The risk and return of venture capital

Abstract

This paper measures the mean, standard deviation, alpha, and beta of venture capital investments, using a maximum likelihood estimate that corrects for selection bias. The bias-corrected estimation neatly accounts for log returns. It reduces the estimate of the mean log return from 108% to 15%, and of the log market model intercept from 92% to $-7\%$. The selection bias correction also dramatically attenuates high arithmetic average returns: it reduces the mean arithmetic return from 698% to 59%, and it reduces the arithmetic alpha from 462% to 32%. I confirm the robustness of the estimates in a variety of ways. I also find that the smallest Nasdaq stocks have similar large means, volatilities, and arithmetic alphas in this time period, confirming that the remaining puzzles are not special to venture capital.

Introduction

This paper measures the expected return, standard deviation, alpha, and beta of venture capital investments. Overcoming selection bias is the central hurdle in evaluating such investments, and it is the focus of this paper. We observe valuations only when a firm goes public, receives new financing, or is acquired. These events are more likely when the firm has experienced a good return. I overcome this bias with a maximum-likelihood estimate. I identify and measure the increasing probability of observing a return as value increases, the parameters of the underlying return distribution, and the point at which firms go out of business.

I base the analysis on measured returns from investment to IPO, acquisition, or additional financing. I do not attempt to fill in valuations at intermediate dates. I examine individual venture capital projects. Since venture funds often take 2–3% annual fees and 20–30% of profits at IPO, returns to investors in venture capital funds are often lower. Fund returns also reflect some diversification across projects.

The central question is whether venture capital investments behave the same way as publicly traded securities. Do venture capital investments yield larger risk-adjusted average returns than traded securities? In addition, which kind of traded securities do they resemble? How large are their betas, and how much residual risk do they carry?

One can cite many reasons why the risk and return of venture capital might differ from the risk and return of traded stocks, even holding constant their betas or characteristics such as industry, small size, and financial structure (leverage, book/market ratio, etc.). First, investors might require a higher average return to compensate for the illiquidity of private equity. Second, private equity is typically held in large chunks, so each investment might represent a sizeable fraction of the average investor's wealth. Finally, VC funds often provide a mentoring or monitoring role to the firm. They often sit on the board of directors, or have the right to appoint or fire managers. Compensation for these contributions could result in a higher measured financial return.

On the other hand, venture capital is a competitive business with relatively free (though not instantaneous; see Kaplan and Shoar, 2003) entry. Many venture capital firms and their large institutional investors can effectively diversify their portfolios. The special relationship, information, and monitoring stories that suggest a restricted supply of venture capital might be overblown. Private equity could be just like public equity.

I verify large and volatile returns if there is a new financing round, IPO, or acquisition, i.e., if we do not correct for selection bias. The average arithmetic return to IPO or acquisition is 698% with a standard deviation of 3,282%. The distribution is highly skewed; there are a few returns of thousands of percent, many more modest returns of “only” 100% or so, and a surprising number of losses. The skewed distribution is well described by a lognormal, but average log returns to IPO or acquisition still have a large 108% mean and 135% standard deviation. A CAPM estimate gives an arithmetic alpha of 462%; a market model in logs still gives an alpha of 92%.

The selection bias correction dramatically lowers these estimates, suggesting that venture capital investments are much more similar to traded securities than one would otherwise suspect. The estimated average log return is 15% per year, not 108%. A market model in logs gives a slope coefficient of 1.7 and a $-7.1\%$, not +92%, intercept. Mean arithmetic returns are 59%, not 698%. The arithmetic alpha is 32%, not 462%. The standard deviation of arithmetic returns is 107%, not 3,282%.

I also find that investments in later rounds are steadily less risky. Mean returns, alphas, and betas all decline steadily from first-round to fourth-round investments, while idiosyncratic variance remains the same. Later rounds are also more likely to go public.

