[We develop a continuous-time model in which a portfolio manager is hired by a management company. On the basis of observed portfolio returns, all agents update their beliefs about the manager's skills. In response, investors can move capital into or out of the mutual fund, and the management company can fire the manager. Introducing firing rationalizes several empirically documented findings, such as the positive relation between manager tenure and fund size or the increase of portfolio risk before a manager replacement and the following risk decrease. The analysis predicts that the critical performance threshold that triggers firing increases signifi-cantly over a manager's tenure and that management replacements are accompanied by capital inflows when a young manager is replaced but may be accompanied by capital outflows when a manager with a long tenure is fired. Our model yields much lower valuation levels for management companies than simple applications of discounted cash flow (DCF) methods and is thus more consistent with empirical observations.]

This paper analyzes the effect of dynamic capital structure adjustments on credit risk. Firms may optimally adjust their leverage in response to stochastic changes in firm value. It is shown that capital structure dynamics lower optimal initial leverage ratios but increase both, fair credit spreads and expected default probabilities for moderate levels of transactions costs. Numerical examples demonstrate that expected default frequencies do not decrease monotonically in the traditional distance to default measure. The magnitude of the effect of capital structure dynamics depends on firm characteristics such as asset volatility, the growth rate, the effective corporate tax rate, debt call features and transactions costs. We find that the underestimation of credit spreads and expected default frequencies is exacerbated when the risk-adjusted drift of the underlying stochastic process is inferred from a model which ignores the opportunity to recapitalize. Finally it is shown that the value-at-risk of corporate bonds increases with the distance to default (DD) both for very low and for very high values of DD whereas it decreases for intermediate values.

The average capital growth rate across firms declines sharply during a recession, and recovers only slowly. We provide a micro-founded explanation for this and several new stylized facts of investment asymmetry. Our investment model features various degrees of reversibility, cyclical macroeconomic shocks, and uncertainty about the state of the economy. Model simulations replicate strikingly different empirical patterns of capital growth rates at the aggregate and firm levels, featuring no slope asymmetry and a positive level asymmetry at the firm level, negative slope and level asymmetries at the aggregate level, and a positive relation between the industry-level slope asymmetry and asset illiquidity.

We study the implications of predictability on the optimal asset allocation of ambiguity-averse long-term investors and analyze the term structure of the multivariate risk–return trade-off considering parameter uncertainty. We calibrate the model to real returns of U.S. stocks, long-term bonds, cash, real estate, and gold using the term spread and the dividend–price ratio as additional predictive variables, and we show that over long horizons, the optimal asset allocation is significantly influenced by the covariance structure induced by estimation errors. The ambiguity-averse long-term investor optimally tilts his or her portfolio toward a seemingly inefficient portfolio, which shows maximum robustness against estimation errors.

We evaluate predictive regressions that explicitly consider the time-variation of coefficients in a comprehensive Bayesian framework. For monthly returns of the S&P 500 index, we demonstrate statistical as well as economic evidence of out-of-sample predictability: relative to an investor using the historic mean, an investor using our methodology could have earned consistently positive utility gains (between 1.8% and 5.8% per year over different time periods). We also find that predictive models with constant coefficients are dominated by models with time-varying coefficients. Finally, we show a strong link between out-of-sample predictability and the business cycle.

This paper shows that short debt maturities commit equityholders to leverage reductions when refinancing expiring debt in low-profitability states. However, shorter maturities lead to higher transaction costs since larger amounts of expiring debt need to be refinanced. We show that this trade-off between higher expected transaction costs against the commitment to reduce leverage in low-profitability states motivates an optimal maturity structure of corporate debt. Since firms with high costs of financial distress and risky cash flows benefit most from committing to leverage reductions, they have a stronger motive to issue short-term debt. Evidence supports the model’s predictions.

We develop a continuous-time model in which a portfolio manager is hired by a management company. On the basis of observed portfolio returns, all agents update their beliefs about the manager’s skills. In response, investors can move capital into or out of the mutual fund, and the management company can fire the manager. Introducing firing rationalizes several empirically documented findings, such as the positive relation between manager tenure and fund size or the increase of portfolio risk before a manager replacement and the following risk decrease. The analysis predicts that the critical performance threshold that triggers firing increases significantly over a manager’s tenure and that management replacements are accompanied by capital inflows when a young manager is replaced but may be accompanied by capital outflows when a manager with a long tenure is fired. Our model yields much lower valuation levels for management companies than simple applications of discounted cash flow (DCF) methods and is thus more consistent with empirical observations.

This paper presents a framework to study how investors’ social concerns affect technology choices. Consequentialist preferences (disutility from aggregate harm) influence outcomes only if investors coordinate, unless internalized harm is independent of an investor’s mass. Non-consequentialist preferences (disutility from stockholdings) affect outcomes regardless of coordination. Both preferences have stronger impact when risk-sharing consequences of technology supply are small (e.g., highly correlated returns), and their effects cannot be inferred from cost-of-capital differences. When harm is stochastic, polluting firms may appear less risky to social investors. Depending on type and strength of social preferences, this can support or hinder the green transition.