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.

We study time variation in expected excess bond returns. We run regressions of one-year excess returns on initial forward rates. We find that a single factor, a single tent-shaped linear combination of forward rates, predicts excess returns on one-to five-year maturity bonds with R 2 up to 0.44. The return-forecasting factor is countercyclical and forecasts stock returns. An important component of the return-forecasting factor is unrelated to the level, slope, and curvature movements described by most term structure models. We document that measurement errors do not affect our central results.