Though much lower than their selection-biased counterparts, a 59% mean arithmetic return and 32% arithmetic alpha are still surprisingly large. Most anomalies papers quarrel over 1–2% per month. The large arithmetic returns result from the large idiosyncratic volatility of these individual firm returns, not from a large mean log return. If $\sigma =1$ (100%), ${\mathrm{e}}^{\mu +(1/2){\sigma }^2}$ is large (65%), even if $\mu =0$. Venture capital investments are like options; they have a small chance of a huge payoff.

One naturally distrusts the black-box nature of maximum likelihood, especially when it produces an anomalous result. For this reason I extensively check the facts behind the estimates. The estimates are driven by, and replicate, two central sets of stylized facts: the distribution of observed returns as a function of firm age, and the pattern of exits as a function of firm age. The distribution of total (not annualized) returns is quite stable across horizons. This finding contrasts strongly with the typical pattern that the total return distribution shifts to the right and spreads out over time as returns compound. A stable total return is, however, a signature pattern of a selected sample. When the winners are pulled off the top of the return distribution each period, that distribution does not grow with time. The exits (IPO, acquisition, new financing, failure) occur slowly as a function of firm age, essentially with geometric decay. This fact tells us that the underlying distribution of annual log returns must have a small mean and a large standard deviation. If the annual log return distribution had a large positive or negative mean, all firms would soon go public or fail as the mass of the total return distribution moves quickly to the left or right. Given a small mean log return, we need a large standard deviation so that the tails can generate successes and failures that grow slowly over time.

The identification is interesting. The pattern of exits with time, rather than the returns, drives the core finding of low mean log return and high return volatility. The distribution of returns over time then identifies the probability that a firm goes public or is acquired as a function of value. In addition, the high volatility, rather than a high mean return, drives the core finding of high average arithmetic returns.

Together, these facts suggest that the core findings of high arithmetic returns and alphas are robust. It is hard to imagine that the pattern of exits could be anything but the geometric decay we observe in this dataset, or that the returns of individual venture capital projects are not highly volatile, given that the returns of traded small growth stocks are similarly volatile. I also test the hypotheses $\alpha =0$ and $\mathrm{E}(R)=15\%$ and find them overwhelmingly rejected.

The estimates are not just an artifact of the late 1990s IPO boom. Ignoring all data past 1997 leads to qualitatively similar results. Treating all firms still alive at the end of the sample (June 2000) as out of business and worthless on that date also leads to qualitatively similar results. The results do not depend on the choice of reference return: I use the S&P500, the Nasdaq, the smallest Nasdaq decile, and a portfolio of tiny Nasdaq firms on the right-hand side of the market model, and all leave high, volatility-induced arithmetic alphas. The estimates are consistent across two basic return definitions, from investment to IPO or acquisition, and from one round of venture investment to the next. This consistency, despite quite different features of the two samples, gives credence to the underlying model. Since the round-to-round sample weights IPOs much less, this consistency also suggests there is no great return when the illiquidity or other special feature of venture capital is removed on IPO. The estimates are quite similar across industries; they are not just a feature of internet stocks. The estimates do not hinge on particular observations. The central estimates allow for measurement error, and the estimates are robust to various treatments of measurement error. Removing the measurement error process results in even greater estimates of mean returns. An analysis of influential data points finds that the estimates are not driven by the occasional huge successes, and also are not driven by the occasional financing round that doubles in value in two weeks.

For these reasons, the remaining average arithmetic returns and alphas are not easily dismissed. If venture capital seems a bit anomalous, perhaps similar traded stocks behave the same way. I find that a sample of very small Nasdaq stocks in this time period has similarly large mean arithmetic returns, large—over 100%—standard deviations, and large—53%!—arithmetic alphas. These alphas are statistically significant, and they are not explained by a conventional small-firm portfolio or by the Fama-French three-factor model. However, the beta of venture capital on these very small stocks is not one, and the alpha is not zero, so venture capital returns are not “explained” by these very small firm returns. They are similar phenomena, but not the same phenomenon.

Whatever the explanation—whether the large arithmetic alphas reflect the presence of an additional factor, whether they are a premium for illiquidity, etc.—the fact that we see a similar phenomenon in public and private markets suggests that there is little that is special about venture capital per se.