The Case for an App-Based Financial Reporting System

The SEC's policing powers have been described as antiquated, but the same criticism has long been made of the United States' financial reporting system. Vasarhelyi and Alles (2008) argued that financial reporting centered on annual 10-K reports and their three core financial statements—the income statement, balance sheet, and statement of cash flows—are becoming obsolete. The primary users of these reports are no longer the unsophisticated investors who were hurt by the 1929 stock market collapse that precipitated the Great Depression but instead are sophisticated financial intermediaries, mutual funds, hedge funds, software traders, regulators, government entities, and analysts. In addition, the costs of preparing financial reports have fallen dramatically from the era of human bookkeepers and paper ledgers; we are now in a world with ubiquitous ERP systems, personal use apps, and cloud-based accounting software. These changes remove many constraints on what information a business conveys to stakeholders.²

Full financial reports are currently only issued once a year; one of the primary reasons was that in the past, it was too expensive and too difficult to do so more often (quarterly reports were not commonplace in 1933 as they have become since). Financial statements have also been simplified to reduce the cognitive load on unsophisticated users. The downside of simplicity is the corresponding lack of detail, a problem the SEC has attempted to solve by continuously increasing the volume of unstructured footnote disclosures accompanying the income statement, balance sheet, and statement of cash flows. These three statements now comprise only a very small fraction of the total length of a 10-K.

Since the Securities Acts of 1933–34, the nature of business, the economic environment, the type of information available, the needs of stakeholders, and the cost and availability of information have changed beyond recognition.

The economic environment has become:

1. substantively multinational,

2. rich in regulatory entities and paperwork,

3. with many public entities,

4. having to cope with the emergence of a wide variety of crypto assets (currencies, stablecoins, NFTs, etc.),

5. increasing level of income difference,

6. demanding ESG, cybersecurity and cryptocurrency information, and

7. prone to significant volatility.

The value of a business' data and its intangible assets have far outstripped the value of physical assets, as evidenced by the wide gap between book and market value. Four of the five largest world valuations are for companies that sell relatively few physical goods (The Economist 2017), such as Alphabet (the parent company of Google) and Meta (the parent company of Facebook).

Big Data sources have changed the nature of the information environment, complementing company collected/generated data with an enormous quantity of exogenous data. These include traditional physical records and locational, traffic, identification, and other data obtainable in bulk. For example, recently, the valuation of Netflix fell dramatically after the release not of financial information but data on its subscriber churn.

Investors now mainly use high-frequency trading to make market purchases, a process whose rhythm is incompatible with annual financial reporting. Companies have components in many industries, and their consolidation leads to non-comparable entities. The world is grappling with accelerating climate change, and ESG reporting is essential for measuring corporate impact on the environment and society. Future corporate reporting will likely require financial, ESG, and cybersecurity reporting.

Entities, including businesses, government, and nonprofits, have developed/acquired very complex internal enterprise systems. Consequently, the incremental cost of providing specific and detailed information has dimensionally decreased. Required reporting systems have not taken advantage of this factor.

Sangster (2018) examined accounting history and explained the evolution of record keeping, from Florentine traders to Pacioli formalizing double entry bookkeeping and related recordkeeping to the needs of businesses. As the scope of business changed, so did the nature of the corporate form, with the rise of the limited liability firm superseding partnerships. Building on this evolutionary process, Alles, Dai, and Vasarhelyi (2021) proposed “Reporting 4.0,” a new financial reporting model. Its objective is to turn financial reporting from “one size fits all” to mass customization by offering users individually customized information with mass production efficiency (Piller and Müller 2004). Alles et al. (2021) argue that Reporting 4.0 will be characterized by “an app that is similar to the use of the intelligence inherent in technology to increase the value-added of the reporting model to a heterogeneous group of users by moving away from being one size fits all.” This paper extends these ideas by proposing a flexible, tailorable implementation of reporting where entities can be made comparable as a product of these environmental changes. This would adapt current business reporting to extant business needs, not anachronistic processes.

