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Using the EDGAR Log File Data Set
... The number of non-robot viewership data (EDGAR i,t ) for firm i in year t is identified following Drake et al. (2015) and Ryans (2017) by removing downloads by computer programs (or robots) from the log files. We do this filtering by using a classification algorithm proposed in Ryans (2017). 9 We conduct a cross-sectional analysis based on five stock characteristics. ...
... The EDGAR system is the main source of firms' regulatory filings, and the SEC maintains log files of all activities performed by users on EDGAR. 16 Following Lee et al. (2015) and Ryans (2017), we first eliminate the requests made by computer programs or automated web crawlers (i.e., robots) and only use nonrobot EDGAR downloads. 17 From the data, we can directly observe investors' information acquisition activity for a broad cross-section of firms over a sample period of 2003-2017. ...
... We use inverse price (InverseP i,t ), relative spread (Spread i,t ), the natural logarithm of market value 16 The raw data is available for download at https://www.sec.gov/data/edgar-log-file-data-set.html and the processed data is available at http://www.jamesryans.com. 17 We use three different filtering algorithms proposed by Drake et al. (2015), Loughran and McDonald (2017) and Ryans (2017). The results are virtually the same with all three approaches. ...
An important dimension of heterogeneity in algorithmic trading (AT) is whether the algorithm is designed to supply or demand liquidity. We show that liquidity-supplying AT facilitates firm managers’ learning from stock prices by fostering information acquisition in markets, thereby increasing sensitivity of firms’ investment to stock price. By contrast, liquidity-demanding AT harms managerial learning from stock prices. Consequently, firm operating performance increases (decreases) with the level of liquidity-supplying (liquidity-demanding) AT. We use the staggered implementation of Autoquote in NYSE as a source of exogenous variation in AT to establish causality. Our findings highlight the real economic effects of AT.
... To address these questions, we collect focal and competitor firms' advertising expenditures from Kantar Media AdSpender, product development announcements from Standard & Poor's Capital IQ database, and customer satisfaction, and WOM from YouGov. To proxy investor attention, we collect data on investors' search of financial documents from the Securities and Exchange Commission's (SEC) EDGAR website (Madsen & Niessner, 2019;Ryans, 2018). We test our hypotheses on a sample of 349 firms across ten years of quarterly data from 2007 to 2017. ...
... These log files contain detailed information about the users' IPs, corporations and filings, and the detailed time stamp (nearest to the second). Recently, the SEC released these log files to the public, and a growing number of academic studies have utilized this dataset for topics relating to investor attention and information acquisition (Drake et al., 2016;Ryans, 2018). The SEC EDGAR system hosts all mandatory filings by public companies. ...
... We obtain our SEC EDGAR search data from Ryans (2018). This dataset is taken from the online EDGAR system, which maintains a log file for all activity performed by system servers. ...
Investors' attention to a firm's stock has been demonstrated to influence stock returns (Da et al., 2011). But does a firm's marketing information draw attention to a firm's stock? Research in finance, accounting, and marketing has investigated advertising as one potential driver of investors' attention to a firm's stock. How about other potential marketing drivers? The authors develop hypotheses related to the impact of the changes in four marketing levers: advertising, product development announcements, WOM, and customer satisfaction on the change in investor attention to a firm's stock. Furthermore, they investigate the moderating role of competitors' marketing levers in these relationships.
To test the hypotheses, they compile a panel dataset with 349 firms covering the 2007-2017 period. The results suggest that the changes in the focal firm's advertising and WOM have a positive and significant impact on the changes in investor attention to the focal firm’s stock. Furthermore, these effects are amplified when there is an increase in competitors' advertising spending and WOM, respectively. For the customer satisfaction lever, the results suggest that the change in competitors' customer satisfaction enhances the impact of the change in focal firm's customer satisfaction on investor attention. Collectively, the results suggest that investors attend to the firm's and its competitors' marketing information in a much more nuanced manner than previously thought.
