27.5%
Calibrated	13.8%	16.4%
Tight	−1.0%	1.5%

Three findings stand out. First, market conditions matter far more than investor type. In the tight scenario the VC cohort loses money (−1.0% IRR, 0.95× multiple) and the CVC cohort barely breaks even (1.5%, 1.10×). Discounted at 10%, neither cohort recovers its capital. Second, the CVC cohort holds up slightly better in the tight scenario, staying above break-even where the VC cohort does not. In this model that resilience comes from the valuation premium, which keeps more CVC exits above cost when values fall, rather than from bridge financing or corporate acquisitions. Third, in the boom scenario the CVC cohort still edges out VC (27.5% vs. 25.6%). The model does not show VC capturing more of the upside in good markets. If that happens in practice, it would come from exit timing that responds to market windows, which this model holds fixed.

6.6Which assumptions matter most

Figure 8 moves one group of inputs at a time by ±20% for both cohorts and records the change in portfolio IRR. Exit values and exit probabilities have the most influence, moving IRR by 8–10 points. Dispersion moves it by about 7 points and failure rates by about 5. Once calibrated to 8%, the IPO share matters little, at 2–2.5 points. Every one of these swings is larger than the difference between cohorts, so the CVC-vs-VC comparison is more robust than the absolute level of either IRR.

Figure 8Sensitivity of portfolio IRR to ±20% changes in each input

• VC-backed
• CVC-backed

Show data

Input (×0.8 → ×1.2)	VC-backed	CVC-backed
Exit values	8.4% → 18.6%	11.7% → 20.4%
Exit probability	9.2% → 17.6%	12.3% → 19.9%
Dispersion (σ)	10.9% → 17.6%	13.4% → 20.2%
Failure rates	17.2% → 12.2%	18.1% → 12.8%
IPO share	12.5% → 14.9%	15.2% → 17.2%

6.7Alternative assumptions

Table 4 shows how the results change under three alternative assumptions for the CVC cohort.

Two results follow. The exit delay is the main thing holding the CVC cohort back: if CVC-backed companies exit on the VC schedule, the CVC lead grows to about 8 points. And the CVC advantage rests entirely on the valuation premium. Without it, slower exits leave the CVC cohort 1.5 points behind on IRR with only a slightly higher multiple.

That last result matters for how the findings should be applied. The strongest evidence for the premium comes from companies with a strategic fit to their corporate investor (Gompers & Lerner, 2000; Ivanov & Xie, 2010). A CVC investment without that fit should not be assumed to carry it.

6.8Stability across random draws

The results above use one random seed. Rerunning the calibrated model with 40 different seeds shows how much is sampling noise.

7Strategic implications and recommendations

7.1For startup founders

1. Align investor selection with company goals. Match investor strengths to stage and objectives. Companies that need patient capital for complex technology may benefit from CVC partnerships; those in fast-moving markets may prioritize VC growth expertise.
2. Choose a corporate investor for fit, not just capital. The CVC advantage in this analysis comes entirely from higher exit values, and the evidence ties those to strategic fit with the parent. A corporate investor with no real connection to your business brings the slower timeline without the premium.
3. Protect optionality and exit flexibility. Avoid terms that constrain future fundraising or exits, especially with corporate investors. A right of first refusal on acquisition can deter other buyers. With fewer than 4% of CVC-backed startups acquired by their corporate investor, keeping multiple exit paths open matters.
4. Plan for acquisition, not IPO. About 92% of successful exits in the calibrated model, and in the market data behind it, are acquisitions. Build relationships with likely acquirers early, whatever the investor mix.
5. Optimize for value per share. When weighing dilution, consider how each investor adds value beyond capital. A strategic investor that meaningfully accelerates growth can justify more dilution if the value gain outpaces the ownership loss.
6. Prepare for long timelines. Average time to exit in the model is 5–6 years, and a third of companies are still private at year 10. Secure enough runway, manage burn conservatively, and set milestones that allow for market volatility.

7.2For corporate venture capital

1. Use explicit strategic and financial frameworks. Define metrics for both financial returns and strategic benefits. In a high-rate environment, corporate CFOs demand more accountability for venture allocations.
2. Deploy strategic assets deliberately. The model's CVC advantage depends on the valuation premium (median 6.5× vs. 5.3× on successful exits). Without it, the CVC cohort trails VC. The premium has to be earned through active support.
3. Measure success in multiples as well as IRR. At similar IRRs the CVC cohort creates about 30% more value. Judging a corporate program on IRR alone understates what patient capital delivers.
4. Align internal stakeholders. Coordinate venture, business development, and corporate development to preserve acquisition option value. The low rate at which parents acquire their own portfolio companies shows how often this breaks down.
5. Invest counter-cyclically, with eyes open. A longer horizon lets CVCs invest when valuations are depressed and traditional capital is scarce. Longer holds also mean more exposure to rate increases (Figure 6), so patience is not free.
6. Co-invest with traditional VCs. VCs bring financial discipline and exit orchestration; CVCs bring strategic resources and stability.

