Buy Side

Kevin Lewis

March 24, 2020

Do Fund Managers Misestimate Climatic Disaster Risk
Shashwat Alok, Nitin Kumar & Russ Wermers
Review of Financial Studies, March 2020, Pages 1146-1183


We examine whether professional money managers overreact to large climatic disasters. We find that managers within a major disaster region underweight disaster zone stocks to a much greater degree than distant managers and that this aversion to disaster zone stocks is related to a salience bias that decreases over time and distance from the disaster, rather than to superior information possessed by close managers. This overreaction can be costly to fund investors for some especially salient disasters like hurricanes and tornadoes: a long-short strategy that exploits the overreaction generates a significant DGTW-adjusted return over the following 2 years.

Where's the Greenium?
David Larcker & Edward Watts
Journal of Accounting and Economics, forthcoming


In this study, we investigate whether investors are willing to trade off wealth for societal benefits. We take advantage of unique institutional features of the municipal securities market to provide insight into this question. Since 2013, states and other governmental entities have issued over $23 billion of green bonds to fund eco-friendly projects. Comparing green securities to nearly identical securities issued for non-green purposes by the same issuers on the same day, we observe economically identical pricing for green and non-green issues. In contrast to a number of recent theoretical and experimental studies, we find that in real market settings investors appear entirely unwilling to forgo wealth to invest in environmentally sustainable projects. When risk and payoffs are held constant and are known to investors ex-ante, investors view green and non-green securities by the same issuer as almost exact substitutes. Thus, the greenium is essentially zero.

Does FOIA Foil the SEC's Intent to Keep Investigations Confidential?
Braiden Coleman et al.
Management Science, forthcoming


The Securities Exchange Commission (SEC) has a long-standing policy to keep formal investigations confidential. In this study, we examine the extent to which compliance with the Freedom of Information Act (FOIA) provides investors with information about on-going SEC investigations. We exploit a unique empirical setting whereby the SEC denies FOIA requests due to ongoing enforcement proceedings (hereafter, exemption denials). We find that exemption denials predict a substantial number of ongoing and future SEC investigations. Exemption denials are also associated with significant negative future abnormal returns, which is consistent with exemption denials providing a noisy public signal that allows certain sophisticated investors to earn future abnormal returns. Overall, our findings suggest that information transparency laws, such as FOIA, have the potential to limit the SEC's ability to maintain effective and confidential investigations.

Under his thumb the effect of president Donald Trump's Twitter messages on the US stock market
Heleen Brans & Bert Scholtens
PLoS ONE, March 2020


Does president Trump's use of Twitter affect financial markets? The president frequently mentions companies in his tweets and, as such, tries to gain leverage over their behavior. We analyze the effect of president Trump's Twitter messages that specifically mention a company name on its stock market returns. We find that tweets from the president which reveal strong negative sentiment are followed by reduced market value of the company mentioned, whereas supportive tweets do not render a significant effect. Our methodology does not allow us to conclude about the exact mechanism behind these findings and can only be used to investigate short-term effects.

What Do Short Sellers Know?
Ekkehart Boehmer et al.
Review of Finance, forthcoming


Using NYSE short-sale order data, we investigate whether short-sellers' informational advantage is related to firm earnings and analyst-related events. With a novel decomposition method, we find that while these fundamental event days constitute only 12% of sample days, they account for over 24% of the overall underperformance of heavily shorted stocks. Importantly, short-sellers use both public news and private information to anticipate news regarding earnings and analysts. Shorting's predictive ability remains significant after controlling for information in analyst actions, and displays no reversal patterns, indicating that short-sellers know more than analysts, and the nature of their information is long term.