... Overall, either the staff of the various regional reserve banks rarely access SEC filings or we are unable to identify the web domain names of these banks. 12 Untabulated analysis (available from the authors) shows that the Fed IP addresses do not meet the criteria for automated web crawlers (Drake, Roulstone, and Thornock 2015;Ryans 2017). An exception in the SEC log data is the Fed staff's views of form N-MFP, the monthly fund holding schedule required of mutual funds, which show strong evidence of automated activity. ...
... where Forecast t t+h is the Fed forecast (H2) (or the average SPF forecast (H2a)) made in quarter t, for nominal GDP growth h quarters ahead, and Fed Neg Tone t is the aggregate negative tone of SEC filings accessed by the Fed between (The full-color version is available online.) 21 As filings with exhibits, such as 10-Ks, are often accessed multiple times in a row (Ryans 2017), we take the daily average of negative tone in Fedaccessed filings before averaging over the period between FOMC meetings (or between SPF forecasts) to calculate our aggregate tone measure. While these duplicate views may be consistent with Fed's review of the exhibits to the filing, the tone measure that we use reflects the textual content only from the main portion of the filing (Loughran and McDonald 2011). ...
This study provides the first empirical evidence that the Federal Reserve (the Fed) systematically retrieves micro-level accounting reports to aid its understanding of the state of the macroeconomy. Using unique data identifying its direct access of corporate SEC filings, we show that the Fed tracks firms that are bellwethers and industry leaders, or that can engender systemic risk. The qualitative information in the Fed-accessed periodic reports explains the Fed’s GDP growth forecasts for up to four quarters, after controlling for contemporaneous aggregate earnings and other economic information. However, professional forecasts fail to incorporate such qualitative accounting information. In addition, this qualitative information is reflected in the tone of the ensuing FOMC meeting discussions as well as in Fed forecasts of key macro demand and supply factors. Overall, our evidence suggests important externalities of micro-level accounting reports, especially qualitative disclosures, on the central bank’s macroeconomic forecasts and, by extension, monetary policy.
Data Availability: Data are available from public sources.
JEL Classifications: E58; G20; M41; M45.
... This study employs the SEC Edgar Database as the primary data source, offering a large collection of financial documents, including 10-K, 10-Q, and 8-K reports [18]. These documents provide credible financial statements, risk disclosures, and management discussions, making them ideal for financial text analysis. ...
This study proposes a financial text analysis method based on a one-dimensional convolutional neural network (1D-CNN), aiming to solve the problems of low efficiency and insufficient accuracy of traditional financial text processing methods in key information extraction and risk classification tasks. By constructing a convolutional network architecture tailored to the characteristics of financial text, the model can efficiently capture local semantic features in the text and perform deep feature extraction. In the experiment, this study selected the 10-K financial report in the SEC Edgar database as the dataset and verified the superiority of the 1D-CNN model through comparative experiments with traditional machine learning models and other deep learning models. The experimental results show that the model has achieved the best performance in terms of extraction rate, coverage rate, and redundancy rate, and also shows high accuracy and robustness in risk classification tasks. In addition, by testing the performance of the model under different noise levels, this study further analyzes the stability and limitations of the 1D-CNN model in the face of data perturbations. The results show that although the performance of the model is reduced in a noisy environment, the overall anti-interference ability is strong, which is suitable for financial text analysis in actual complex scenarios. This study provides an effective technical solution for intelligent financial text processing. It not only theoretically verifies the feasibility of 1D-CNN in financial text analysis but also provides an important reference for building a smarter and more efficient financial management system in the future. CCS CONCEPTS Computing methodologies~Machine learning~Machine learning approaches~Neural networks
... 27 Users are partially anonymized as the EDGAR log files show the first three octets of the IP address and replace the fourth with a unique string. Following previous studies (e.g., Lee, Ma, and Wang, 2015; Drake et al., 2020;Ryans, 2017;Li and Sun, 2021), we remove the records of users that download more than 50 unique firms' filings per day from the sample and keep only the successful request records (code = 200) and exclude records that refer to an index (idx = 1) since the index pages provide links to the firm's filings instead of the filings themselves. the filing firm, and the time-stamp for each request. ...