Vasarhelyi and Alles (2008) proposed creating a “technological testbed” to examine alternative reporting mechanisms that better reflect the modern business, reporting, and market environments. The app proposed by Alles et al. (2021) expands the Galileo model (Vasarhelyi and Alles 2008)³ with features to include exogenous information and an intelligent prediction module customized to its users. There has been an evolutionary change in the reporting environment, with Extensible Business Reporting Language (XBRL) now mandated as the medium of communicating large corporation financial information in the U.S. Its use has broadened from its initial application of tagging the face of the financial statements to include data contained in the footnotes.

Figure 1 describes the proposed initial information schemata, where internal corporate ERP data are complemented by external cross-sectional data and delivered to a relational database. This database would allow aggregated and detailed (down-drilled) access to information. A filtering schema would limit the accessibility of proprietary information. Automated software (“intelligent agents”) would constantly access and highlight to users relevant data in the form of krons (routines that are time activated) and Daemons (routines that are circumstance activated) (Vasarhelyi and Hoitash 2005). Stakeholders could use pre-set style sheets (a template for which data and how is to be presented) and/or build their own reporting frameworks instead of using the one-size-fits-all company-chosen statement format in the 10-K.

Potential expansions of this conceptualization (Figure 2) include: (1) a wide set of exogenous variables, (2) real-time intelligent agents to facilitate data analysis (including data prediction), and (3) interface with public or private data and blockchains where organizations could store comments made by participants or many other forms of relevant information in the value chain. These enhanced capabilities utilize existing technologies, although they have not yet been adequately implemented in organizational reporting.

While the SEC has issued many new regulations concerning GAAP reporting over the last decade and a half, the fundamentals of the annual 10-K are the same. Reporting standards have remained stagnant and antiquated, resulting in a substantial decrease in the value of financial statements (Lev and Gu 2016). Hence, it is time to move on from the testbeds proposed by Vasarhelyi and Alles (2008) to actual implementations of a new financial reporting system that takes into consideration:

1. The informational needs of the sophisticated participants of the modern economy.

2. The capabilities of modern technologies to communicate and analyze vast amounts of data at a very low cost.

3. Paradigm shifts that render archaic tradeoffs between effort in information preparation and benefits for stakeholders from the 1930s irrelevant to modern economies.

4. Complex multinational companies with global supply chains and sophisticated financial engineering.

5. Smartphone adoption by billions of people combined with cloud computing, creating an unprecedented demand for information.

The app-based economy of today is based on the principle of allowing users to shape their experience by picking and choosing services through user-friendly interfaces that obviate their need to understand the underlying technological infrastructure. The key is choice, allowing users to either tailor comparisons or use pre-set style sheets provided in shared libraries or templates that a broad set of stakeholders share.

This paper similarly proposes shifting from a supply-constrained model of financial reporting—one in which regulators decide what required financial information is provided to which stakeholders and in what form—to one that is demand driven. Users would choose how they consume data, in what format, and at what level of detail, aggregation, and sophistication. As with entertainment, the objective is to recognize that with modern network technology, there is no longer any reason for a one size fits all solution. Such a restricted outcome may have been optimal when the cost of preparing and communicating reports was excessive, but those constraints no longer apply.

XBRL dramatically changes both the cost equation and the communication medium. Data need no longer be consolidated into the regulated format of the three main financial statements and accompanied by a large number of text-based free-form information. While traditional reporting may be convenient for unsophisticated stakeholders, financial analysts often simply extract the raw data from the 10-K and then discard the document. XBRL makes that process of disaggregation and extraction a push-button operation (Cong, Du, and Vasarhelyi 2018).

Some highly sophisticated users are already shaping how they consume financial information into a demand-driven process by using data provided in the 10-K as inputs into their models of firm performance and value. But most stakeholders lack the ability to undertake this kind of disaggregation and reaggregation exercise that places them at a disadvantage relative to market sophisticates. That is why this paper argues for using financial reporting to democratize reporting, with all users able to view financial information in the way that suits them best.

Moreover, since Vasarhelyi and Alles (2008) first made their proposal to rethink accounting reports, the demand for information about businesses has increased to encompass non-financial data, particularly data related to Corporate Social Responsibility (CSR), governance, cybersecurity, and environmental impact. Some of that information is provided by the firm through mandated disclosures. Some disclosure it voluntarily; others include it in non-accounting disclosures to other bodies (i.e., the stock exchange, the EPA, and state regulators); furthermore, much comes from external watchdog sources, like NGOs. The advent of social media, blogs, Twitter, etc. has resulted in the emergence of “Big Data,” which also enables significant insights about a business directly from users and investors, such as consumer satisfaction.

