Can Mentoring Help Female Assistant Professors? Interim Results from a Randomized Trial by Francine D. Blau, Janet M. Currie, Rachel T. A. Croson and Donna K. Ginther. Published in volume 100, issue 2, pages 348-52 of American Economic Review, May 2010

Short‐term trade ideas are a component of analyst research highly valued by institutional investors. Using a novel and comprehensive database, we find that trade ideas have a stock price impact at least as large as recommendation and target price changes. Trade ideas based on expectations of future events are more informative than those identifying incomplete incorporation of past information in stock prices. Analysts with better access to a firm's management produce better trade ideas. Institutional investors trade in the direction of trade ideas. Investors following trade ideas can earn significant abnormal returns, consistent with analysts possessing valuable short‐term stock‐picking skills.

We show that market‐maker balance sheet and income statement variables explain time variation in liquidity, suggesting liquidity‐supplier financing constraints matter. Using 11 years of NYSE specialist inventory positions and trading revenues, we find that aggregate market‐level and specialist firm‐level spreads widen when specialists have large positions or lose money. The effects are nonlinear and most prominent when inventories are big or trading results have been particularly poor. These sensitivities are smaller after specialist firm mergers, consistent with deep pockets easing financing constraints. Finally, compared to low volatility stocks, the liquidity of high volatility stocks is more sensitive to inventories and losses.

Reviews various books related to accounting. "Advanced Accounting," 3rd ed., by Floyd A. Beams; "Three Degrees Above Zero: Bell Labs in the Information Age," by Jeremy Bernstein; "Wiley-Ronald Auditing Service," by D.R. Carmichael and Martin Benis; "Dictionary of Accounting Terms," by Derek French.

We propose a new framework for optimal experimentation, which we term the “A/B testing problem.” Our model departs from the existing literature by allowing for fat tails. Our key insight is that the optimal strategy depends on whether most gains accrue from typical innovations or from rare, unpredictable large successes. If the tails of the unobserved distribution of innovation quality are not too fat, the standard approach of using a few high-powered “big” experiments is optimal. However, if the distribution is very fat tailed, a “lean” strategy of trying more ideas, each with possibly smaller sample sizes, is preferred. Our theoretical results, along with an empirical analysis of Microsoft Bing’s EXP platform, suggest that simple changes to business practices could increase innovation productivity.

We placed 85,000 retail trades in six retail brokerage accounts from December 2021 to June 2022 to validate the Boehmer et al. algorithm, which uses subpenny trade prices to identify and sign retail trades. The algorithm identifies 35% of our trades as retail, incorrectly signs 28% of identified trades, and yields uninformative order imbalance measures for 30% of stocks. We modify the algorithm by signing trades using the quoted spread midpoints. The quote midpoint method does not affect identification rates but reduces the signing error rates to 5% and provides informative order imbalance measures for all stocks.

Maintaining economic output during the COVID-19 pandemic results in benefits for firm shareholders but comes at a potential cost to public health. Using store-level data, we examine how a CEO's political leaning impacts this trade-off. We document that firms with a Republican-leaning CEO experience a relative increase in store visits compared to firms with a Democratic-leaning CEO. The increase in store visits is associated with higher sales and positive abnormal stock returns. However, we also document higher COVID-19 transmission rates and more employee safety complaints in communities where establishments with higher store traffic are managed by a Republican-leaning CEO.

Using a natural experiment from a retail gasoline antitrust case, we study how asymmetric information sharing affects oligopoly pricing. Empirically, price competition softens when, following case settlement, information sharing shifts from symmetric to asymmetric, with one firm losing access to high-frequency granular rival price data. We provide theory and empirics illustrating how strategic ignorance creates price commitment, leading to higher price-cost margins. Using a structural model, we find substantial profit-enhancing effects of asymmetric information sharing. These results provide a cautionary tale for antitrust agencies regarding the potential unintended consequences of limiting price information sharing among firms.