We analyze over 2.6 million news articles and propose a novel measure of aggregate joint news coverage of firms. The measure strongly and negatively predicts market returns, both in sample and out of sample. The relation is causal, robust to existing predictors, and is especially strong when market uncertainty is high or when market frictions are large. Using data on EDGAR downloads by unique IPs, we provide direct evidence that joint news triggers attention spillover across firms. Our results are consistent with the explanation that joint news generates a contagion in investor attention and causes marketwide overvaluations and subsequent reversals.
... Standard errors are shown in the parentheses. [39] to measure attention [28,29,39,66] and examine whether institutional cross-blockholding leads to investor distraction [12,38,67]. All the ESV measures are in the logarithm form. ...
Institutional investors routinely hold blocks of stocks in multiple firms within an industry. While such cross-blockholding boosts a portfolio firm’s financial performance, could it distract investors from attending to firm activities in a nonfinancial domain, hurting its performance in that domain? The authors answer this question in the context of corporate social responsibility (CSR). They first document that cross-held firms perform worse on social responsibility than non-cross-held firms do. A quasi-natural experiment based on mergers between institutional blockholders helps establish causality. Next and as their primary contribution, the authors demonstrate investor distraction as the mechanism. Using two proxies of distraction—EDGAR search volume and shareholder proposals on socially responsible investment—they show that the negative impact of institutional cross-blockholding on CSR mainly comes from investor distraction when investors hold multiple blocks simultaneously. By highlighting the social cost of institutional cross-blockholding, this article finds a distraction effect of institutional cross-ownership, which extends our understanding of this unique ownership structure.
... 24 We follow Loughran and McDonald (2017) and define internet protocols (IPs) with more than 50 downloads in a day as being robot activity. Our results are similar when we follow Drake, Roulstone, and Thornock (2015), Ryans (2017), or Lee, Ma, and Wang (2015) in eliminating the robot downloads. 25 We are able to conduct this analysis for the deals filed between February 14, 2003, and June 30, 2017, since the SEC only made internet search traffic for EDGAR filings available through SEC.gov in this period. ...
Using novel merger valuation data, we show that firms selected by investment banks as “comparable peers” are more than twice as likely to later become takeover targets themselves compared to matched control firms. Peer firms not subsequently acquired attract more institutional ownership and analyst coverage, deliver strong operating performance, reduce investments, and increase payouts. Investors are inattentive, though, to peer identification at the time of merger filings’ public disclosure. A portfolio that longs peers and shorts controls earns up to 15.6 alpha annually, which mainly comes from the long leg and is difficult to explain by short-sale constraints.
Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.
... Column (1) 18 We obtain data from SEC MIDAS to calculate algorithmic trading measures and use the EDGAR search volume data provided by http://www.jamesryans.com/. See Ryans (2017) for details on the measure. 19 The inferences are unaffected by winsorization. ...
Employing the SEC Tick Size Pilot Program, which increases the minimum trading unit of a set of randomly selected small-capitalization stocks, we examine whether and how an exogenous change in stock liquidity affects corporate voluntary disclosure. Using difference-in-differences analyses with firm fixed effects, we find that treatment firms respond to the liquidity decline by issuing fewer management earnings forecasts, while, in contrast, control firms do not exhibit a significant change. Next we show that the effect is more pronounced when firms experience more severe liquidity decreases during the TSPP and rule out a set of alternative explanations. Further strengthening the identification, we find a consistent reversal effect after the end of the pilot program. To generalize our findings, we use voluntary 8-K filings and conference calls as alternative voluntary disclosure proxies and find similar effects. Overall, these findings show how an exogenous change in stock liquidity shapes the corporate information environment.
... In the absence of an audit of the in-control statement of management, research of the financial reporting systems and company's innate characteristics is hard with only publicly observable data. Researchers have been using corporate governance indicators (Clatworthy and Peel 2013), network analyses of management and supervisory board members (Bruynseels and Cardinaels 2014), (transcripts of) earnings calls with financial analysts (Hobson et al. 2017) and web-crawlers (Ryans 2017) to identify as much as possible. ...
