Knowledge that Transforms

Sell-side quantitative equity research analysts (Quants) conduct econometric analyses of stock returns to uncover market anomalies and assist equity analysts and institutional clients with stock selection. We present novel evidence that establishes their role in helping analysts and mutual fund clients discover market anomalies and capital markets evolve toward greater pricing efficiency. Specifically, we find that analysts and mutual fund clients with greater access to Quants make recommendations and trades that reveal greater knowledge of anomalous cross-sectional return predictability. More importantly, cross-sectional return predictability is weaker in stocks that have higher coverage (ownership) by analysts (mutual fund clients) with access to Quants, and strengthens when quasi-exogenous brokerage house closures reduce the availability of Quants.

This study examines the capital-market effects of tipper-tippee insider trading laws. To do so, I exploit the unexpected decision issued by the Court of Appeals for the Second Circuit in U.S. v. Newman 773 F.3d 438, which reduced legal jeopardy for Second Circuit-based market participants prior to being overturned. Consistent with Newman constraining insider trading enforcement, I find strong evidence of plausible insider trading in the Second Circuit following the ruling. I also document a substantial reduction in general trading activity in Second Circuit stocks, as well as an increase in daily quoted spreads and the price impact of trading. These findings are consistent with unchecked insider trading increasing transaction costs and crowding-out investors. In total, my results show that tipper-tippee insider trading restrictions play an important role in bolstering market integrity, and that market participants can to some degree counteract insider trading when public regulation is constrained.

Researchers often use regression-based x-Scores (e.g., conservatism C-Score , misstatement F-Score ) from a stage 1 model as a dependent variable in stage 2. We argue that this x-Score analysis can cause coefficient biases and interpretation problems because (1) x-Score does not capture new sources of variation, and (2) the estimates often hinge on unacknowledged technical assumptions. Instead, we recommend that researchers include the test variables and the relevant controls in stage 1, obviating the need for an x-Score . In replication analyses, some important published findings change after we remove the coefficient bias caused by the use of x-Score as a dependent variable.

The Securities and Exchange Commission encourages the presentation of information or data in graphical form to improve users' ability to understand financial disclosures. We find a dramatic increase in the disclosure of both qualitative and quantitative infographics in 10-K filings over time and substantial cross-sectional variation in firms' choices regarding image types, data content, and the placement of infographics within 10-Ks. We provide evidence on factors associated with firms’ use of infographics and explore the persistence with which they are disclosed over time. Finally, we investigate the relation between the use of infographics and uncertainty in capital markets.

A CEO's short horizon and associated myopic actions are typically viewed as detrimental to the firm. In contrast, studying a voluntary disclosure model wherein capital market and product market strategic considerations are in play, we show that the CEO's myopic behavior can improve a firm's long-term value. In particular, the disclosures of a long-horizon CEO are seen as being entirely focused on the firm's interests and thus as being exploitative of customers. A short-horizon CEO myopically focused on short-term stock price is less aligned with the firm and, consequently, her disclosures are more customer friendly. As a corollary, when no disclosure is forthcoming, customers are less skeptical that the myopic CEO is withholding information to exploit them. This improves customers' willingness to pay with a myopic CEO, leading to higher firm profitability. The paper also layers in compensation design to derive the optimal degree of managerial short-term focus to induce.

Li, Watts, and Zhu (2024) provide evidence that retail investors trade in response to financially material ESG news. This evidence is consistent with retail investors trading in response to the financial implications of ESG-related information in much the same way that they trade in response to the financial implications of other information. The authors suggest that their evidence is inconsistent with retail investors making investment decisions based on their nonpecuniary preferences. I suggest that their research design is constructed to identify financially motivated trading and is not well suited to drawing inferences about retail investors’ nonpecuniary investment decisions.

We examine the impact of increasing whistleblowing bounties on whistleblowers' strategy and regulatory efficiency in detecting fraud. Our analysis shows the regulator extracts information about the incidence of fraud from whistleblowers' actions, and the quality of such information depends on the size of whistleblowing bounties. With a larger bounty, upon receiving a whistleblowing report, the quality of the regulator's information about fraud deteriorates, whereas upon observing no whistleblowing, the information quality about no fraud improves. Although the informational improvement upon no whistleblowing has not been widely discussed, we demonstrate it is a key determinant of the optimal whistleblowing program. We show, considering the informational value of whistleblowing and no whistleblowing, the regulator should set the bounty to encourage more whistleblowing when the prior belief of fraud is stronger and the insider is better informed. Our analysis generates policy and empirical implications for designing and studying whistleblowing programs.

This paper examines whether and how public environmental enforcement affects private lenders’ monitoring efforts and the effectiveness of such monitoring. We capture lender monitoring using environmental covenants in loan agreements. Consistent with the prediction that stringent public environmental enforcement increases lenders’ monitoring incentives, we find that in the presence of higher environmental regulatory enforcement intensity, lenders are more likely to use environmental covenants when lending to polluting borrowers and when the loans are secured by real property collateral. Moreover, consistent with the prediction that stringent public environmental enforcement facilitates lender monitoring, we find that environmental covenants are more effective in reducing borrowers’ toxic chemical releases when environmental regulatory enforcement is stronger. Taken together, our findings corroborate the importance of public environmental enforcement in inducing lenders’ monitoring efforts, as well as the joint role of public enforcement and private lender monitoring in curbing corporate pollution.

A signal that identifies asset pricing bubbles would be valuable so investors could reposition their portfolios to weather the bubble, yet ex ante bubble identification has proven illusory. Arif and Sul (2024) study whether industry-level investment in net operating asset (NOA) accruals is an ex ante accounting-based signal that predicts bubbles. Using industry-level price run-ups across 49 countries, they find that NOA accruals predict a higher likelihood of a future crash, lower future returns, and larger analyst forecast errors, especially following run-up periods. The authors attribute these patterns to sentiment-driven overinvestment. My discussion summarizes the contributions of Arif and Sul's findings to the asset pricing bubbles, behavioral finance, and aggregate accruals literatures. I also outline empirical challenges faced by studies investigating bubbles and recommend approaches to further strengthen inferences. Finally, I propose opportunities for future research to integrate deeper accounting knowledge into bubble research.

We study the optimal dynamic contract that provides incentives for an agent (e.g., SPAC sponsor, VC general partner, CTO) to exploit investment opportunities/targets that arrive randomly over time via a costly search process. The agent is privy to the arrival as well as to the quality of the target and can take advantage of this for rent extraction during the search process and the ensuing production. The optimal contract provides the agent with incentives for timely and truthful reporting via a time-varying threshold for investment and an internal charge for the time spent on search. In the equilibrium, as time elapses, the charge becomes progressively higher while the investment threshold is progressively lower, resulting in overinvestment at a time-varying degree. Our model generates empirically testable predictions regarding investments (such as M&As, hedge fund activism, VC investing, SPACs, and internal innovations), linking the degree of overinvestment to observable firm and industry characteristics. • Optimal dynamic contract for the search and use of investment opportunities/targets. • Adverse selection arises regarding the arrival of targets and their quality. • Optimal contract involves a progressively declining hurdle for investments. • The investments hurdle is always below the first-best, implying overinvestment. • Our results shed light on internal innovation, M&A, SPACs, VC and HFA investments.