To access this extensive range of information that goes well beyond the mandated 10-K, stakeholders must go to various sources. Some sources are free, while others, such as Bloomberg, are not. The Extended Business Reporting App (EBRA) aims for it to be a portal that combines these disparate data sources or provides links to these other sites. Notably, the EBRA is meant to operate as a supervised wiki, meaning that users and the reporting entity can provide their links to helpful information sources, which can become available to other app users. An extension of this idea is to allow third-party add-ons to the app that are either free or for a fee, such as proprietary valuation models. Open sourcing of code, applications, and information has emerged as a major force in the modern economy. It is premature to discuss ownership and governance of EBRA, but there are many models of similar initiatives in operation today that can be examined for best practices, such as XBRL, the IFRS Foundation, Ethereum, Bitcoin, and the ISO system.

To illustrate how customization of EBRA might work, consider the Economic Value Added or EVA™ model, the Stern-Stewart consultancy's signature product (hence, the trademark). While the basic formula for EVA can be found in any accounting or finance textbook, clients employ Stern-Stewart because they undertake over 200 proprietary adjustments to GAAP data. For example, they are known to capitalize R&D and advertising expenses rather than treat them as period costs, as GAAP mandates. This fee-based add-on can be used with the app, making these insights available to a much larger group of stakeholders.

The capability of EBRA would be enhanced if XBRL was applied to the final numbers on the 10-K the underlying general ledger data. That is the objective of XBRL and the Audit Data Standard.⁴ Tagging, combined with an app interface, would expand the capabilities of demand-driven reporting. For example, instead of being restricted to the use of GAAP, a close approximation of reporting using IFRS would become feasible. Like Stern-Stewart, users would be able to undertake accounting methods not allowed for under either GAAP or IFRS.

In today's social media environment, companies such as Facebook, Apple, Microsoft, Amazon, Tik Tok, and Alphabet have collected many dimensions of data about billions of individuals, organizations, and governments. Figure 3 illustrates many sources of information obtained by Alphabet (Google) just from some of its numerous subsidiaries. These data are now highly valued and traded with other entities to be used in monetized predictive algorithms (Zuboff 2015). Similarly, data from multiple sources about a business's production and sales sides can be aggregated, providing a holistic view of the business.

Big Data about customers from such sources as patterns of website views, comments, and sales data are collected by companies such as Google, Facebook, and Amazon. The value of this information is reflected in the fact that such data are now routinely transacted (Cheong, Sokol, and Wang 2022). Users of EBRA could acquire this information and link to the most traditional data continuously, assuming links to those databases were possible.

The traditional process of disclosure, collection, and analysis is too static and episodic compared to the dynamism of the modern information environment. Quarterly, let alone annual summaries of information, like annual income, owners' equity, receivables, and payables, are inadequate for even long-term analyses, let alone managing day-to-day, or even millisecond-to-millisecond, trading needs. Stakeholders need continuous data streams to facilitate the analysis that underlies real-time business monitoring and automatic trading. Cho, Vasarhelyi, and Zhang (2019) discussed the future of data ecosystems and potential dilemmas. It raised the issue of integrating different dataflows into a data ecosystem supporting a wide set of decisions. For example, a strong stream of negative posts relative to a company's high-selling product needs to be parsed, understood, and somehow reflected in the views of the management, investors, and regulators. EBRA can provide continuous data access to users when links are connected to frequent-updated data sources, like stock trading prices, peer companies' positions, and trading volumes.

App-based reporting has two objectives:

1. To bring together in one location business information that is now distributed in many different locations (often, different web portals), such as the SEC's EDGAR, corporate websites, links to NGO websites, focused on various CSR and good governance issues. There would also be links to third-party providers, such as Bloomberg, stockbrokers, social media extraction providers, and financial analysts.