Several committees, institutions, and practitioners are currently working on defining appropriate, and reliable Audit Quality Indicators (AQIs). The experiences the Foundation for Auditing Research (FAR) made with collecting audit quality data may inform the search for appropriate and reliable AQIs. In this paper I discuss different types of audit (quality) measures and their availability in the Netherlands. Furthermore, I discuss the (potential) information value, limitations, and recommendations for a wide range of audit quality measures and sources.
... Based on EDGAR server access traffic from , Drake, Roulstone, and Thornock (2015 found that information acquisition via EDGAR positively influences market efficiency. Ryans (2017) analyzed the same data set from a different perspective and demonstrated the differences between human requests and financial robots. ...
Financial disclosure analysis and Knowledge extraction is an important financial analysis problem. Prevailing methods depend predominantly on quantitative ratios and techniques, which suffer from limitations like window dressing and past focus. Most of the information in a firm's financial disclosures is in unstructured text and contains valuable information about its health. Humans and machines fail to analyze it satisfactorily due to the enormous volume and unstructured nature, respectively. Researchers have started analyzing text content in disclosures recently. This paper covers the previous work in unstructured data analysis in Finance and Accounting. It also explores the state of art methods in computational linguistics and reviews the current methodologies in Natural Language Processing (NLP). Specifically, it focuses on research related to text source, linguistic attributes, firm attributes, and mathematical models employed in the text analysis approach. This work contributes to disclosure analysis methods by highlighting the limitations of the current focus on sentiment metrics and highlighting broader future research areas.
... We use all of these investor attention measures in our analysis. We download and filter GSV, EDGAR, and Bloomberg data as in Drake, Roulstone, and Thornock (2015), Ryans (2017), and Ben-Rephael, Da, and Israelsen (2017), respectively. WIKI-just like GSV, SEC EDGAR downloads, and search activity on Bloomberg-is a direct measure of attention, in contrast to indirect measures based on financial market variables like trading volume or volatility, which are used in Barber and Odean (2008). ...
Using daily advertising data, we analyze the short-term effects of advertising on investor attention and on financial market outcomes. Based on various investor attention proxies, we show that advertising positively affects attention. However, it has only little impact on turnover and liquidity. Most importantly, short-term stock returns are not significantly influenced by advertising. Further results suggest that previous findings of an economically significant positive relation between advertising and returns are due to reverse causality. Thus, the belief that stock prices can be temporarily inflated via advertising is misguided.
... We use all of these investor attention measures in our analysis. We download and filter GSV, EDGAR, and Bloomberg data as in Drake, Roulstone, and Thornock (2015), Ryans (2017), and Ben-Rephael, Da, and Israelsen (2017), respectively. WIKI-just like GSV, SEC EDGAR downloads, and search activity on Bloomberg-is a direct measure of attention, in contrast to indirect measures based on financial market variables like trading volume or volatility, which are used in Barber and Odean (2008). ...
This paper investigates whether newspapers report more favorably about advertising corporate clients than about other firms. Our identification strategy based on high-dimensional fixed effects and high frequency advertising data shows that advertising leads to more positive press coverage. This advertising bias in reporting is found among local and national newspapers. Further results show that advertising bias manifests particularly in less negative reporting after bad news events such as negative earnings surprises or extremely negative stock returns. Our findings cast doubt on the independence of the press from corporate pressure and hint at important information frictions.
We examine the informativeness of analyst stock recommendations in the presence of aggregate uncertainty. Our results suggest that a one standard deviation increase in aggregate uncertainty decreases the likelihood of influential recommendation revisions by 5.26%. Increased aggregate uncertainty leads to a small stock price impact for upgrade and downgrade recommendations. Our findings reveal consistent search for information by investors, which, support a post‐recommendation price drift amidst high aggregate uncertainty. We further find that investors of firms with fewer distracted shareholders, less readable financial statements, and more informed trading seek more information when aggregate uncertainty is high. Our study highlights that investors become more cautious while responding to analysts' stock recommendations during high aggregate uncertainty.
