The Prediction Of Future Earnings Using Financial Statement Information: Are Xbrl Company Filings Up To The Task?

Financial statement data for large companies became available to the public in XBRL format starting in 2009 in the United States. Proponents of XBRL, along with the SEC, argue that XBRL filings offer several advantages over data provided by data aggregators, such as lower cost, faster availability, and broader coverage. The purpose of this study was to contribute to the combody of knowledge by investigating whether current XBRL company filings are useful in the prediction of future earnings and to attempt to interactively obtain the balances of 70 accounting concepts needed to create an earnings prediction model from a sample of XBRL filings. Current XBRL filings do not allow for interactive extraction of required accounting elements because too many accounting elements are missing from the XBRL filings. Accordingly, an additional data set was created by manually populating missing accounting concepts within the XBRL filings if sufficient component accounting concepts existed within the same XBRL filing (e.g., if current liabilities and long-term liabilities were tagged in the XBRL filing, total liabilities could be calculated). This process mimicked what could be performed by added functionality built directly into the XBRL taxonomy. This functionality would not create any excess time, effort, or cost for preparers or users. This fully populated XBRL data set allows the user to create earnings prediction models interactively, whereas the current XBRL data set does not. This indicates that current XBRL company filings are likely to be limited in their usefulness in other areas as well, while a more fully populated set of XBRL company filings that includes additional data has the potential to improve the usefulness of XBRL data

Data Quality Problems Troubling Business and Financial Researchers: A Literature Review and Synthetic Analysis

The data quality of commercial business and financial databases greatly affects research quality and reliability. The presence of data quality problems can not only distort research results, destroy a research effort but also seriously damage management decisions based upon such research. Although library literature rarely discusses data quality problems, business literature reports a wide range of data quality issues, many of which have been systematically tested with statistical methods. This article reviews a collection of the business literature that provides a critical analysis on the data quality of the most frequently used business and finance databases including the Center for Research in Security Prices (CRSP), Compustat, S&P Capital IQ, I/B/E/S, Datastream, Worldscope, Securities Data Company (SDC) Platinum, and Bureau Van Dijk (BvD) Orbis and identifies 11 categories of common data quality problems, including missing values, data errors, discrepancies, biases, inconsistencies, static header data, standardization, changes in historic data, lack of transparency, reporting time issues and misuse of data. Finally, the article provides some practical advice for librarians to facilitate their scholarly communications with researchers on data quality problems

Data Standardization and Quality Degradation of Human-readable Data

Data standardization is a widely recommended solution to improving data quality. Despite the potential benefits, we examine if it has any unintended, especially undesirable, side effects on data quality. The eXtensible Business Reporting Language (XBRL) is an XML-based open standard that aims to facilitate the preparation, exchange and comparison of financial reports. Leveraging the unique opportunity created by the exogenous mandatory XBRL adoption enforced by the U.S. SEC, we use a difference-in-differences (DID) research design to establish the causal relationship between XBRL adoption and quality of HTML-formatted financial reports, an important source for investors and analysts to obtain firms’ financial information. Surprisingly, we find the mandatory XBRL adoption has degraded the quality of the adopting firms’ HTML-formatted financial reports, as measured by a number of data quality metrics, including spelling errors and readability. The U.S. SEC and adopting firms need design appropriate policies to minimize the undesirable side effects

Empirical Research on Financial Notes to the Accounts and Earnings Management

Managers can influence the amount of net income their firm reports by variation in the application of accounting policies or by making real cash flow decisions. 'Earnings management' is the term given when such choices or decisions distort the fair presentation of earnings. Such earnings management activities can lead to negative consequences in the long-term. Accounting scandals in the past have shown that earnings management can even threaten the existence of a firm. Therefore, it is of crucial importance to detect and restrict earnings management. The notes to the accounts can provide information, which is otherwise not presented on the face of the financial statements. Especially the accounting policy disclosures improve the understanding about a firm’s current and future earnings. According to the comparability theory, there should be comparable accountings of firms in the same industry that are subject to similar economic events. Extending this theory, managers of comparable firms should translate the same economic events into similar notes to the accounts and contain similar earnings and discretionary accruals. Therefore, this PhD thesis examines whether similar notes to the accounts are negatively associated with a firm’s propensity to manage earnings. This means that the effect of similar textual accounting policy disclosures or rather notes relative to other firms in the same industry is tested on both, accrual-based and real earnings management proxies. This research uses detail-tagged XBRL notes from SEC EDGAR system as data source. To operationalize the within-industry similarity of the XBRL-formatted notes, the co-sine similarity measure was utilized in this study. Two different similarity scores of the notes are adopted. First, the full set of accounting policy disclosures and second, the revenue recognition disclosures. The key findings demonstrate that firms with more similar notes of the previous year conduct less accrual-based earnings management activities in the following fiscal year. Also, the empirical analyses show that more similar accounting policy and revenue recognition disclosures are negatively associated with real earnings management activities. Collectively, these results indicate that firms with an overall better accounting information environment as measured by more similar notes, relative to industry peers, engage in less accrual-based and real earnings management activities in the following year