2. To give stakeholders more control over how they process the data they use to make decisions, maximizing data utility for each user's needs. Based on user preferences, the app may provide raw data that can be downloaded and analyzed by a user's analytic tools or offer a comprehensive summary and/or automatic decision implementation. Third-party software, either open-source or subscription-based, may be integrated with EBRA to facilitate data presentation.

The desired consequences of app use include reducing information asymmetries between investors, greater market participation, improved financial literacy, lowering the expenses of searching for relevant information, and increasing the ability to assimilate large amounts of disparate information. The economic rationale for the app-based reporting model is lowering the transaction cost of users going to different portals to obtain desired data and then processing it into the desired presentation format

Adopting XBRL reduces the cost of capital for businesses. Despite this, tagging does not create new information or change any underlying financial statements. By design, data tagging is entirely invisible to the readers of those statements. All XBRL does is make it easier for software to access the tagged data and digitally transmit it across the internet. What XBRL replaces is capturing data from a paper document to a database and creating consistency in classification.

App-based reporting takes the benefits of XBRL and leverages them to the next level. With XBRL already in practice, EBRA can take advantage of the inherent flexibility of tagged data. Instead of simply reproducing the paper 10-K in digital format, the app-based system would enable users to shape the way in which that raw data are both analyzed and presented.

Just like XBRL removes the need to transcribe paper 10-Ks for all businesses, the EBRA reduces the transaction cost of collecting relevant information for all users. Moreover, unlike XBRL, the app is demand-driven, so data providers can vend their data through the app, either for free or for a charge, and let users decide whether it is worth acquiring.

The app aims to reduce the cognitive load and workload (J. Rose, Roberts, and M. Rose 2004; Parte, Garvey and Gonzalo-Angulo 2018) of users by allowing them to shape how they consume data.

In addition to firms' financial information, the app will utilize available exogenous data. With the advent of substantially improved computer power, large corporate and cloud data storage capabilities, improved algorithms, and the availability of large amounts of exogenous data, the use of an expanded set of available information is becoming more realistic (Cho et al. 2019). To better understand the demand for information about a business, this paper attempts an illustration of readily available information to a non-professional investor. Starbucks is a sample business, and Google and other search resources are used to find relevant information.

The investor decision process can be framed in terms of risk versus return. While Starbucks data are obtainable in publicly available 10-K reports, investors are interested in more than the information provided to the SEC. Some of this extra information is publicly available on the company's official website, Yahoo! Finance, restaurant rating websites, Google Shop, job service websites, and more. Some information is available on private information services like Bloomberg.

For example, Starbucks' environmental footprint, diversity and equality information, and insights into its intellectual properties are available on the company's website. The company's ESG score and comparisons to its competitors are available on Yahoo! Finance. Product popularity measures are available on restaurant ratings (such as Yelp) and company websites. Information about the company's employees, turnover, loyalty, and more, may be derived from job service sites like Indeed or Glassdoor.com.

While the exogenous data presented are publicly available, some may only be available in private data sources. Such data would include historical download trends, global penetration, detailed comparisons to competitors, and other featured research, all of which are available on Bloomberg. Appendix A shows Starbucks' census data. The fact that information is easily available does not imply its availability to all market participants. Investors must expend effort to obtain and use the information, and unsophisticated investors might lack the knowledge to even know to look or where to do so.

When choosing a specific stock, investors are most likely to buy rather than sell attention-grabbing stocks that have experienced extreme returns, inflated trading volume, or been the subject of extensive media coverage (Gavish, Qadan, and Yagil 2021). However, this temporary positive pressure does not generate superior returns, and may reverse afterward (Da, Engelberg, and Gao 2011). The demand for information not available in financial statements may be observed in internet searches. Drake, Roulstone, and Thornock (2012) found that investors' abnormal Google searches increased about two weeks before an earnings announcement, spike markedly at the announcement, and continued at high levels for a period after the announcement. Abnormally high google searches were also correlated with higher stock prices in the short-run (Da et al. 2011) and higher buying/selling volume for unsophisticated/sophisticated investors (Gavish et al. 2021). Bartov, Faurel, and Mohanram (2018) find that Twitter's aggregate opinion predicts a firm's forthcoming quarterly earnings and announcement returns.