What role does investors’ firsthand experience have in stock selection, and does this firsthand experience lead to better investment outcomes? Using a unique data set containing the stock research activities of patrons of Las Vegas casino hotels, we find evidence that investors’ firsthand experience motivates interest in Vegas‐related travel industry stocks. Additionally, their interest in these stocks predicts strong performance; Vegas interest leads to positive abnormal returns of up to 3.7% (0.4%) over the following year (month), and abnormal returns are highest in industries that are related to Las Vegas.
This study examines managers’ strategic use of concurrent disclosures around the announcement of negative material events. We predict and find that managers disclosing negative 8-K news are more likely to issue a concurrent press release about an unrelated event relative to a press release providing additional context for the 8-K–triggering event in order to increase investor information processing costs. This strategy appears distinct from the bundling of news to deter litigation. We find that managers more commonly issue concurrent unrelated press releases when they have stronger incentives to impede the pricing of negative information and that doing so is associated with a reduction in the speed with which prices reflect the news. Our findings shed light on a previously unexplored tool managers use to exploit investors’ processing capacity constraints to “hide” negative news.
JEL Classifications: G12; G14; M41; M48.
This study investigates whether tax-based proprietary costs associated with being a public firm (i.e., costs resulting from increased visibility to the tax authority) discourage public listing. I exploit the introduction of a mandatory disclosure requirement (FIN 48) which generated a signal to the government regarding the uncertainty of public firms’ tax positions, allowing for more carefully targeted audits. I hypothesize and find evidence of an increased propensity to go private by public, tax aggressive firms following the enactment of the disclosure rule but prior to its adoption. Cross-sectionally, the effect is stronger among firms that are more sensitive to tax-based proprietary costs. Moreover, IPOs by tax aggressive firms exhibit a relative decline after FIN 48, consistent with the disclosure requirement deterring private, tax aggressive firms from going public. Overall, my findings suggest that mandatory disclosure rules imposing tax-based proprietary costs may discourage some firms from operating as public entities.
I examine how investors’ search for different types of information affects managers’ reporting decisions. I distinguish investors’ search for information about firm fundamentals (“fundamental search”) from their search for information about managers’ incentives (“incentive search”). Based on a parsimonious model of misreporting, I predict that fundamental search reduces the earnings response coefficient, which reduces a manager’s benefits from misreporting, resulting in less misreporting. In contrast, incentive search increases the earnings response coefficient, which increases the benefits from misreporting, resulting in more misreporting. I test these predictions using an empirical technique that classifies EDGAR downloads as fundamental search or incentive search. Consistent with my theoretical predictions, I find that both fundamental and incentive search are positively associated with measures of price efficiency, but that fundamental (incentive) search is negatively (positively) related to the earnings response coefficients and intentional restatements. I also confirm my findings in the XBRL adoption setting where the costs of fundamental search exogenously decreased, and show that the XBRL adoption increased investors’ search for fundamental information relative to incentive information, decreased the earnings response coefficient, and decreased misreporting. Collectively, the results show that investors’ information demand can shape managers’ reporting decisions, and that its effects can vary depending on the type of information investors search for.
We investigate the effect of tick size, a key feature of market microstructure, on managerial learning from stock prices. Using a randomized controlled tick-size experiment, the 2016 Tick Size Pilot Program, we find that a larger tick size increases a firm’s investment sensitivity to stock prices, suggesting that managers glean more new information from stock prices to guide their investment decisions as the tick size increases. Consistently, we also find that changes in managerial beliefs, as reflected in adjustments of forecasted capital expenditures, respond more strongly to market feedback under a larger tick size. Additional evidence suggests the following mechanism through which tick size affects managerial learning: a larger tick size reduces algorithmic trading, in turn encouraging fundamental information acquisition. Increased fundamental information acquisition generates incremental information about growth opportunities, macroeconomic factors, and industry factors, with respect to which the market has a comparative information advantage over management.