The Development of a New Digital Business Reporting Standard - Inline XBRL

Digital reporting has become one of the key elements for regulatory reporting by businesses to the government in the UK. By tagging financial information with individual codes that are based on accounting standards and regulatory reporting regime, financial reports are now transformed into eXtensible Business Reporting Language (XBRL) instance documents and can be processed by XBRL-enabled software tools. This research explores how two of the governmental bodies in the UK - HM Revenue and Customs and Companies House - got involved in a close cooperation with the XBRL community in the UK and together with IT consultants and technical experts developed a human-readable format of XBRL, known as Inline XBRL (iXBRL). The central concern is the fabrication of the new standard through formation of a network of actors and objects involved in the process. The study adopts an Actor Network Theory perspective that highlights the complexity of technology implementation in regulatory environments using concepts from sociomateriality theory (Latour, 2005; Leonardi & Barley, 2008). It draws on documentary evidence and semi-structured interviews with regulators, software vendors, and other organisations involved in developing digital reporting facility in the UK. The study has found that iXBRL affordances acted as a catalyst for the network to grow and the regulators to fulfil their obligations by addressing the pressures from major consulting companies, accounting professional bodies, and software vendors. The XBRL rendering functionality produced the perception of both capability for users of financial reports and constraint for filers. To overcome this constraint, the network of actors directed all resources to producing a visually more accessible rendering mechanism that was inscribed in iXBRL. By focusing on the UK unique context of the mandate of iXBRL-based filing, the thesis contributes by illustrating what was compromised and what was gained for heterogeneous groups of actors when establishing the infrastructure for digital business reporting

Financial Narratives of U.S. Biotechnology Companies Before, During, and After the Great Recession

The regulatory environment and associated financial disclosures required of U.S. companies have become increasingly complex as a result of dynamic market conditions and the need to assure stakeholders of the integrity of reported financial results. Advances in technology now provide the opportunity for unique investigation into the narratives included in regulated financial disclosures. The purpose of this study was to determine whether regulated financial disclosure narratives of U.S. biotechnology companies change before, during, and after a financial crisis. The research aimed to characterize financial narratives through textual analysis and the use of predetermined semantics. This is a quantitative study of Management Discussion and Analysis narratives for U.S. biotechnology companies before, during, and after the financial crisis of 2008. The study furthers the understanding of how narratives are used in regulated disclosures. This study demonstrated that financial narratives of U.S. biotechnology companies follow patterns based on predetermined language semantics. In addition, the clustering patterns change before, during, and after a financial crisis, evidence of an association between reporting narratives and earnings per share was not observed. Given the advances in analytical data technology, it is recommended that this study serve as proof of concept for public companies, stakeholders, and regulators to adopt a more efficient review of public company financial disclosures. Further research should also be completed of different reporting narratives and industries

TEXTUAL DISCLOSURE IN SEC FILINGS AND LITIGATION RISK

Prior studies are quite ambivalent on the relation between disclosure and litigation risk since greater disclosure can be perceived as either ex-ante deterrent or ex-post misleading. I hypothesize that more information is disclosed in the non-numerical narratives in SEC filings than that has been analyzed in the extant literature. Using comprehensive hand-collected data on federal securities class action lawsuits spanning nearly two decades, matched peers, and widely used measures in natural language processing (NLP) that capture degree, readability, and sentiments in textual disclosures, I find results consistent with the theoretical view that argues that more and difficult to comprehend disclosure is often perceived as ex-post misleading, hence, increasing the odds of litigations. After controlling for other explanatory numerical variables, these results are robust to various empirical specifications using difference-in-differences (DiD), principal component analyses (PCA), and market response, across different types of shareholder class action litigations. Finally, using the Ninth Circuit Court of Appeals ruling, Re: Silicon Graphics Inc., that led to an unexpected and sudden reduction in the threat of litigation for firms headquartered in the Ninth Circuit, I find that firms that are headquartered in the Ninth Circuit tend to use more uncertainty words in their filings post-shock, which is consistent with my main results. Such findings indicate that there is a need to distinguish between more versus better disclosures

