Knowledge that Transforms

I study how an activist shareholder organizes a cost-sharing coalition with active shareholders while seeking support from passive shareholders outside the coalition. A larger coalition shifts more of the campaign cost to members, allowing the activist to pursue lower-value proposals. Observing a larger coalition therefore lowers passive shareholders’ posterior expectation of proposal value. The activist chooses the largest coalition that preserves support from pivotal passive shareholders, endogenizing coalition size. Consequently, coalition size need not be a monotone measure of activist influence, and activism frequency depends on passive shareholders’ preferences. Coalition building strengthens the proxy-fight threat and can increase prevote settlements.

We investigate a dynamic oligopoly model with two phases: a normal phase where an essential input is available, and a disruption phase where firms must rely on inventories. Disruptions create an overcutting pricing incentive arising from the option to forgo sales and monopolize the market once rivals stock out. This monopoly option causes the price to jump to a supracompetitive level at the advent of the disruption and to remain above the competitive price path throughout. Price caps raise welfare, but reduce inventory investment. Government reserves reduce prices and raise buffer stocks, as do reserve mandates when financially feasible.

We propose a novel approach to synthesize presumably information-driven insider trading signals for the cross section of stocks. We find that the resulting straightforward composite strategy can often forecast returns in a global sample. This predictability is strongest in equal-weighted portfolios, short holding periods, and insider-buying signals, and it improves further with global diversification. Cross-country analysis reveals that varying insider trading restrictions between countries have limited explanatory power for the performance of the composite strategy. Overall, the results are consistent with a short-term informational advantage of insiders, which might be due to the skillful interpretation of non-private news.

This study examines whether programmable spreads, defined as the difference between decentralized finance smart-contract lending rates and U.S. Treasury-Bill benchmarks, contain forward-looking information about liquidity stress and risk aversion. Using data from major stablecoin lending markets, it finds that programmable spreads are associated with subsequent financial tightening and contain statistically significant forward-looking information regarding future changes in U.S. financial conditions. Wider spreads also forecast higher future excess returns in equity and bond markets, consistent with the predictive relationships implied by time-varying risk premia. These relationships hold both in sample and out of sample and are strongest at short horizons. Moreover, these findings suggest that programmable spreads may serve as high-frequency, complementary market-based indicators of funding stress and information associated with time-varying risk aversion.

This paper studies how the accuracy of a short‐term performance measure affects its value when a principal decides whether to retain or fire an agent, who can manipulate that measure. Compared with ignoring performance and never firing the agent, the performance measure is valuable when the savings in the expected cost of the retention error is more than the expected cost of the firing error. A more accurate performance measure can have a non‐monotonic effect on its value: Greater accuracy improves the principal's ability to correctly identify the agent, which increases efficient firing, but it causes the incompetent agent to increase manipulation to avoid being fired, which increases inefficient retention. With a less accurate performance measure, the incompetent agent has weaker incentives for manipulation, and an increase in accuracy increases its value due to the identification effect. With a more accurate performance measure, the manipulation effect dominates, and an increase in accuracy decreases its value. The results imply that the principal prefers an imperfect performance measure when the agent can choose the level of manipulation.

Since the 2000s, U.S. public pension funds have actively shifted their risky investments away from public equities and toward alternative assets like private equity and hedge funds—some much more than others. We explore a range of possible explanations for these trends and argue that beliefs about the risk-adjusted return of alternatives have played a central role. Pension beliefs are shaped by investment consultants, peers, and experience in the 1990s. Other demand-side channels related to risk-seeking motives, spending needs, and fund governance have weaker empirical support. We also consider supply-side factors and agency interpretations of the data.

We develop a model of data-driven competition in which product quality depends on users’ data contributions and firms’ investments in data infrastructure. Users’ inability to internalize the value generated by their data, together with firms’ strategic investment incentives, can lead to data underinvestment—limiting quality—or overinvestment—concentrating market power. We introduce a data union that coordinates users’ contributions and can be implemented through a data trust or intermediary that aggregates user data. By shaping data compensation, the union enables users to capture and internalize more of their data’s value, enhancing user welfare, especially when combined with data sharing among firms. We apply these insights to financial data, digital payments, and Open Finance. Our analysis shows that data portability can strengthen competition but, without coordination, may leave data investment too low or excessive.