Almost two decades ago, the Jenkins Committee found the financial reporting model insufficient for user needs and called for greater disclosure of a broader set of non-financial information (AICPA 1994). More recently, this understanding that more non-financial information is required has been translated into two commonly used reports: Corporate Social Responsibilities (CSR) and Environment, Social, and Governance (ESG).

Cohen, Holder-Webb, Nath, and Wood (2011) found that unsophisticated investors expressed high levels of use of three classes of non-financial information: economic performance indicators, governance, and CSR information. While respondents expressed even higher levels of interest in the future uses of all classes, they preferred their information to be provided through audited filings or from third parties, possibly reflecting concern for the integrity and reliability of non-financial disclosures.

Using survey data from global investment organizations, Amel-Zadeh and Serafeim (2018) find that the most frequent motivation for ESG information is investment performance, followed by client demand, product strategy, and ethical considerations. A critical impediment to using ESG information is the lack of adequate reporting standards. A significant step toward remedying this problem is the recent decision by the IFRS Foundation to create the International Sustainability Standards Board (ISSB) to parallel its International Financial Standards Board, thus placing ESG reporting on the same level as traditional financial reporting.⁵ As the ISSB solidifies ESG standards, a reporting app capable of consolidating information about a business in one easily accessible flexible location becomes ever more crucial for ESG-sensitive investors.

The ISSB has issued an example of what it envisages an ESG report would look like, using the example of Netflix.⁶ While it is a commendable document, it is in Portable Document Format (PDF) format and is 42 pages long. It would be much easier to digest and analyze if the data contained was available in a disaggregated digital app.

Being native digital, the EBRA will not only be able to create many types of reports but seamlessly move from one report to another through aggregation and disaggregation. Furthermore, views could be customized, stored, group-shared, sold as a product, copyrighted, etc. These instances are the “style sheets” prescribed in the Galileo model of Vasarhelyi and Alles (2008) and shown in Figure 4.

EBRA can be designed in a wide set of structures and contents. This section illustrates two simple reporting system designs/capabilities.⁸

EBRA would use XBRL representation to: extract data directly from EDGAR, enable hyperlinks to move from aggregated data to detailed underlying statements, use a relational database structure to facilitate customized data comparisons, perform ratio analysis, and build customized formulas. These user-defined metrics can be stored as a generic template and be used to analyze other firms.

Currently, mandatory disclosures of important internal financial data, such as the 10-K and 10-Q, are disclosed in the EDGAR database. However, these disclosures are not integrated and are compared to limited historical data on a year-to-year basis. As a result, stakeholders need to gather historical data from different reports. For example, cash flow data disclosure in the 10-Ks is provided only with data for the previous two years. To get a firm's public data on quarterly cash flows in the current year, stakeholders must gather the data by opening at least three statements: one 10-K and two 10-Qs.

Stakeholders can satisfy their demand for custom views of business data, for example, calculating “EBITDA” and the quick ratio of a business. With XBRL, stakeholders can jump to a 10-K item by clicking on the hyperlink in the table of content. However, acquiring a specific internal financial statistic and incorporating it into an analysis is still time-consuming. For instance, the two ways for stakeholders to calculate “EBITDA” are:

1. Net Income + Interest + Taxes + Depreciation + Amortization

2. Operating Profit + Depreciation + Amortization

Stakeholders must extract three or five items from an income statement and then use the formulas to get the result. If they are not using third-party data such as Yahoo! Finance, stakeholders must repeat the above process for each firm on each financial index. The process is repetitive and time-consuming. Design 1 aggregate and visualizes internal financial data. The application integrates financial data in the database layer by storing financial data from reports (such as “cash and cash equivalents” from all 10-Ks and 10-Qs). The app connects the database to any pre-built formula in the aggregated layer. Users can see visualized data, customize the visualization and customize the formula in the UI layer. Figure 5 illustrates the integrated view of Design 1, and the ensuing figures display details of its parts.

1. By examining content from FinTech companies, key financial indexes should be placed in a pre-designed dashboard (Figure 6). All the data can be extracted directly from the database layer.

