Venture Fund
The Risk and Return of Venture Capital
John H. Cochrane1 Graduate School of Business, University of Chicago March 19, 2004
School of Business, University of Chicago, 1101 E. 58th St. Chicago IL 60637, 773 702 3059, john.cochrane@gsb.uchicago.edu. I am grateful to Susan Woodward, who suggested the idea of a selection-bias correction for venture capital returns, and who also made many useful comments and suggestions. I gratefully acknowledge the contribution of Shawn Blosser, who assembled the venture capital data. I thank many seminar participants and two anonymous referees for important comments and suggestions. I gratefully acknowledge research support from NSF grants administered by the NBER and from CRSP. Data, programs, and an appendix describing data procedures and algebra can be found at http://gsbwww.uchicago.edu/fac/john.cochrane/research/Papers/. JEL code: G24. Keywords: Venture capital, Private equity, Selection bias.
1 Graduate
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. We can only measure a return when a firm goes public, is acquired, or gets a new financing round. These events are more likely when the firm has achieved a good return, so estimates that do not correct for selection bias are optimistic. The bias-corrected estimate neatly accounts for log returns. It reduces the estimate of mean log return from 108% to 15%, and of the log market model intercept from 92% to -7%. However, log returns are very volatile, with an 89% standard deviation. Therefore, arithmetic average returns and intercepts are much higher than geometric averages. The selection bias correction dramatically attenuates but does not eliminate 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 check the robustness of the estimates in a variety of ways.
John H. Cochrane1 Graduate School of Business, University of Chicago March 19, 2004
School of Business, University of Chicago, 1101 E. 58th St. Chicago IL 60637, 773 702 3059, john.cochrane@gsb.uchicago.edu. I am grateful to Susan Woodward, who suggested the idea of a selection-bias correction for venture capital returns, and who also made many useful comments and suggestions. I gratefully acknowledge the contribution of Shawn Blosser, who assembled the venture capital data. I thank many seminar participants and two anonymous referees for important comments and suggestions. I gratefully acknowledge research support from NSF grants administered by the NBER and from CRSP. Data, programs, and an appendix describing data procedures and algebra can be found at http://gsbwww.uchicago.edu/fac/john.cochrane/research/Papers/. JEL code: G24. Keywords: Venture capital, Private equity, Selection bias.
1 Graduate
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. We can only measure a return when a firm goes public, is acquired, or gets a new financing round. These events are more likely when the firm has achieved a good return, so estimates that do not correct for selection bias are optimistic. The bias-corrected estimate neatly accounts for log returns. It reduces the estimate of mean log return from 108% to 15%, and of the log market model intercept from 92% to -7%. However, log returns are very volatile, with an 89% standard deviation. Therefore, arithmetic average returns and intercepts are much higher than geometric averages. The selection bias correction dramatically attenuates but does not eliminate 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 check the robustness of the estimates in a variety of ways.