We construct a present-value model with a rational bubble identified using stock market data and survey expectations. The model captures the 1970s “nifty-fifty” bubble, the dot-com bubble, the bubble prior to the 2007–2009 recession, a COVID-19 bubble, and a recent bubble possibly linked to artificial intelligence. When bubble-adjusting the price–dividend ratio, we no longer find explosive dynamics and excess volatility in stock prices. To examine the macroeconomic implications of a bubble, we use the bubble innovation as an instrument in a structural vector autoregression. The results show that a positive bubble shock is expansionary and leads to a higher monetary policy rate.

The PCAOB adopted critical audit matters (CAMs) to meet public demand for informative audit disclosure, but stakeholders are concerned this goal has not been achieved. We explore this disconnect via interviews with 30 highly experienced auditors. We find that audit firms expended considerable resources to implement CAM best practices. However, overwhelming institutional pressure gave rise to informal rules of thumb that prioritize symbolic comfort over substantive change. The first is don’t be an outlier , so auditors defer to the national office to ensure conformity and avoid PCAOB scrutiny. The second is report the “right” number of CAMs by never reporting zero and reporting at least one recurring CAM. The third is avoid surprises by communicating with the client to ensure that CAMs do not contain original information and allowing management to preempt auditor disclosures. Collectively, these rules yield CAMs that comply with PCAOB standards but do not provide new information and instead maintain the status quo.

Machine learning (ML) could strengthen banks’ resilience through improved credit risk screening and ultimately benefit financial stability. Yet, ML adoption in banking remains limited, with simpler linear models still predominating. We argue that the emergence of highly flexible ML models has created a new challenge for forecasting tasks: ‘model multiplicity’—where equally accurate ML models at the aggregate level produce divergent individual-level predictions (‘predictive multiplicity’) or differ in their decision surfaces (‘procedural multiplicity’). These issues raise fundamental questions: Why should an individual or firm be subject to an adverse credit risk model outcome when there is an equally accurate model that treats them more favorably? Using the world’s largest loss database of corporate defaults, we examine these two phenomena in recovery rate ( RR ) modeling and propose heterogeneous ML ensembles as a natural solution. By combining predictions and decision surfaces from multiple well-performing ML models, ensembles mitigate risks associated with predictive multiplicity by ensuring that borrowers are not subject to the fluctuations of a single model, and reduce procedural multiplicity by providing a robust measure of features that ultimately improve out-of-sample RR predictions. By addressing the ‘multiplicity of good models’ problem, our study emphasizes the importance of model stability and provides new insights for the future development of ML models.

The sensitivity of the external finance premium to firms’ net-worth-to-capital ratio is central to the strength of the financial accelerator, yet direct firm-level evidence remains scarce. We estimate this elasticity using balance sheet and income statement data for Swiss nonfinancial firms over 1998–2016. To address the endogeneity of net worth, we employ two complementary instrumental variable strategies: one based on firms’ non-operating income and the other a shift-share design that interacts predetermined exposure to financial income with aggregate dividend returns. Mapping the estimated elasticity into the costly state verification framework as implemented by Bernanke et al. (1999) yields structural monitoring costs of about one quarter of firms’ gross return on capital, with estimates ranging from 0.15 to 0.35 across specifications. Our results provide direct firm-level support for the financial accelerator mechanism and imply monitoring costs of the same order of magnitude as the benchmark calibration of Bernanke et al. (1999).

We document widespread use of personal financial advice among retail investors. Individuals seek competent and trusted sources for financial advice among their family and friends. Investors who provide advice to family and friends are positively selected and emphasize the reputational costs of giving risky financial advice. While previous studies have shown that advice shared on social media promotes active trading, we show that personal financial advice encourages investing in funds over single stocks. Our evidence complements the existing literature on financial advice in online social networks by highlighting differences in incentives and outcomes of advice to close personal connections.

Alex M. Thomas of School of Arts and Sciences, Azim Premji University reviews “Relocating Development Economics: The First Generation of Modern Indian Economists” by Maria Bach. The Econlit abstract of this book begins: “Explores the work of the Early Nationalists, the first generation of modern economists in India in the mid- to late-nineteenth century, focusing on how they produced forward-thinking knowledge on economic development.”