ABSTRACT The increasing volume of accounting research presents challenges in efficiently navigating and synthesizing information. We introduce AIRA—an artificial intelligence (AI)-based application developed to assist scholars in consolidating accounting research papers using generative AI models. Traditional literature review methods are time-consuming. AIRA streamlines this process with natural language prompting. To ensure the application’s reliability and usefulness, we use the design science methodology to validate it meets the designed objectives. Overall, faculty found AIRA achieves its objectives of being useful and easy to use, and they plan to continue using it in the future. A large sample of employees from the Securities and Exchange Commission (SEC) also believed the tool would be useful for their work. In examining the first 500 prompts that were entered by anonymous users, we find that it is used for literature search and retrieval, summarization of research findings, and literature review creation, among other tasks.

ABSTRACT We investigate the impact of board gender diversity and the IT expertise of female board members on data breaches. Using data from U.S. firms between 2005 and 2022, we find that gender diverse boards with female IT expertise are significantly less likely to experience data breaches. These results are robust to multiple endogeneity and sensitivity tests. Cross-sectional analysis reveals that the effect is driven by independent female board members, and requires a critical mass of female representation. Furthermore, auditors do not charge increased fees for breached firms when such board structures are present. The channel analysis suggests that gender-diverse boards with female IT expertise enhance cybersecurity investments, thereby mitigating cybersecurity risks. Overall, the findings shed new light regarding the oversight role of boards in cybersecurity risk management. Data Availability: Data are available from the sources identified in the manuscript. JEL Classifications: M2; M1.

ABSTRACT Social media platforms such as Twitter influence capital markets by rapidly disseminating information; yet, this environment is increasingly shaped by nonhuman bots. Building on theories of investor attention and information salience, we examine whether bots amplify market reactions to earnings news by directing attention toward larger surprises. Using machine learning to classify 12.02 million tweets discussing S&P 1,500 firms in 2018, we measure firm-specific abnormal bot activity and analyze its association with market responses to earnings announcements. We find that bot activity amplifies the relationship between earnings surprises and abnormal returns. Additional analyses reveal that this effect is stronger when bot sentiment is positive but diminishes with excessive positivity, varies by bot type, and is most pronounced for firms with fewer analysts, further supporting our investor attention argument. Our findings highlight bots’ roles as “attention amplifiers” and underscore the need for greater scrutiny of algorithmic actors in financial markets. Data Availability: Data are available from the public sources identified in the text. JEL Classifications: D83; E71; G14; G41; M41.

ABSTRACT This study aims to improve the prediction of fiscal distress in local governments (LGs) using machine learning (ML) methods. We apply six ML algorithms, including Logistic Regression, XGBClassifier, RUSBoost, C4.5 Decision Tree, Exactly Balanced Bagging, and Roughly Balanced Bagging, which are combined with and without two undersampling methods—Majority Sampling and Optimal Ratio Sampling—to predict fiscal distress of LGs. Analyzing financial and socioeconomic indicators from LGs across 49 U.S. states from 2015 to 2017, we find that regardless of undersampling strategy, the Exactly Balanced Bagging Classifier generally outperforms other ML algorithms including Logistic Regression: its F1-scores range from 53.11 percent to 55.33 percent, significantly higher than 27 percent to 58 percent of Logistic Regression. Furthermore, Optimal Ratio Sampling offers the most significant benefit to Logistic Regression. These findings offer valuable insights into the analysis and prediction of municipal fiscal distress, particularly within the context of imbalanced datasets. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: G31; G32; G33; M21.

ABSTRACT With the recent increase in demand for environmental, social, and governance (ESG) reporting, more companies are creating board-level ESG committees to oversee progress in these areas. Companies are also becoming more aware of the important role that information technology (IT) can play in achieving ESG reporting goals. I argue that an improvement in ESG factors goes hand in hand with an improvement in IT. Specifically, I predict that companies with ESG committees will also benefit through an improvement in IT outcomes. Using a sample of companies from 2004 to 2022 I investigate two direct and two indirect IT outcomes. I find that environmentally and socially focused committees are negatively associated with both IT-related material weaknesses in internal controls and cybersecurity breaches. Further, companies with governance committees report fewer breaches and issue more accurate earnings forecasts. Conversely, I find that firms with environmental and social committees take longer to announce earnings. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: G30; M40; O32.

ABSTRACT This research addresses the challenge of extracting financial data from unstructured sources, a persistent issue for accounting researchers, investors, and regulators. Leveraging large language models (LLMs), this study introduces a novel framework for automated financial data extraction from Portable Document Format (PDF)-formatted files. Following a design science methodology, this research develops the framework through a combination of text mining and prompt engineering techniques. The framework is subsequently applied to analyze governmental annual reports and corporate environmental, social, and governance reports, which are presented in PDF format. Test results indicate that the framework achieves an average 99.5 percent accuracy rate in a notably short time span when extracting key financial indicators. A subsequent large out-of-sample test reveals an overall accuracy rate converging around 96 percent. This study contributes to the evolving literature on applying LLMs in accounting and offers a valuable tool for both academic and industrial applications. Data Availability: Data are available upon request. JEL Classifications: M41; O31; C81.