Prime (information) brokerage
Nitish Kumar et al.
Journal of Financial Economics, forthcoming


We show that hedge funds gain an information advantage from their prime broker banks regarding the banks' corporate borrowers. The connected hedge funds make abnormally large trades in the stocks of borrowing firms prior to loan announcements, and these trades outperform other trades. The outperformance is particularly strong for trades of hedge funds that have high revenue potential for prime broker banks. These informed trades appear to be based on information not just about the loan itself but also about firms' fundamentals such as future earnings. Finally, we find evidence suggesting that equity analysts inside the banks are one potential conduit of information transfer.

Do Business Ties Generate Private Information? Evidence from Institutional Trading Around M&A Announcements
David Pedersen
Finance Research Letters, forthcoming


Business ties generate private information for institutional investors related to a firm's 401(k) trustee. These investors strategically trade a bidder's stock around the announcement of a deal, reducing holdings ahead of value-destroying deals and increasing their positions before value-creating ones. Superior trading ability does not explain this behavior as these same investors do not exhibit similar trading behavior in firms with which they do not have a business tie.

Does the Dodd-Frank Act reduce the conflict of interests of credit rating agencies?
Francesca Toscano
Journal of Corporate Finance, forthcoming


I compare issuer-paid ratings, represented by Standard & Poor's (S&P) to investor-paid ratings, represented by Egan-Jones Ratings Company (EJR), after the passage of the Dodd-Frank Act. My results show that S&P ratings are lower than EJR ratings in the post-Dodd-Frank period, especially for firms able to generate revenue to credit rating agencies (CRAs); i.e., firms with a large bond issuance, larger firms, and low-performing firms. Further, I find evidence of a greater accuracy of S&P ratings relative to EJR ratings in the post-Act period as shown by the lower probability of large credit rating changes and rating reversals. Finally, I show that issuer-paid ratings are more concerned about providing timely ratings in the post-Dodd-Frank period, thus protecting their reputation as leading information providers, than investor-paid ratings. My results are robust to a wide battery of robustness tests.

What you see is not what you get: The costs of trading market anomalies
Andrew Patton & Brian Weller
Journal of Financial Economics, forthcoming


Is there a gap between the profitability of a trading strategy on paper and that which is achieved in practice? We answer this question by developing a general technique to measure the real-world implementation costs of financial market anomalies. Our method extends Fama-MacBeth regressions to compare the on-paper returns to factor exposures with those achieved by mutual funds. Unlike existing approaches, ours delivers estimates of all-in implementation costs without relying on parametric microstructure models or explicitly specified factor trading strategies. After accounting for implementation costs, typical mutual funds earn low returns to value and no returns to momentum.

Auditors are Known by the Companies They Keep
Jonathan Cook et al.
Journal of Accounting and Economics, forthcoming


We study the role of reputation in auditor-client matching. Using 1.2 million employment records from US broker-dealers, we find that broker-dealer clients of the same auditor have similar financial adviser misconduct profiles. Our estimates indicate that variation in client misconduct behavior is nearly half as important as variation in client size in explaining matches. Auditors adjust their portfolios when presented with new information about client behavior, and those with the most significant reputation concerns are least likely to deal with high misconduct clients. Finally, we find that an auditor's reputation for accepting high misconduct clients predicts their new clients' future misconduct. Together, our results present new evidence on how reputation affects audit relationships, and the consequences of auditors' reputation concerns for client behavior. Our results also indicate an unintended consequence of audit mandates: non-discerning auditors emerge to serve clients with low endogenous demand for auditing.

Empirical Asset Pricing via Machine Learning
Shihao Gu, Bryan Kelly & Dacheng Xiu
Review of Financial Studies, forthcoming


We perform a comparative analysis of machine learning methods for the canonical problem of empirical asset pricing: measuring asset risk premiums. We demonstrate large economic gains to investors using machine learning forecasts, in some cases doubling the performance of leading regression-based strategies from the literature. We identify the best-performing methods (trees and neural networks) and trace their predictive gains to allowing nonlinear predictor interactions missed by other methods. All methods agree on the same set of dominant predictive signals, a set that includes variations on momentum, liquidity, and volatility.

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