The VPIN metric (Easley et al. 2012b) aims to detect and predict the toxicity of order flow. This paper examines the sensitivity and robustness of VPIN to the choice of trade classification scheme, which is the major input used to compute VPIN. We compare deterministic trade-by-trade classification approaches with results computed using a newly proposed heuristic approach, bulk volume classification. We find substantial differences for all levels of aggregation: trade classification, order imbalance, VPIN and identifying “toxic periods”. We also find that the detection of toxic periods does not yield consistent results in more than 60% of cases. But regression analysis can identify volume and return volatility as parameters that contribute to higher levels of sensitivity.

This paper analyzes the risk dynamics surrounding convertible bond offerings (CBOs) and Seasoned Equity Offerings (SEOs). As convertible bonds are commonly believed to be very effective at mitigating adverse selection or overinvestment problems we would expect differing risk and return patterns for convertible bond and seasoned equity issuers. By analyzing 1148 convertible bond offerings and comparing them to 2905 seasoned equity offerings, we show however that for both issuer types the systematic risk increases prior to issuance and drops sharply thereafter. This result is consistent with the notion of exercising real options, as growth options are always riskier than the underlying assets and exercising them at issuance causes an immediate drop in risk. The real option framework and the proposed dynamics of systematic risk also provide a rational explanation for the negative announcement effect, as well as any long-term underperformance subsequent to the CBO and the SEOs.

CDS spreads contain information about expected credit risk, but how accurate is this information when uncertainty about credit risk arises? We document that CDS spreads of firms on negative credit watch (review for downgrade) change systematically into the direction implied by the ex-ante uncertain review outcomes: they widen before reviews concluded with downgrades and tighten before reviews concluded with confirmations. Moreover, CDS spreads widen before deteriorations of corporate credit risk such as leverage, interest rate coverage and Altman Z-score that occur over the review period. Importantly, we do not find similar patterns for firms’ stock returns during the review period. The evidence is novel and suggests that the CDS market not only reflects the increase in uncertainty but also accurate forward-looking information about the outcomes of the uncertainty-inducing events.

From 2005 to 2015, the 25 largest global financial institutions paid combined more than $285 billion in legal penalties. We examine the reaction of banks' stocks, bonds, and credit default swaps to the announcements of monetary penalties. We observe a reduced default risk and lower financing costs, as well an increase in the stock market valuation, suggesting that banks benefit from settling lawsuits. The positive reaction is likely driven by the resolution of uncertainty surrounding these proceedings. While the sued bank's systemic risk increases in the size of the relative monetary penalty, we also document positive spillover effects to other banks facing pending lawsuits with the same plaintiff, demonstrating the systemic effect of law enforcement against banks. Furthermore, banks appear to correctly anticipate penalties, as they are cash flow-effective but not income-effective in the year they are announced.

This paper focuses on the relationship between the reader's information-extracting costs of finance articles and the article's number of citations. The reader's information-extracting costs are measured using three metrics: (i) the Flesch-Kincaid readability score, (ii) the article's length, and (iii) the number of complex words. Based on a sample of more than 14,000 full text articles published between 2000 and 2016 in 16 finance journals, we show that the information-extracting costs of finance journals have significantly increased over time, while the topics of these articles, determined by machine-learning topic modeling, remained relatively constant. We find a positive correlation between the reader's information-extracting costs and the number of citations achieved by a paper for articles that are published in the top-three finance journals (JF, JFE, RFS), but do not observe this pattern for articles published in other major finance journals.