The detection of fraudulent financial statements using textual and financial data

Das Vertrauen in die Korrektheit veröffentlichter Jahresabschlüsse bildet ein Fundament für funktionierende Kapitalmärkte. Prominente Bilanzskandale erschüttern immer wieder das Vertrauen der Marktteilnehmer in die Glaubwürdigkeit der veröffentlichten Informationen und führen dadurch zu einer ineffizienten Ressourcenallokation. Zuverlässige, automatisierte Betrugserkennungssysteme, die auf öffentlich zugänglichen Daten basieren, können dazu beitragen, die Prüfungsressourcen effizienter zuzuweisen und stärken die Resilienz der Kapitalmärkte indem Marktteilnehmer stärker vor Bilanzbetrug geschützt werden. In dieser Studie steht die Entwicklung eines Betrugserkennungsmodells im Vordergrund, welches aus textuelle und numerische Bestandteile von Jahresabschlüssen typische Muster für betrügerische Manipulationen extrahiert und diese in einem umfangreichen Aufdeckungsmodell vereint. Die Untersuchung stützt sich dabei auf einen umfassenden methodischen Ansatz, welcher wichtige Probleme und Fragestellungen im Prozess der Erstellung, Erweiterung und Testung der Modelle aufgreift. Die Analyse der textuellen Bestandteile der Jahresabschlüsse wird dabei auf Basis von Mehrwortphrasen durchgeführt, einschließlich einer umfassenden Sprachstandardisierung, um erzählerische Besonderheiten und Kontext besser verarbeiten zu können. Weiterhin wird die Musterextraktion um erfolgreiche Finanzprädiktoren aus den Rechenwerken wie Bilanz oder Gewinn- und Verlustrechnung angereichert und somit der Jahresabschluss in seiner Breite erfasst und möglichst viele Hinweise identifiziert. Die Ergebnisse deuten auf eine zuverlässige und robuste Erkennungsleistung über einen Zeitraum von 15 Jahren hin. Darüber hinaus implizieren die Ergebnisse, dass textbasierte Prädiktoren den Finanzkennzahlen überlegen sind und eine Kombination aus beiden erforderlich ist, um die bestmöglichen Ergebnisse zu erzielen. Außerdem zeigen textbasierte Prädiktoren im Laufe der Zeit eine starke Variation, was die Wichtigkeit einer regelmäßigen Aktualisierung der Modelle unterstreicht. Die insgesamt erzielte Erkennungsleistung konnte sich im Durchschnitt gegen vergleichbare Ansätze durchsetzen.Fraudulent financial statements inhibit markets allocating resources efficiently and induce considerable economic cost. Therefore, market participants strive to identify fraudulent financial statements. Reliable automated fraud detection systems based on publically available data may help to allocate audit resources more effectively. This study examines how quantitative data (financials) and corporate narratives, both can be used to identify accounting fraud (proxied by SEC’s AAERs). Thereby, the detection models are based upon a sound foundation from fraud theory, highlighting how accounting fraud is carried out and discussing the causes for companies to engage in fraudulent alteration of financial records. The study relies on a comprehensive methodological approach to create the detection model. Therefore, the design process is divided into eight design and three enhancing questions, shedding light onto important issues during model creation, improving and testing. The corporate narratives are analysed using multi-word phrases, including an extensive language standardisation that allows to capture narrative peculiarities more precisely and partly address context. The narrative clues are enriched by successful predictors from company financials found in previous studies. The results indicate a reliable and robust detection performance over a timeframe of 15 years. Furthermore, they suggest that text-based predictors are superior to financial ratios and a combination of both is required to achieve the best results possible. Moreover, it is found that text-based predictors vary considerably over time, which shows the importance of updating fraud detection systems frequently. The achieved detection performance was slightly higher on average than for comparable approaches