Knowledge that Transforms

ABSTRACT We train a machine learning method on a class of informed trades to develop a new measure of informed trading, informed trading intensity (ITI). ITI increases before earnings, mergers and acquisitions, and news announcements, and has implications for return reversal and asset pricing. ITI is effective because it captures nonlinearities and interactions between informed trading, volume, and volatility. This data‐driven approach can shed light on the economics of informed trading, including impatient informed trading, commonality in informed trading, and models of informed trading. Overall, learning from informed trading data can generate an effective informed trading measure.

ABSTRACT Using novel data from the pharmaceutical industry, we study product prices and innovation around mergers. Exploiting within‐deal variation in product market consolidation, we show that prices increase more for drugs in consolidating markets than for matched control drugs. Estimates indicate a 2% average price effect that persists for about one year. Price increases expand with acquirer‐target product similarity and are more pronounced within less competitive product markets with fewer players and no generic competition. Examination of trade‐offs reveals these deals generate significant shareholder value. They also spur labeling and other manufacturing‐related innovation, but not the development of new drugs.

ABSTRACT The psychology literature documents that individuals derive current utility from their beliefs about future events. We show that, as a result, investors in financial markets choose to disagree about both private information and price information. When objective price informativeness is low, each investor dismisses the private signals of others and ignores price information. In contrast, when prices are sufficiently informative, heterogeneous interpretations arise endogenously: most investors ignore prices, while the rest condition on it. Our analysis demonstrates how observed deviations from rational expectations (e.g., dismissiveness, overconfidence) arise endogenously, interact with each other, and vary with economic conditions.

ABSTRACT We combine state minimum wage changes with individual‐level income and credit data to estimate the effect of wage gains on the debt of low‐wage workers. In the three years following a $0.88 minimum wage increase, low‐wage workers experience a $2,712 income increase and a $856 decrease in debt. The entire decline in debt comes from less student loan borrowing among enrolled college students. Credit constraints, buffer‐stock behavior, and other rational channels cannot explain the reduction in student debt. Our results are consistent with students perceiving a utility cost of borrowing student debt arising from mental accounting.

ABSTRACT All‐cash homebuyers account for one‐third of U.S. home purchases between 1980 and 2017. We use multiple data sets and research designs to robustly estimate that mortgaged buyers pay an 11% premium over all‐cash buyers to compensate home sellers for mortgage transaction frictions. A dynamic, representative‐seller model implies only a 3% premium, which would suggest an 8% puzzle. Accounting for heterogeneity in selling conditions explains half of this difference, but a puzzle holds in conditions with high transaction risk. An experimental survey of U.S. homeowners replicates these patterns and suggests that belief distortions can explain the puzzle in these high‐risk states.

ABSTRACT Examining the contractual disclosures during the sale of private‐label residential mortgage‐backed securities before the 2008 financial crisis, we find that textual contents in the risk‐factor section predict subsequent losses and yet were not reflected in pricing. Insurance companies, especially life insurers and insurers with low regulatory capital ratios, are more exposed to textual risks. Consistent with issuers hedging litigation risks with disclosure, we find that textual contents are associated with second‐lien underreporting and preissuance written communications. Overall, we find that investors neglected risks in the purportedly safe assets before the crisis.

ABSTRACT This paper shows that the liquidation value of collateral depends on the interdependency between borrower and collateral risk. Using transaction‐level data on short‐term repurchase agreements (repo), we show that borrowers pay a premium of 1.1 to 2.6 basis points when their default risk is positively correlated with the risk of the collateral that they pledge. Moreover, we show that borrowers internalize this premium when making their collateral choices. Loan‐level credit registry data suggest that the results extend to the corporate loan market as well.

ABSTRACT Policymakers frequently use guarantees to mitigate panic‐based runs in the financial system. We analyze a binary‐action coordination game under the global games framework and propose a novel intervention program that screens investors based on their heterogeneous beliefs about the system's stability. The program only attracts investors who are at the margin of running, and their participation boosts all investors' confidence in the financial system. Compared with government guarantee programs, our proposed program is as effective at mitigating runs but features two advantages: it costs less to implement and it is robust to moral hazard.

ABSTRACT Political identity and partisanship are salient features of today's society. Using deeds records and voter rolls, we show that current residents are more likely to sell their homes when opposite‐party neighbors move in nearby than when unaffiliated or same‐party neighbors do. This is especially true when the new neighbors are politically active, consistent with an animosity between parties mechanism. We conclude that affective polarization is not limited to purely political settings and affects one of the household's most important financial decisions, their home transactions.

ABSTRACT We document an unprecedented brain drain of Artificial Intelligence (AI) professors from universities from 2004 to 2018. We find that students from the affected universities establish fewer AI startups and raise less funding. The brain‐drain effect is significant for tenured professors, professors from top universities, and deep‐learning professors. Additional evidence suggests that unobserved city‐ and university‐level shocks are unlikely to drive our results. We consider several economic channels for the findings. The most consistent explanation is that professors' departures reduce startup founders' AI knowledge, which we find is an important factor for successful startup formation and fundraising.