Exploiting the heteroskedasticity of the changes in short-term and long-term interest rates and exchange rates around the FOMC announcement, we identify three structural monetary policy shocks. We eliminate the predictable part of the shocks and study their effects on financial variables and macro variables. The first shock resembles a conventional monetary policy shock, and the second resembles an unconventional monetary shock. The third shock leads to an increase in interest rates, stock prices, industrial production, consumer prices, and commodity prices. At the same time, the excess bond premium and uncertainty decrease, and the U.S. dollar depreciates. Therefore, this third shock combines all the characteristics of a central bank information shock.

Firms increasingly rely on markets for technology to acquire innovations developed outside their boundaries, yet acquiring intellectual property rights alone often does not guarantee successful implementation. Many technologies depend on tacit know-how that must be supplied by the provider after the transaction is completed. This paper examines whether common ownership between a technology provider and a potential adopter mitigates this implementation problem. I develop a model in which overlapping institutional investors cause the provider to partially internalize the adopter’s gains from successful implementation, strengthening incentives to transfer tacit know-how. This mechanism operates only when know-how is unverifiable – absent this friction, common ownership leaves matching and outcomes unchanged. Under moral hazard, the model predicts that common ownership increases the likelihood of technology transfer to a given adopter, that this effect is stronger when tacit know-how is more important, and that common ownership improves post-transfer outcomes conditional on adoption. I test these predictions using U.S. patent reassignments between publicly traded firms. Using within-deal variation across competing potential adopters and plausibly exogenous variation from passive index-fund holdings, I show that common ownership increases the likelihood that a firm acquires a technology, particularly when the transferred bundle is more tacit. Common ownership predicts stronger subsequent innovation and higher future firm value, especially when ownership overlap is concentrated among investors with stronger incentives to monitor the provider. These findings show how ownership structure shapes interfirm technology transfer by affecting not only who acquires a technology, but also how much value is created.

We examine how institutional saving mechanisms influence retirement saving decisions under bounded rationality and income risk. Using a life-cycle experiment with habit formation and loss aversion, we test mandatory and voluntary binding savings under deterministic and stochastic income. Voluntary commitment improves saving performance only when income is predictable; under uncertainty, it fails to improve performance. Mandatory savings do not raise total saving, as participants reduce voluntary contributions. These results emphasize the role of income smoothing in enabling behavioral interventions to improve long-term financial outcomes.

Predicting IPO first-day returns is inherently challenging due to the wide range of contributing factors, each with distinct statistical properties. We assess the performance of several machine learning (ML) techniques and identify XGBoost as the most statistically effective model for forecasting first-day returns. Using a comprehensive set of 863 pre-IPO variables, our high-performing predictive model accurately estimates both the direction and magnitude of IPO first-day returns. The most influential predictors include underwriter agency measures, price revision, and the free-float fraction. Using a rolling-window predictive approach, the model demonstrates substantial practical value, generating approximately $300 billion in gains from IPOs with positive first-day returns and avoiding more than $22 billion in losses from those with negative returns over the 2000–2016 period.