Working Papers
Institutional Investors and Information Acquisition: Implications for Asset Prices and Informational Efficiency (with Adrian Buss)
We study the joint determination of endogenous information acquisition and equilibrium asset prices in a rational expectation equilibrium model with a continuum of asset managers who care about their performance relative to a benchmark and have CRRA preferences. In the presence of benchmarking, managers are less willing to deviate from the benchmark and, thus, to speculate based on private information, such that less of a manager's private information gets incorporated into prices. As benchmarking also reduces the fraction of managers that endogenously decide to acquire private information, prices are substantially less informative in the presence of institutional investors. The benchmark asset is therefore perceived to be more risky, leading to a decline in price, which can dominate the positive price effect stemming from the managers' excess demand due to index-hedging, and a substantial increase in return volatility. We also provide a numerical algorithm to solve rational expectation equilibrium models with general preferences and general distributions for payoffs and noisy supply.
On the Dispersion of Skill and Size in Active Management: Multi-Agent Dynamic Equilibrium with Endogenous Information
What is the optimal size of the active portfolio management industry? How does the distribution of skill and wealth across investors within industry affect asset prices and information aggregation? This paper studies these and closely related questions in a dynamic rational expectations equilibrium (REE) economy with endogenous information. The dispersion of skill and capital arise endogenously in our infinite horizon model with ex-ante identical investors with constant relative risk aversion (CRRA). Besides new theoretical implications, our model links private information (unobservable) to capital allocation (observable), and thereby brings the predominantly theoretical REE literature one step closer to the econometrician.
Optimal Slow Trading with High Speed "Front Runners"
High frequency traders (HFT) employ several trading strategies with different externalities on other (slower) traders. One of the most controversial HFT strategies consist of trading on orders from other (slower) market participants fractions of a second before these trades arrive to the market, sometimes referred to as "front running". This paper contributes to the literature by studying the optimal reaction of the slow trader to the high speed "front runner" and the implications of this reaction for market liquidity. We study a perfect Bayesian equilibrium in which the slow insider needs to decide how much noise to add to its trade to prevent HFT market entry. Importantly, we distinguish between two types of noise: "random noise" and "liquidity noise", the latter consisting of repeating trades of noise traders. While both types of noise prevent learning of the HFT, only liquidity noise has the capability of reducing market liquidity available to the HFT. This reduces HFT profits and therefore more capable of preventing HFT entry, increasing profits for slow insiders.
Optimal Benchmarking with Delegated Information Choice (with Adrian Buss)
Retailer Power with Linear Contracts (with Markus Reisinger and Tim Thomes)