This study examines the effect of corporate tax enforcement on managerial self-dealing, with a focus on manipulated gifts of insider stock. Prior work suggests that managers employ a variety of manipulative techniques to maximize their personal tax benefits from donating corporate stock, such as strategically timing gifts based on private information and fraudulently backdating gifts to the date with the highest price. Building on prior literature suggesting that the tax authority can discipline managerial misconduct, we hypothesize that IRS scrutiny from a corporate tax audit raises managers’ perceived risk of detection, who refrain from making manipulated stock gifts while the firm is under audit. Using a novel, firm-specific measure to identify firms under audit, we find direct evidence that heightened scrutiny from tax enforcement serves as an effective monitoring mechanism and reduces managers’ self-dealing behavior.
SEC confidential treatment (CT) orders are regulatory exemptions that enable firms to redact proprietary information from SEC filings if the disclosure would cause competitive harm and if the information is immaterial to investors. This study examines the role of firms’ political connections in the SEC’s decisions to approve versus reject CT requests before and after Congressional intervention and internal SEC scrutiny into the CT process. CT requests from politically connected firms are less likely to be rejected before Congressional intervention and internal SEC scrutiny and are more likely to be rejected following these events. When the SEC rejects CT requests, firms must disclose the contents of the unapproved redactions. These disclosures are informative to investors, on average, and are less informative following Congressional intervention and internal SEC scrutiny. Together, these findings contribute to the literature on political influence in SEC oversight and disclosure regulation and provide unique evidence on the role of Congressional intervention in SEC decision making.
Using a novel dataset containing investors’ access of company filings through the SEC’s EDGAR system, we show that the abnormal number of IPs searching for firms’ financial statements strongly predicts future stock returns and firm fundamentals. A long-short portfolio based on our measure of information acquisition activity generates a monthly abnormal return of 80 basis points that is not reversed in the long-run. Consistent with theories of endogenous information acquisition, the return predictability is more pronounced for firms with larger and lengthier financial filings that are more costly to process, and for IPs searching current and historical filings simultaneously. Our findings suggest investors’ costly information acquisition activities reveal their private expectation of firm value.
Unlike prior research that focuses on determinants of firm‐specific stock price crashes (SPCs), we study the consequences of SPCs on market information efficiency. The tension underlying our research question stems from two competing explanations. As an unanticipated shock, a SPC could stimulate (distort) information efficiency by triggering investor rational attention (opinion divergence). Our identification strategy involves a difference‐in‐differences analysis in which SPC firms in the treatment sample are propensity score matched with non‐SPC firms in the industry‐peer control sample, as well as placebo tests for falsification. Consistent with the stimulation effect, we find an increase of the earnings response coefficient and a decrease in post‐earnings announcement drift, from the pre‐ to post‐SPC period, for SPC firms, but not for non‐SPC firms. Further analyses reveal that SPC firms attract increased investor attention, as reflected in greater analyst coverage and more investor access to firms’ online financial filings following such an event. Using mutual fund flow redemption pressure based on hypothetical sales as an exogenous shock to SPCs, we provide evidence corroborating our causal interpretation of the main findings. Collectively, the evidence suggests that SPCs can attract increased investor attention, bringing about positive externalities by stimulating market information efficiency. This article is protected by copyright. All rights reserved.
I study venture capital firms’ (VCs) use of public market information and how attention to this information relates to private market investment outcomes. I link web traffic to public filings hosted on EDGAR to individual VCs. VCs analyze public information about industry peers before most deals. An increase in industry filing views relates positively to the probability of an exit through acquisition, suggesting that public information helps identify paths to acquisition. The effect is stronger when the VC has less access to private information, especially for low-reputation VCs. Policymakers should consider spillover effects on private markets when setting public disclosure requirements.