7.3For traditional venture capital

1. Recalibrate valuation benchmarks. Update models and comparables for the high-rate environment. With over 70% of recent exits below invested capital, and IPOs accounting for under one in ten exits, expectations need resetting.
2. Extend portfolio support horizons. Reserve more for follow-ons, use continuation vehicles for promising companies, or run smaller, more concentrated funds. In the model, a third of companies are still private when a ten-year fund would need to wind down.
3. Emphasize operational value-add. Help portfolio companies with burn management, partnership introductions, and milestone-based planning.
4. Partner with CVCs. Corporate co-investors can supply stability and resources in downturns, and the evidence associates them with higher exit values.
5. Reset LP expectations. Recent vintages are struggling (Preqin reports a 3.7% median net IRR for 2021 funds so far), and the tight-money scenario produces negative returns. Communicate realistic timelines while highlighting attractive entry valuations.

8Conclusion

Corporate and traditional venture capital are distinct but complementary ways to finance innovation, each with structural advantages in different conditions. Traditional VCs excel at diversification, rapid scaling, and IRR optimization through timely exits. Corporate investors provide strategic acceleration, market access, and patient capital through downturns.

Calibrated to observed exit data, the simulation shows these paths deliver similar IRRs but different value. Portfolio IRRs of 14–16% sit within the range of median venture fund returns, and differ by about a point on average across runs, while the CVC cohort returns about 30% more capital. That advantage rests on a strategic valuation premium the evidence links to strategic fit; without it, slower exits leave CVC behind. Investor type also matters far less than market conditions: moving from a boom to a tight market costs either cohort about 26 points of IRR and pushes the VC cohort below break-even.

The post-2022 environment has sharpened these tradeoffs. Traditional VCs face pressure from delayed exits and LP return expectations. Corporate investors must justify strategic programs against higher hurdle rates, and some programs have already gone quiet. Corporate participation remains high, about 23% of U.S. deals in 2024 by PitchBook's count and 28% globally by Counterpart's, which suggests the market values this diversified funding base, especially as capital becomes more expensive.

Neither model alone serves the startup ecosystem best. Their coexistence and collaboration provide a sturdier foundation for financing innovation through market cycles. Understanding the risk-return profile of each helps founders, corporations, and fund managers make better decisions about capital structure, investment approach, and strategic positioning.

Appendix

Company-level returns. Each company is modeled as a $10M investment that returns its full exit value. Real investors own a fraction of the company and are diluted by later rounds, and fund LPs pay fees and carry. Portfolio IRRs here measure value creation by the companies, which is why they should run above net fund returns.

Benchmark data processing. Preqin deal exports were grouped by target company. For each company, the CVC's first funding round was matched to the first subsequent exit deal (trade sale, IPO, merger, secondary buyout, or unspecified exit). Holding periods use only companies first backed in 2005–2014 to limit bias from recent investments that have not had time to exit. IPO shares count IPOs against all non-bankruptcy exits. Bankruptcies are sparsely recorded in both PitchBook and Preqin, so the data is used to check exit mix and timing, not failure rates.

Data sources and use. Market data in this paper comes from commercial databases (PitchBook and Preqin, with supplementary investor profiles from PrivCo), accessed in April 2025 for this independent study, and from the public reports and academic studies listed in References. Figures drawn from the databases are aggregated or summarized. No raw records, deal-level data, or investor-level data are reproduced or redistributed. PitchBook, Preqin, and PrivCo and their data remain the property of their respective owners, and this paper is not affiliated with or endorsed by any of them. Model results are my own calculations from the assumptions stated in Table 1.

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23. Tong, T. W., & Li, Y. (2010). Real options and investment mode: Evidence from corporate venture capital and acquisition. Organization Science, 21(1), 101–119.
24. WilmerHale. (2022). 2022 Venture Capital Report.
25. Zapflow. (2022). How Does Inflation Impact VC Investments?

Primary data collected for this study (April 2025)

1. PitchBook investor profiles for five active corporate venture investors (general statistics, exits by type, investments by deal type), and a ranking of corporate venture investors by number of investments since 2015.
2. Preqin Pro deal exports for four of the same investors (5,642 transactions in total).
3. PrivCo investor tearsheets for the same investors.