Technological Growth and Asset Pricing

We study the asset‐pricing implications of technological growth in a model with “small,” disembodied productivity shocks and “large,” infrequent technological innovations, which are embodied into new capital vintages. The technological‐adoption process leads to endogenous cycles in output and asset valuations. This process can help explain stylized asset‐valuation patterns around major technological innovations. More importantly, it can help provide a unified, investment‐based theory for numerous well‐documented facts related to excess‐return predictability. To illustrate the distinguishing features of our theory, we highlight novel implications pertaining to the joint time‐series properties of consumption and excess returns.     

The Output Effect of a Transition to Price Stability When Velocity Is Time Varying

This paper explores the effect of time‐varying velocity on output responses to policies for reducing/stopping inflation. We study a dynamic general equilibrium model with sticky prices in which we introduce time‐varying velocity. Specifically, we endogenize time‐varying velocity into the model developed by Ireland (1997) for analyzing optimal disinflation. The nonlinear solution method reveals that, depending on velocity, the “disinflationary boom” found by Ball (1994) may disappear even under perfect credibility and that early output losses may be much larger than previously thought. Indeed, we find that a gradual disinflation from a low inflation may even be undesirable.     

Efficiently Inefficient Markets for Assets and Asset Management

We consider a model where investors can invest directly or search for an asset manager, information about assets is costly, and managers charge an endogenous fee. The efficiency of asset prices is linked to the efficiency of the asset management market: if investors can find managers more easily, more money is allocated to active management, fees are lower, and asset prices are more efficient. Informed managers outperform after fees, uninformed managers underperform, while the average manager's performance depends on the number of “noise allocators.” Small investors should remain uninformed, but large and sophisticated investors benefit from searching for informed active managers since their search cost is low relative to capital. Hence, managers with larger and more sophisticated investors are expected to outperform.     

Dynamic Trading with Predictable Returns and Transaction Costs

We derive a closed‐form optimal dynamic portfolio policy when trading is costly and security returns are predictable by signals with different mean‐reversion speeds. The optimal strategy is characterized by two principles: (1) aim in front of the target, and (2) trade partially toward the current aim. Specifically, the optimal updated portfolio is a linear combination of the existing portfolio and an “aim portfolio,” which is a weighted average of the current Markowitz portfolio (the moving target) and the expected Markowitz portfolios on all future dates (where the target is moving). Intuitively, predictors with slower mean‐reversion (alpha decay) get more weight in the aim portfolio. We implement the optimal strategy for commodity futures and find superior net returns relative to more naive benchmarks.