We use required 8-K filings around major borrowings to shed light on firms’ choices of whether to comply with SEC disclosure rules. Exploiting within-firm variation, we find that firms are more likely to hide loans with high spreads and tight financial covenants. We further find that firms appear to exploit the ambiguity of the definition of materiality, as they are more likely to selectively disclose (hide) “immaterial” loans when interest rates are low (high). Firms are less likely to hide loans when investors anticipate borrowing during asset acquisition, when firms are followed by more equity analysts or receive more investor attention, and when the firms’ stock prices are more volatile. Lastly, we provide evidence that the SEC does not rigorously enforce compliance with 8-K loan disclosures.
We investigate how information choices affect equity returns and risk. Building on an existing theoretical model of information and investment choice, we estimate a learning index that reflects the expected benefits of learning about an asset. High learning index stocks have lower future returns and risk compared to low learning index stocks. Analysis of a conditional asset pricing model, long-run patterns in returns and volatilities, other measures of information flow, and the information environment surrounding earnings announcements reinforce our interpretation of the learning index. Our findings support the model’s predictions and illustrate a novel empirical measure of investor learning.
This study examines how an increase in tick size affects algorithmic trading (AT), fundamental information acquisition (FIA), and the price discovery process around earnings announcements (EAs). Leveraging the SEC's randomized Tick Size Pilot experiment, we show that a tick size increase results in a decline in AT and a sharp drop in absolute cumulative abnormal returns and volume around EAs. More importantly, we find increased FIA in the preannouncement period. Specifically, we show: (1) treatment firms' pre-announcement returns better anticipate next quarter's standardized unexpected earnings; (2) these firms experience an increase in EDGAR web traffic prior to EAs; and (3) they exhibit a drop in price synchronicity with index returns. Taken together, our evidence suggests that while an increase in tick size reduces AT and abnormal market reaction after EAs, it also increases FIA activities prior to EAs.
JEL Classifications: M40; M41; G12; G14.
Using a natural experiment (the SEC's 2016 Tick Size Pilot Program), we investigate the effects of an increase in tick size on financial reporting quality. The tick size pilot program reduces algorithmic trading and increases fundamental investors’ information acquisition and trading activities. This in turn increases the scrutiny of managers’ financial reporting choices and reduces their incentives to engage in misreporting. Using a difference‐in‐differences research design, we find a significant decrease in the magnitude of discretionary accruals, a significant reduction in the likelihood of just meeting or beating analysts’ forecasts, and a marginally significant decrease in restatements for the treated firms in the pilot program. Furthermore, we find that the change in financial reporting quality is concentrated in treated firms experiencing decreases in algorithmic trading and increases in information acquisition activities. We also find that the mispricing of accruals is significantly lower for treated firms. Taken together, our results suggest that an increase in tick size has a causal effect on firms’ financial reporting quality.
This article is protected by copyright. All rights reserved
Using a direct measure of investor attention generated from the Securities and Exchange Commission’s EDGAR (Electronic Data Gathering, Analysis, and Retrieval) log files, the authors revisit the stock return predictability of the divergence of opinions in the presence of varying degree of investor attention and information acquisition. They document a positive relationship between the divergence of opinions and future stock returns, consistent with the risk hypothesis, as opposed to the overvaluation hypothesis. More importantly, the authors find that the predictive power of divergence of opinions is more pronounced in stocks with lower investor attention. They further document the construction and profitability of divergence of opinions portfolios augmented with investor attention. A portfolio that goes long on stocks with low investor attention and the highest divergence of opinions and short on stocks with low attention and the lowest divergence of opinions generates a Fama-French 5-factor monthly alpha of 1.14%.
Using a new and direct measure of investor attention generated from the SEC’s EDGAR log files, we revisit the stock return predictability of call-put implied volatility spread through the lens of investor attention. We find that as investor attention heightens, the volatility spread return predictability becomes more pronounced, providing favorable evidence for the informed trading hypothesis as opposed to the mispricing hypothesis. More importantly, we document the construction and profitability of spread-and-high-attention portfolios. A portfolio that goes long on stocks with the highest investor attention and the highest volatility spread and short on stocks with the highest attention and the lowest volatility spread generates a Fama-French 5-factor monthly alpha of 2.43%.
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