Abstract We examine the role of race and racial concordance between financial advisors and their local community. We document significant differences in stock market participation based on community racial composition, as well as differences in the characteristics of communities served by minority advisors. Notably, minority advisors are more likely to serve racially concordant communities, which tend to be poorer. We find that racial concordance has only a modest relation with local stock market participation. However, while minority advisors are more likely to leave the industry, this relation is mitigated among advisors located in more concordant communities. (JEL G20, G50, D14, J15)

Abstract When brokers, analysts, and fund managers buy or sell stocks for their own accounts, these “access employees” of financial institutions outperform retail investors over short windows up to a month. They earn particularly high abnormal returns when they trade before earnings announcements, revisions of analyst recommendations, and large stock price changes. We also find evidence consistent with profitable front-running and information leakage around the execution of corporate insider trades and block trades by mutual funds, as well as the release of revised recommendations by analysts who work at the same brokerage firm. (JEL G12, G14, G18) Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

This study investigates whether tax-based proprietary costs associated with being a public firm (i.e., costs resulting from increased visibility to the tax authority) discourage public listing. I exploit the introduction of a mandatory disclosure requirement (FIN 48) which generated a signal to the government regarding the uncertainty of public firms’ tax positions, allowing for more carefully targeted audits. I hypothesize and find evidence of an increased propensity to go private by public, tax aggressive firms following the enactment of the disclosure rule but prior to its adoption. Cross-sectionally, the effect is stronger among firms that are more sensitive to tax-based proprietary costs. Moreover, IPOs by tax aggressive firms exhibit a relative decline after FIN 48, consistent with the disclosure requirement deterring private, tax aggressive firms from going public. Overall, my findings suggest that mandatory disclosure rules imposing tax-based proprietary costs may discourage some firms from operating as public entities.

Outside of financial crises, investors have little incentive to produce private information on banks’ short-term liabilities held as information-insensitive safe assets. The same does not hold during crises. We compare the information effects of different policy interventions. We measure information production using credit default swap spreads during the Global Financial Crisis and the European debt crisis. We study abnormal information production around major events and find that capital injections reduced abnormal information production while early European stress tests increased it. High levels of information production predict bank balance sheet contraction and higher government expenditures to support financial institutions.

Since network data commonly consists of observations from a single large network, researchers often partition the network into clusters in order to apply cluster‐robust inference methods. Existing such methods require clusters to be asymptotically independent. Under mild conditions, we prove that, for this requirement to hold for network‐dependent data, it is necessary and sufficient that clusters have low conductance, the ratio of edge boundary size to volume. This yields a simple measure of cluster quality. We find in simulations that when clusters have low conductance, cluster‐robust methods control size better than HAC estimators. However, for important classes of networks lacking low‐conductance clusters, the former can exhibit substantial size distortion. To determine the number of low‐conductance clusters and construct them, we draw on results in spectral graph theory that connect conductance to the spectrum of the graph Laplacian. Based on these results, we propose to use the spectrum to determine the number of low‐conductance clusters and spectral clustering to construct them.

Macroeconomic development remains an important policy goal because of its ability to lift entire populations out of poverty. In our review of the literature, we emphasize that the best way to achieve this objective is to embrace a synthesis of methods and ideas, with the science of experiments as a unifying feature. Randomized controlled trials need representative data and structural modeling, and macro models need to be designed and disciplined to the realities and data of developing-country economies. Macroeconomic models have key lessons for gathering and analyzing micro evidence and for moving to an evaluation of macro policy. Resource constraints, heterogeneity, general equilibrium effects, obstacles to trade, dynamics, and returns to scale can all play key roles. A synthesis for macro development is well under way. (JEL C93, D00, E00, O10, O11, O12)

The financial intermediation wedge of the banking sector used to co-move positively with the federal funds rate, but the post-GFC era saw a disconnect between them. We develop a flexible price dynamic general equilibrium with banks’ liquidity creation to offer an explanation. In a corridor system, the financial wedge and policy rate are shown to co-move, and the pass-through of monetary policy onto both inflation and output obtains. However, the post-GFC floor system obviates the need for the financial wedge to cover the cost of obtaining reserves, so the wedge and the policy rate indeed disconnect in equilibrium; furthermore, we show that the disconnect obstructs monetary expansions from generating inflation. In this environment, tightening bank capital requirement leads to disinflationary pressure. Money-financed fiscal expansions that subsidise non-bank sectors’ borrowing costs improve output and reduce default risks but increase inflation. The model uses banks’ liquidity creation via credit extension to provide a rationale for both the pre-pandemic disinflation and the post-pandemic inflation. The results hold both on the dynamic paths and in the steady state, and the role of money enlarges the Taylor rule determinacy region.

Government interventions as a solution to systemic banking crises continue to receive wide criticism. The new regulatory frameworks advocate banks’ bail-ins and resolutions that do not require governments’ involvement. However, as the recent events with Credit Suisse and Silicon Valley Bank show, the government still plays an active role in rescuing and resolving the bank's problems. We use the financial stability model of Goodhart et al.’s (2005, 2006a) to analyze the effects of various bank policy interventions on banks’ performance during the crisis rescue phase. We then explore whether those interventions work effectively in facilitating bank recovery and whether they reduce systemic risk in the long run. We use a unique granular bank-level dataset from 22 advanced economies covering the 1992–2017 period. We find that bank recapitalization without debt resolution measures does not resolve bank distress. The empirical results document that “bad-bank” resolution is positively correlated with a bank’s recovery as well as lower systemic risk. Those findings contribute to the ongoing debate on the optimal bank resolution architecture during systemic events.

Abstract The vast majority of managers’ earnings forecasts are issued concurrently (i.e., bundled) with their firm’s current earnings announcement. We document a predictable bias in these forecasts—the forecasts fail to fully reflect the persistence of the current earnings surprise. Specifically, we find that managers issue (1) optimistically biased forecasts alongside negative earnings surprises and (2) pessimistically biased forecasts alongside large positive earnings surprises. Bayesian updating implies this bias could be unintentional, but we find that the bias is stronger when managers have greater incentives and fewer constraints to issue biased forecasts, suggesting that, to some extent, the bias might be intentional. Relatedly, although managers typically have better information about their firm’s earnings than analysts, we show that analyst reliance on these biased management forecasts represents a mechanism (and an alternative interpretation) for a similar analyst underreaction to current earnings attributed in the literature to analysts’ cognitive bias. We also find that, on average, investors do not appear to initially understand the bias in these forecasts but do unravel it over longer windows. However, investors more quickly unravel the bias when the manager has a history of issuing biased forecasts and when the firm has more sophisticated investors. Overall, we document that managers’ forecasts appear to repeatedly underweight the persistence of current earnings surprises, are biased in ways that improve investors’ perceptions of managers’ ability, and that this behavior concentrates in subsamples where outsiders have a harder time recognizing any bias.