Applying a "co-search" algorithm to Internet traffic at the SEC's EDGAR website, we develop a novel method for identifying economically-related peer firms. Our results show that firms appearing in chronologically adjacent searches by the same individual (Search Based Peers or SBPs) are fundamentally similar on multiple dimensions. In direct tests, SBPs dominate GICS6 industry peers in explaining cross-sectional variations in base firms' out-of-sample: (a) stock returns, (b) valuation multiples, (c) growth rates, (d) R&D expenditures, (e) leverage, and (f) profitability ratios. We show that SBPs are not constrained by standard industry classification, and is more dynamic, pliable, and concentrated. Our results highlight the potential of the collective wisdom of investors - extracted from co-search patterns - in addressing long-standing benchmarking problems in finance.
Relative total shareholder return (rTSR) is increasingly used to incentivize and evaluate managers. Although compensation experts acknowledge a primary objective is to filter shocks unrelated to managerial performance, we document that a significant subset of firms, who choose index-based rTSR-benchmarks in lieu of specific peers, do not adequately achieve this objective. Structural estimates reveal that noisy-benchmark selection implies significant negative performance consequences. Reduced-form analysis shows that a firm's choice of index-based benchmarking is 1) driven by its compensation consultants' systematic tendencies and governance-related frictions, and 2) associated with lower ROA, suggesting noisy-benchmark selection is a novel indicator of weak governance.
We apply modern machine learning techniques to characterize disclosure misclassification by public companies. We find that 12-26% of disclosures are misclassified; those concerning material definitive agreements, executive or director turnover, and delistings are most commonly misclassified. Using EDGAR search traffic data, we provide evidence that misclassification is associated with less investor attention. Through this attention channel, misclassification leads to a significant and persistent impact on absolute market returns. For misclassified filings, search traffic is 4-12% lower and absolute market reactions are 46-79 bps smaller. Consistent with strategic motives, misclassification is more likely for negative news and when market attention is high.
We estimate and test a model of voluntary disclosure in which a manager's information set is uncertain (Dye 1985; Jung and Kwon 1988). In this model, a manager makes his disclosure decision to maximize the market price, but sometimes, for exogenous reasons, he cannot or is not willing to disclose. We offer a flexible framework to measure the prevalence of unobservable disclosure frictions and the quality of managers' private information. More broadly, the method can be used to test for voluntary disclosure in datasets featuring an option to withhold. We also develop theory-based tests for detecting whether a firm is reporting strategically. At the firm level, we reject strategic reporting for between 1/3 to 2/3 of the sample of firms. Finally, estimating the model with quarterly management guidance, we document that firms face a disclosure friction between 30% to 46% of the time. Conditional on not facing a friction, firms strategically withhold between 4.3% to 20.7% of the time. To aid policymakers, these estimates predict that the level of voluntary forecasts will increase by 2.6% to 13.5% in a counter-factual world without strategic information withholding.
In knowledge-based economies, many businesses enterprises defy traditional industry boundaries. In this study, we evaluate six "big data" approaches to peer firm identifications and show that some, but not all, "wisdom-of-crowd" techniques perform exceptionally well. We propose an analytical framework for understanding when crowds can be expected to provide wisdom and show, theoretically and empirically, that their efficacy is related to crowd sophistication and task complexity. Consistent with this framework, we find that a "crowd-of-crowds" approach, which combines EDGAR user co-searches and analyst co-coverage, dominates other state-of-the-art methods for identifying investment benchmarks.
This paper examines whether investors correctly distinguish qualitative information from promotional language in press releases related to material events of US public firms. For a variety of material events, firms are required to issue a Form 8-K, but 37% of the time also voluntarily issue a press release concerning the same event, half of which occur prior to the 8-K filing date. Using textual analysis, I find that firms are more likely to issue a press release if the underlying 8-K tone is positive, and that tonal differences between the 8-K and the press release are driven in part by quotes from officers. I also find economically significant responses in firms' stock returns to tonal language in the 8-K, as well as to tonal differences between the two disclosures. To verify whether my strategy of comparing the press release against the 8-K is isolating the effects of promotional language or additional information, I test and find evidence of an initial positive reaction but subsequent negative drift from positively toned press releases. This implies that investors may have initially responded to both information and spin. Nominating investor inattention as a possible mechanism for overreaction, I use novel search traffic micro-data from the SEC EDGAR website and detect lower 8-K search intensity in the presence of a press release. Together, my results are consistent with some investors overestimating the degree of substitutability between the two disclosures and thus failing to readjust expectations accordingly.
We identify the presence of high frequency arbitrageurs in the US treasury market through intraday exchange outages. Evidence complementing our identification shows that order cancellation behavior also changed during the outage, consistent with arbitrageurs' profit maximization motives. Our estimates suggest that arbitrageurs represent approximately 69 to 94% of the quote depth in the spot treasury market. In addition, their presence seems to have large effects for the bid-ask spread of the 30-year treasury bond, which is the most illiquid product within its class.[Motivating Graph]