Abstract We provide evidence that investors with leverage constraints demand leverage for the sake of leverage. We study the equity closed-end fund (CEF) market and document a strong positive relation between fund leverage and CEF premiums, indicating that investors pay a relative premium for leverage. We perform a quasi-natural experiment and identify leverage as a causal driver of the premium. Leverage changes do not signal improved fund performance. Instead, the only benefit to investors of increased leverage is amplified exposure via greater volatility and risk exposure. We supply external validity by relating our results to the betting-against-beta factor. (JEL G12, G14, G32)

Economic, political, and public policy uncertainty affect merger and acquisition (M&A) activity. In this paper, we use Department of Justice (DOJ) and Federal Trade Commission (FTC) interventions in the M&A market to investigate whether regulatory interventions also matter. Our results support this conjecture. Using the Hoberg and Phillips (2010) similarity scores to identify product market competitors, we confirm a clear and significant DOJ/FTC regulatory enforcements' deterrence effect on future M&A transaction activity, a result robust to many alternative specifications. Additional evidence indicates that this deterrence effect is (at least partly) driven by both regulatory outcome uncertainty and regulatory intervention probability channels. Our results identify an (un)intended consequence of antitrust regulation that affects M&A activity.

Consider a bipartite network where N consumers choose to buy or not to buy M different products. This paper considers the properties of the logit fit of the N × M array of “ i ‐buys‐ j ” purchase decisions, <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"> <a:mi mathvariant="bold">Y</a:mi> <a:mo>=</a:mo> <a:msub> <a:mrow> <a:mo stretchy="false">[</a:mo> <a:msub> <a:mrow> <a:mi>Y</a:mi> </a:mrow> <a:mrow> <a:mi>i</a:mi> <a:mi>j</a:mi> </a:mrow> </a:msub> <a:mo stretchy="false">]</a:mo> </a:mrow> <a:mrow> <a:mn>1</a:mn> <a:mo>≤</a:mo> <a:mi>i</a:mi> <a:mo>≤</a:mo> <a:mi>N</a:mi> <a:mo>,</a:mo> <a:mn>1</a:mn> <a:mo>≤</a:mo> <a:mi>j</a:mi> <a:mo>≤</a:mo> <a:mi>M</a:mi> </a:mrow> </a:msub> </a:math>, onto a vector of known functions of consumer and product attributes under asymptotic sequences where (i) both N and M grow large, (ii) the average number of products purchased per consumer is finite in the limit, (iii) there exists dependence across elements in the same row or same column of Y (i.e., dyadic dependence), and (iv) the true conditional probability of making a purchase may, or may not, take the assumed logit form. Condition (ii) implies that the limiting network of purchases is sparse : only a vanishing fraction of all possible purchases are actually made. Under sparse network asymptotics, I show that the parameter indexing the logit approximation solves a particular Kullback–Leibler Information Criterion (KLIC) minimization problem (defined with respect to a certain Poisson population). This finding provides a simple characterization of the logit pseudo‐true parameter under general misspecification (analogous to a (mean squared error (MSE) minimizing) linear predictor approximation of a general conditional expectation function (CEF)). With respect to sampling theory, sparseness implies that the first and last terms in an extended Hoeffding‐type variance decomposition of the score of the logit pseudo composite log‐likelihood are of equal order. In contrast, under dense network asymptotics, the last term is asymptotically negligible. Asymptotic normality of the logistic regression coefficients is shown using a martingale central limit theorem (CLT) for triangular arrays. Unlike in the dense case, the normality result derived here also holds under degeneracy of the network graphon. Relatedly, when there “happens to be” no dyadic dependence in the data set in hand, it specializes to recently derived results on the behavior of logistic regression with rare events and i.i.d. data. Simulation results suggest that sparse network asymptotics better approximate the finite network distribution of the logit estimator. A short empirical illustration, and additional calibrated Monte Carlo experiments, further illustrate the main theoretical ideas.

Since January 2014, the U.S. Treasury has been issuing floating rate notes (FRNs). These notes pay quarterly interest based on an average of the constant maturity rates of newly issued three-month T-bills during the quarter. We show how to price such FRNs. We estimate that they have been paying excess interest between 3 and 42 basis points above the implied interest of other Treasury securities. We interpret this fact through the lens of a model where money-like assets differ in their degrees of moneyness. Additional empirical evidence supports this interpretation.

We study the role of litigation risk in M&A valuations. Specifically, we hypothesize that litigation risk leads to strategic valuations in fairness opinions (FOs) obtained in M&A transactions. Employing a regulatory shock to merger litigation risk and focusing on the most common valuation techniques – peer firm comparables and DCF analysis – we find that target-sought FOs exhibit lower valuations when litigation risk is high. The effect is concentrated in deals with greater agency conflicts between target management and outside shareholders. Furthermore, downward-biased valuations reduce appraisal litigation but are also associated with lower premiums. In contrast to prior work suggesting that target-sought FOs are used to negotiate a higher takeover price, our findings imply that they are used, at least in part, to mitigate litigation risk and facilitate successful deal completion. Our findings are relevant to academics, practitioners, and regulators interested in M&A price formation, and highlight the role litigation plays therein.