When SEC disclosure guidelines are not bright-lined, do firms use discretion strategically? To answer this question, we apply modern machine learning techniques to characterize disclosure misclassification by public companies. We find that 17% of disclosures are misclassified, and those concerning material definitive agreements, executives or directors contracts and turnover, and delisting notices are most commonly misclassified. Using EDGAR search traffic data, we provide evidence that misclassification successfully reduces investor attention. Through attention, misclassification leads to a significant and persistent impact on absolute market reactions. For misclassified filings, search traffic is 45% lower and absolute market reactions are 79 bps smaller. Finally, we find that managers strategically misclassify disclosure if the news is negative and when market attention is high.
This paper studies the trade-offs in information content learned from forecasts of earnings announcement dates obtained from a mandatory forecasting regime (China) versus a voluntary regime (US). First, we show that forecasts in China, like the US, embed unrevealed firm performance information and that investors respond to forecast disclosures in a manner consistent with their information content. Furthermore, unique to the US, both the voluntary forecast decision and the timing of the forecast convey incremental information about firm performance. Unique to China, the timing of the mandatory forecast is uniformly fixed and on average much earlier than their endogenous counterparts in the US. This feature enables us to estimate the trade-off between timeliness and accuracy of forecasts -- a 10% increase in timeliness of forecasts increases absolute forecast errors by 7%. Despite this trade-off, 25% of earnings-related news is learned on the date of the initial forecast in China compared to 11% in the US relative to news learned on the date of the earnings announcement. Finally we ask whether China firms who were ostensibly the target of the mandatory regulation -- the likely silent firms under a voluntary regime do in fact provide informative forecasts under the mandatory regime. We construct and estimate a linear forecasting model and show that the precision of the manager's private information is associated with the decision to forecast in the US. Using this parameter, we identify the counter-factually silent China firms and find that these firms also provide useful information via their forecasts. Our collective evidence informs policymakers of a) the role of the uniform timing of the mandatory forecast in improving the timeliness of information concerning firm performance and b) suggests that mandatory forecasting induced otherwise silent firms to provide useful information, but at the loss of information from the voluntary disclosure choice itself.
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.
We compare the performance of a comprehensive set of alternative peer identification schemes used in economic benchmarking. Our results show the peer firms identified from aggregation of informed agents' revealed choices in Lee, Ma, and Wang (2014) perform best, followed by peers with the highest overlap in analyst coverage, 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. Conversely, peers firms identified by Google and Yahoo Finance, as well as product market competitors gleaned from 10-K dis-closures, turned in consistently worse performances. We contextualize these results in a simple model that predicts when information aggregation across heterogeneously informed individuals is likely to lead to improvements in dealing with the problem of economic benchmarking.
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]
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.