Knowledge that Transforms

It is a major challenge for asset pricing models to generate a high equity premium and a low risk-free rate while imposing realistic consumption dynamics. To address this issue, our paper proposes a novel pricing channel: we allow for consumption drops that can spark an economic crisis. This new feature generates a large equity premium even if possible consumption drops are of moderate size. In turn, our model also matches the consumption data of 42 countries along several dimensions. In particular, our approach generates a realistic number of crises that have realistic durations and involve clustering of moderate consumption drops.

We propose a novel human capital model that decomposes aggregate income risk into highand low-income risk. We find that high-income risk is priced, while low-income risk is insignificant. The high-income factor alone explains 77% of the cross-sectional variation in the twenty-five size and book-to-market portfolios, earns a risk premium of about 7% per year, and its pricing power extends to the full cross-section of individual stocks. It is also related to the value factor, suggesting that the value premium might be compensation for income risk. Overall, our evidence indicates that high-income risk is an important macroeconomic risk factor.

Financial constraints are fundamental to empirical research in finance and economics. We propose two tests to evaluate how well measures of financial constraints actually capture constraints. We find that firms typically classified as constrained do not actually behave as if they were constrained: they have no trouble raising debt when their demand for debt increases exogenously and use the proceeds of equity issues to increase payouts to shareholders. Our evidence suggests that extant findings that have been attributed to constraints may instead reflect differences in the growth and financing policies of firms at different stages of their life cycles.

We find that differences in individuals' prenatal environments explain heterogeneity in financial decisions later in life. An exogenous increase in exposure to prenatal testosterone is associated with the masculinization of financial behavior, specifically with elevated risk taking and trading in adulthood. We also examine birth weight. Those with higher birth weight are more likely to participate in the stock market, whereas those with lower birth weight tend to prefer portfolios with higher volatility and skewness, consistent with compensatory behavior. Our results contribute to the understanding of how the prenatal environment shapes an individual's behavior in financial markets later in life.

We propose a hybrid approach for estimating beta that shrinks rolling window estimates toward firm-specific priors motivated by economic theory. Our method yields superior forecasts of beta that have important practical implications. First, unlike standard rolling window betas, hybrid betas carry a significant price of risk in the cross-section even after controlling for characteristics. Second, the hybrid approach offers statistically and economically significant out-of-sample benefits for investors who use factor models to construct optimal portfolios. We show that the hybrid estimator outperforms existing estimators because shrinkage toward a fundamentals-based prior is effective in reducing measurement noise in extreme beta estimates.

We characterize linkages between average returns calculated at different horizons. Theoretically, when stocks incorporate information slowly, average short-horizon returns are downward biased. Buy-and-hold strategies can amplify the effect. In contrast, existing theories analyze price noises that are independent of fundamentals, and buy-and-hold portfolio returns are unaffected. We document horizon effects as large as 10% annualized in daily and monthly style portfolios and international indices. Slow reaction to market information, identified by gradually declining lagged betas, is an important cause. These findings have natural consequences for performance evaluation.

This paper documents the existence of a CEO investment cycle, in which disinvestment decreases over a CEO's tenure, while investment increases, leading to "cyclical" firm growth in assets and employment. The estimated variation in investment rate over the CEO investment cycle is of the same order of magnitude as the differences caused by business cycles or financial constraints. Results from a number of tests generally support the view that the investment cycle is caused by agency problems, leading to increasing investment quantity and decreasing investment quality over time as the CEO gains more control over his board.

We examine the effect of trust in venture capital. Our theory predicts a positive relationship of trust with investment, but a negative relationship with success. Using a hand-collected dataset of European venture capital deals, we find that the Eurobarometer measure of trust among nations positively predicts venture capital firms' investment decisions, but that it has a negative correlation with successful exits. Our theory also predicts that earlier stage investments require higher trust, that syndication is more valuable in low-trust situations, and that higher trust investors use more contingent contracts. The empirical evidence supports these predictions.

We examine the effect of institutional ownership on corporate disclosure policy using a regression discontinuity design. Using a novel dataset comprising every 8-K filing between 1996 and 2006, we find that positive shocks to institutional ownership around Russell index reconstitutions increase the quantity, form, and quality of disclosure. Compared with those at the bottom of the Russell 1000 index, firms at the top of the Russell 2000 index increase institutional ownership by 9.8%, and disclose 4.7% longer 8-K filings with 21.3% more embedded graphics. This incremental disclosure significantly increases the information content of 8-K filings for the market and for analysts.

We propose a Bayesian-averaging portfolio choice strategy with excellent out-of-sample performance. Every period a new model is born that assumes means and covariances are constant over time. Each period we estimate model parameters, update model probabilities, and compute robust portfolio choices by taking into account model uncertainty, parameter uncertainty, and non-stationarity. The portfolio choices achieve higher out-of-sample Sharpe ratios and certainty equivalents than rolling window schemes, the 1/N approach, and other leading strategies do on a majority of 24 datasets.