We derive a firm's optimal capital structure and managerial compensation contract when employees are averse to bearing their own human capital risk, while equity holders can diversify this risk away. In the presence of corporate taxes, our model delivers optimal debt levels consistent with those...

We examine the importance of asset pricing anomalies (alphas) for the real economy. We develop a novel quantitative model with lumpy investment that features such informational inefficiencies and yields closed-form solutions for cross-sectional distributions of firm dynamics. Our findings...

We examine the importance of cross-sectional asset pricing anomalies (alphas) for the real economy. We develop a novel quantitative model of the cross-section of firms that features lumpy investment and informational inefficiencies, while yielding distributions in closed form. Our findings...

We use machine learning to construct a statistically optimal and unbiased benchmark for firms' earnings expectations. We show that analyst expectations are on average biased upwards, and that this bias exhibits substantial time-series and cross-sectional variation. On average, the bias increases...

We use machine learning to construct a statistically optimal and unbiased benchmark for firms' earnings expectations. We show that analyst expectations are on average biased upwards, and that this bias exhibits substantial time-series and cross-sectional variation. On average, the bias increases...

We examine the importance of asset pricing anomalies (alphas) for the real economy. We develop a novel quantitative model with lumpy investment that features such informational inefficiencies and yields closed-form solutions for cross-sectional distributions of firm dynamics. Our findings...

We introduce a real-time measure of conditional biases in firms' earnings forecasts. The measure is defined as the difference between analysts' expectations and a statistically optimal unbiased machine-learning benchmark. Analysts' conditional expectations are, on average, biased upwards, and...