Knowledge that Transforms

Do informed shareholders who can influence corporate decisions improve governance? We demonstrate this may not be generally true in a model of takeovers. The model suggests that a shareholder’s ability to collect information and trade ex post may cause him, ex ante, to support pursuing value-destroying takeovers or oppose value-enhancing takeovers. Surprisingly, we find conditions under which giving the active shareholder greater influence weakens governance and reduces firm value, even if such influence power can be used to reject bad takeovers ex post. Our model sheds light on the limitations of relying on informed, active shareholders to improve governance.

We develop a model to analyze information aggregation and learning in housing markets. Households enter a neighborhood by buying houses and consuming each other’s final goods. In the presence of pervasive informational frictions, housing prices serve as important signals to households and capital producers about the neighborhood’s economic strength. Our model provides a novel amplification mechanism in which noise from housing markets propagates throughout the local economy via learning because of the complementarity in households’ decisions, distorting migration into the neighborhood and the supply of capital and labor. We provide consistent evidence based on the recent U.S. housing cycle.

Can algorithms assist firms in their decisions on nominating corporate directors? Directors predicted by algorithms to perform poorly indeed do perform poorly compared to a realistic pool of candidates in out-of-sample tests. Predictably bad directors are more likely to be male, accumulate more directorships, and have larger networks than the directors the algorithm would recommend in their place. Companies with weaker governance structures are more likely to nominate them. Our results suggest that machine learning holds promise for understanding the process by which governance structures are chosen and has potential to help real-world firms improve their governance.

Macrofinance term structure models rely too heavily on the volatility of expected inflation news as a source for variations in nominal bond yield shocks. We develop and estimate a model featuring inflation nonneutrality and preference shocks. The stochastic volatility of inflation and consumption govern bond risk premiums movements, whereas preference shocks generate fluctuations in real rates. The model accounts for key bond market features without resorting to an overly dominating expected inflation channel. The estimation shows that preference shocks are strongly negatively correlated with market distress factors and that real rate news is the dominant driver of nominal yield shocks.

Lending marketplaces aimed at directly connecting retail lenders and borrowers retreat from auctions and, instead, set prices and allocate credit on their own, despite evidence that retail investors possess valuable soft and nonstandard information. We investigate this puzzle by analyzing a unique data set of 7,455 auctions and 34 million bids from a leading British peer-to-business platform. We find that the platform was vulnerable to liquidity shocks, resulting in sizable deviations from information efficiency. Deviations increased over time because of a growing role played by noncrowd players, particularly large investors and algorithms.

We study the role of investor experience in the formation of asset price bubbles. We conduct a call market experiment in which participants trade assets with each other and a learning-to-forecast experiment in which participants only forecast future prices (while trade based on these forecasts is computerized). Each experiment comprises three treatments varying the information that participants receive about the fundamental value. Each experimental market is repeated three times. Throughout, we observe sizable bubbles that persist despite participant experience. Our findings in the call market experiment contrast with those in the literature. Our findings in the learning-to-forecast experiment are novel.

We study the impact of foreign institutional investors on price efficiency with firm-level international data. Using additions to the MSCI index and the U.S. Jobs and Growth Tax Relief Reconciliation Act as exogenous shocks to foreign ownership, we show that greater foreign ownership increases stock price informativeness, especially in developed economies. This increase arises from new information that foreign investors bring in and displacement of less-informed domestic retail investors. Finally, we show that foreign ownership, particularly from active investors, increases market liquidity, reduces firms’ cost of equity, and increases firms’ real investment growth.

This paper uncovers that expected excess bond returns display a positive correlation with the slope of the yield curve (i.e., yield spread) in expansions but a negative correlation in recessions. We use a macro-finance term structure model with different market prices of risk in expansions and recessions to show that a very accommodating monetary policy in recessions is a key driver of this switch in return predictability.

The rise of centralized mining pools for risk sharing does not necessarily undermine the decentralization required for blockchains: because of miners’ cross-pool diversification and pool managers’ endogenous fee setting, larger pools better internalize their externality on global hash rates, charge higher fees, attract disproportionately fewer miners, and grow more slowly. Instead, mining pools as a financial innovation escalate miners’ arms race and significantly increase the energy consumption of proof-of-work-based blockchains. Empirical evidence from Bitcoin mining supports our model’s predictions. The economic insights inform other consensus protocols and the industrial organization of mainstream sectors with similar characteristics but ambiguous prior findings.

Using detailed data for U.S. homeowners, we document a negative, nonlinear relation between the loan-to-value ratio (LTV) of homeowners’ primary residence and their labor income. Consistent with high LTV individuals experiencing constrained mobility, we find stronger effects among subprime, liquidity- constrained individuals and those living in regions with limited alternative local employment opportunities and strict noncompete law enforcement. Though high LTV individuals are less likely to move across MSAs, they are more likely to change jobs without changing their residence. We find no effects among similar neighboring renters employed at the same firm and with a similar job tenure.