Knowledge that Transforms

Contracts and exits from a sample of 179 investment rounds in 132 entrepreneurial firms by 17 European venture capital (VC) funds are analyzed. The data indicate the financial contracts are quite heterogeneous in terms of both the cash flow and control rights. The use of different securities by European VC funds does not depend on the definition of venture capital, and the securities used are not functional equivalents. A normative empirical analysis of exit shows the likelihood of different types of exit vehicles (IPO, acquisition, and liquidation) and the returns to venture capital depend on not only firm specific characteristics but also the allocation of cash flow and control rights.

To question the statistical significance of return predictability, we cannot specify a null that simply turns off that predictability, leaving dividend growth predictability at its essentially zero sample value. If neither returns nor dividend growth are predictable, then the dividend-price ratio is a constant. If the null turns off return predictability, it must turn on the predictability of dividend growth, and then confront the evidence against such predictability in the data. I find that the absence of dividend growth predictability gives much stronger statistical evidence against the null, with roughly 1-2% probability values, than does the presence of return predictability, which only gives about 20% probability values. I argue that tests based on long-run return and dividend growth regressions provide the cleanest and most interpretable evidence on return predictability, again delivering about 1-2% probability values against the hypothesis that returns are unpredictable. I show that Goyal and Welch's (2005) finding of poor out-of-sample R does not reject return forecastability.

Goyal and Welch (2007) argue that the historical average excess stock return forecasts future excess stock returns better than regressions of excess returns on predictor variables. In this article, we show that many predictive regressions beat the historical average return, once weak restrictions are imposed on the signs of coefficients and return forecasts. The out-of-sample explanatory power is small, but nonetheless is economically meaningful for mean-variance investors. Even better results can be obtained by imposing the restrictions of steady-state valuation models, thereby removing the need to estimate the average from a short sample of volatile stock returns.

We examine the accuracy and contribution of the Merton distance to default (DD) model, which is based on Merton's (1974) bond pricing model. We compare the model to a “naïve” alternative, which uses the functional form suggested by the Merton model but does not solve the model for an implied probability of default. We find that the naïve predictor performs slightly better in hazard models and in out-of-sample forecasts than both the Merton DD model and a reduced-form model that uses the same inputs. Several other forecasting variables are also important predictors, and fitted values from an expanded hazard model outperform Merton DD default probabilities out of sample. Implied default probabilities from credit default swaps and corporate bond yield spreads are only weakly correlated with Merton DD probabilities after adjusting for agency ratings and bond characteristics. We conclude that while the Merton DD model does not produce a sufficient statistic for the probability of default, its functional form is useful for forecasting defaults.

We analyze the role of knowhow acquisition in the formation and duration of joint ventures. Two parties become partners in a joint venture to benefit from each other’s knowhow. Joint operations provide each party with the opportunity to acquire part of its partner’s knowhow. A party’s increased knowhow provides the impetus for the dissolution of the joint venture. We characterize the conditions under which dissolution takes place, identify the party that buys out its partner, determine the time to dissolution, establish its comparative statics, and examine the implications of knowledge acquisition for the desirability of joint venture formation.

We derive an asymptotic expansion formula for option implied volatility under a two-factor jump-diffusion stochastic volatility model when time-to-maturity is small. We further propose a simple calibration procedure of an arbitrary parametric model to short-term near-the-money implied volatilities. An important advantage of our approximation is that it is free of the unobserved spot volatility. Therefore, the model can be calibrated on option data pooled across different calendar dates to extract information from the dynamics of the implied volatility smile. An example of calibration to a sample of S&P 500 option prices is provided.

This article applies the methodology of Bai and Ng (2002, 2004) for decomposing panel data into systematic and idiosyncratic components to both stock returns and turnover panels. This approach works well for both returns and turnover, despite the presence of severe heteroscedasticity and nonstationarity of individual stocks' turnover. We test the mutual fund separation model of Lo and Wang (2000). Trading due to systematic risk in returns can account for 66% of systematic turnover. Thus, portfolio rebalancing due to systematic risk is a very important motive for stock trading. Finally, several common turnover measures may understate the impact of stock trading.

This article shows that investors financing a portfolio of projects may use the depth of their financial pockets to overcome entrepreneurial incentive problems. Competition for scarce informed capital at the refinancing stage strengthens investors' bargaining positions. And yet, entrepreneurs' incentives may be improved, because projects funded by investors with “shallow pockets” must have not only a positive net present value at the refinancing stage, but one that is higher than that of competing portfolio projects. Our article may help understand provisions used in venture capital finance that limit a fund'sinitial capital and make it difficult to add more capital once the initial venture capital fund is raised.

We study the role played by private and public information in the process of price formation in the U.S. Treasury bond market. To guide our analysis, we develop a parsimonious model of speculative trading in the presence of two realistic market frictions—information heterogeneity and imperfect competition among informed traders—and a public signal. We test its equilibrium implications by analyzing the response of two-year, five-year, and ten-year U.S. bond yields to order flow and real-time U.S. macroeconomic news. We find strong evidence of informational effects in the U.S. Treasury bond market: unanticipated order flow has a significant and permanent impact on daily bond yield changes during both announcement and nonannouncement days. Our analysis further shows that, consistent with our stylized model, the contemporaneous correlation between order flow and yield changes is higher when the dispersion of beliefs among market participants is high and public announcements are noisy.