We have several sayings in our company that I like to repeat publicly.The first is "Security is a process not a product," meaning security is something you do rather than something you buy or even something that you are.It is people and systems doing something normally all day, every day-that is security and not just in the sense that what they do keeps us secure.Of course, the process of doing security is what keeps us secure by definition, but it is the process that keeps us secure, not the products that are used in the process (nor even the people who are not always part of every process).My second saying is "Security is more like accounting than it is like firefighting."Both are processes, but accounting is something you do every day when you process transactions or analyze data, whereas firefighting is something you do on occasion and in response to something causing harm.Notably firefighting is reactive, whereas accounting is proactive.Yes, there is something very much like firefighting in cybersecurity in the form of incident response, and that too is a security function.But day in day out, and even statistically speaking, firefighting is practically absent.Security events occur every day, and many can be called "incidents."However, the necessary response does not resemble firefighting.A day in the life of a security analyst or even a security engineer involves categorizing events identified by security systems, finding ways to tune those systems, configuring systems to address gaps in visibility, and measuring and reporting status and progress.That is a lot more like bookkeeping, audits, and reports.My favorite saying is "because security gives us freedom" in the sense of the freedom to focus on other things with confidence.But this is more than offloading a burden.It is about operational excellence.Any cybersecurity or even information technology (IT) professional will tell you how valuable it is to simply design and run systems well.Keep backups.Take inventory.Document your processes.Validate important actions like onboarding and offboarding employees.Log everything.Develop metrics.Plan and test and test again.The financial world understands this, and the initial operational standards used in IT stem from those.This operational excellence enables us to scale, do more, and identify pain points, bottlenecks, and opportunities.It gives us freedom.Like any field, cybersecurity has its standards and established methods, but despite how much is documented and readily available, the practice is highly fragmented and surprisingly nonprescriptive.Standards, common practices, frameworks, convergence via external pressures, and resources like MITER CVE, ATT&CK, VirusTotal, professional organizations and training, conferences, and community, are heavily leveraged, but it is left to the organization to use them.It is highly valuable to use a framework in your cybersecurity plans as it will give you some alignment with the industry and maybe even some cover when compliance applies.But compliance does not equal security.That takes maturity.The National Institute of Standards and Technology (NIST), Department of Defense (DoD), Cybersecurity and Infrastructure Security Agency, and many other organizations and private entities have developed their own concepts of maturity levels.The Cybersecurity Maturity Model Certification from the DoD even describes its certification levels in terms of maturity, although they refer to granularity of controls rather than overall methods and practices.

ABSTRACT Prior ERP research has focused primarily on adoption and implementation; however, in an ERP implementation, maintenance and support (M&S) also has a significant impact on the long-term viability and financial expenditures of organizations. Many organizations turn to application management service (AMS) providers to manage ERP M&S. We surveyed professionals knowledgeable in their companies’ use of AMS for ERP. We find associations between variables consistent with a business case of “keep the lights on” versus “value-driven” partnership and benefits achieved from the arrangement. We also examine activities that should lead to more M&S success: customization, quality documentation, and process standardization. We find associations between some of these variables and (1) more focus on M&S during implementation, (2) challenges transitioning to AMS, and (3) quality of AMS support. Our results are a first step toward research in AMS for ERP M&S and add to the AIS literature on the ERP post-implementation stage. Data Availability: Data available upon request.

ABSTRACT We develop a measure of disclosure quality using disaggregation of financial statement items from the Form 10-K XBRL filing. Our measure (ITEMS) extends Chen, Miao, and Shevlin’s (2015)DQ measure and is distinct from R. Hoitash and U. Hoitash’s (2018)ARC measure. Our measure provides a simple measure of disaggregation by counting the balance sheet and income statement line items, it does not depend on the data aggregators’ collection process and is readily available shortly after the Form 10-K is filed. We validate ITEMS by showing that firm fundamentals correlate to ITEMS in the predicted direction using OLS regression. We find that ITEMS explains consequences of disclosure quality: forecast error, forecast dispersion, bid-ask spread, and cost of equity capital. Further, ITEMS has explanatory power of disclosure quality consequences incremental to DQ and ARC, and it is distinct from ARC evident from different associations with disclosure quality consequences and reporting quality. Data Availability: Data are available from public sources identified in the text. JEL Classifications: M10; M40; M41.

ABSTRACTThis study examines the degree to which Loughran and McDonald (LM) word lists are informative at the item level of SEC filings, such as risk factors (RF) and management’s discussion and analysis (MDA) disclosures in 10-X reports. In this context, we explore if sentiment types are informative when associated with other material events, namely cybersecurity breaches. Our results support the assertion that sentiment types, beyond positive and negative, are informative at the individual disclosure item level, as tested in the RF and MDA sections. We also find that investors respond to different types of sentiment between RF and MDA. We find an economically significant estimated average economic impact of $469 million/firm. We further contribute to the literature by applying novel statistical methods that advance empirical accounting literature.Data Availability: Data are available from the public sources cited in the text.