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.

An investment factor, long in low-investment stocks and short in high-investment stocks, helps explain the new issues puzzle. Adding the investment factor into standard factor regressions reduces the SEO underperformance by about 75%, the IPO underperformance by 80%, the underperformance following convertible debt offerings by 50%, and Daniel and Titman's (2006) composite issuance effect by 40%. The reason is that issuers invest more than nonissuers, and the investment factor earns a significantly positive average return of 0.57% per month.

Evidence of stock-return predictability by financial ratios is still controversial, as documented by inconsistent results for in-sample and out-of-sample regressions and by substantial parameter instability. This article shows that these seemingly incompatible results can be reconciled if the assumption of a fixed steady state mean of the economy is relaxed. We find strong empirical evidence in support of shifts in the steady state and propose simple methods to adjust financial ratios for such shifts. The in-sample forecasting relationship of adjusted price ratios and future returns is statistically significant and stable over time. In real time, however, changes in the steady state make the in-sample return forecastability hard to exploit out-of-sample. The uncertainty of estimating the size of steady-state shifts rather than the estimation of their dates is responsible for the difficulty of forecasting stock returns in real time. Our conclusions hold for a variety of financial ratios and are robust to changes in the econometric technique used to estimate shifts in the steady state.

Twenty-five years of volatility research has left the macroeconomic environment playing a minor role. This paper proposes modeling equity volatilities as a combination of macro- economic effects and time series dynamics. High-frequency return volatility is specified to be the product of a slow-moving component, represented by an exponential spline, and a unit GARCH. This slow-moving component is the low-frequency volatility, which in this model coincides with the unconditional volatility. This component is estimated for nearly 50 countries over various sample periods of daily data. Low-frequency volatility is then modeled as a function of macroeconomic and financial variables in an unbalanced panel with a variety of dependence structures. It is found to vary over time and across countries. The low-frequency component of volatility is greater when the macroeconomic factors of GDP, inflation, and short-term interest rates are more volatile or when inflation is high and output growth is low. Volatility is higher not only for emerging markets and markets with small numbers of listed companies and market capitalization relative to GDP, but also for large economies. The model allows long horizon forecasts of volatility to depend on macroeconomic developments, and delivers estimates of the volatility to be anticipated in a newly opened market. The Author 2008. Published by Oxford University Press on behalf of the Society for Financial Studies. All rights reserved. For permissions, please e-mail: [email protected]., Oxford University Press.

This article introduces a new nonparametric test to detect jump arrival times and realized jump sizes in asset prices up to the intra-day level. We demonstrate that the likelihood of misclassification of jumps becomes negligible when we use high-frequency returns. Using our test, we examine jump dynamics and their distributions in the U.S. equity markets. The results show that individual stock jumps are associated with prescheduled earnings announcements and other company-specific news events. Additionally, S&P 500 Index jumps are associated with general market news announcements. This suggests different pricing models for individual equity options versus index options.

This article presents a model of optimal control of corporate boards of directors. We determine when one would expect inside versus outside directors to control the board, when the controlling party will delegate decision-making to the other party, the extent of communication between the parties, and the number of outside directors. We show that shareholders can sometimes be better off with an insider-controlled board. We derive endogenous relationships among profits, board control, and the number of outside directors that call into question the usual interpretation of some documented empirical regularities.

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.

Our article comprehensively reexamines the performance of variables that have been suggested by the academic literature to be good predictors of the equity premium. We find that by and large, these models have predicted poorly both in-sample (IS) and out-of-sample (OOS) for 30 years now; these models seem unstable, as diagnosed by their out-of-sample predictions and other statistics; and these models would not have helped an investor with access only to available information to profitably time the market.

We test and confirm the hypothesis that individual investors are net buyers of attention-grabbing stocks, e.g., stocks in the news, stocks experiencing high abnormal trading volume, and stocks with extreme one-day returns. Attention-driven buying results from the difficulty that investors have searching the thousands of stocks they can potentially buy. Individual investors do not face the same search problem when selling because they tend to sell only stocks they already own. We hypothesize that many investors consider purchasing only stocks that have first caught their attention. Thus, preferences determine choices after attention has determined the choice set.