2. The app enables the comparison among current values and previous values by using absolute volume or change ratios.

3. Users can customize the dashboard by selecting any other pre-built financial index to replace the four indexes shown in Figure 6 (total asset, total revenue, net income, and profit margin).

4. Users can build customized formulae to calculate special financial indexes if they create an app account. The customized templates can be saved to analyze the other firms.

Users would click “Details” to see underlying data. For instance, if app users are interested in the details of total assets, they can click the “Details” button to jump to the balance sheet details, as displayed in Figure 7.

The two main functions of the UI layer are visualization and customization. Users select financial indexes and compare them over a customized period. For example, the “EBITDA” and “Net Income” indexes can be visualized and compared. Figure 8 illustrates “Customized Visualization” using quarterly reporting from 2020 to the present. Users customize the reporting period and frequency depending on their preference. Cross-firm index selection can be enabled. For example, “EBITDA” can be compared between Starbucks and Netflix, and comparisons visualized. “Details” can be clicked to peruse underlying financial statements. In this case, the users would click “Details” of “EBITA—Current” to see the latest income statement (Figure 9).

Financial statements present financial disclosure other than financial indexes. The EBRA can aggregate such data and provide stakeholders with pre-built analysis. Figure 10 takes the international segment section of Starbucks as an example. The pre-built analysis in the segment report is the change ratio of the segment revenue compared to the previous year. Users get the segment revenue volume, the change ratio, and the ranks of the reporting segments in an integrated dashboard.

Users see the segment report details by clicking the “Details” button. If the “Details” take other financial footnotes as a reference, users can click “Notes” with a hyperlink to jump to the reference. The link will guide the users to “Note 1” in Figure 11.

Firms often share voluntary reports and SEC-required documents on different websites, while EDGAR only collects SEC-required files. These various reports are integrated for ease of use. The links to the reports are stored.

Take Starbucks as an example—mandatory reports, such as 10-Q, 10-K, and S-8, are sourced from the EDGAR database. Voluntary disclosures like Starbucks' “Annual Letter to Shareholders,” and its ESG report, “Global Environmental and Social Impact Report,” are available on Starbucks' official website. The EBRA displays report names in the UI layer and connect with the database layer's corresponding hyperlinks. The UI layer's search and content filter functions help the audience find their desired content quickly. Figure 12 shows the UI layer illustration.

The app enables firms to provide voluntary exogenous evidence. The firms first specify the textual content where they want to attach evidence, then embed hyperlinks into the statement so stakeholders can study the evidence. For example, assuming that Starbucks tends to emphasize their investment in the Mobile App, they first highlight the phrase “Starbucks Mobile App” in their financial statement (Figure 13). Users' comments and the ratings of the “Starbuck Mobile App” can be provided as exogenous evidence in financial disclosures. Thus, Starbucks can then embed the hyperlink (Figure 14) on the Apple Store to support its statements.

Material contingency issues, such as legal proceedings, can be attached using a hyperlink as mandatory exogenous evidence. For example, Starbucks discloses a material processing court case—Council for Education and Research on Toxics v. Starbucks Corporation et al.—in their FY 2021 10-K. California court records are an acceptable exogenous source for contingency disclosure. Stakeholders can keep up to date using the link to the case summary (Figure 15).

While ABRA may prove by itself to be a very valuable tool, standard setters must expand the extant supply of information. The following suggestions may improve the App's ability to provide the kind of information that would help to make better decisions:

1. Focus on Strategy and relate it to the results—an analysis of the company's Strategy and how its actions reflect the attempt to achieve this Strategy. The company's Strategy is usually explained in the MDA. The analysis of the company's performance should include its ability to achieve its own goals. The FASB should encourage firms to disclose how their new acquisitions align with their Strategy, how their results reflect the adherence to their Strategy, etc.

2. Enable verification of managerial estimates—while there are many very important estimates provided by management, there is no requirement for verification of such estimates. This invites careless, manipulative, and misleading estimates (Lev and Gu 2016).

3. More detailed information—if one of the most substantial criticism against traditional financial reporting was that the increased level of detail causes the user to be unable to determine what is relevant and what is not (Lev and Gu 2016) the use of the suggested app eliminates this problem. Firms should be required to enter as much information as possible, even down to the transaction level; however, this information should then be classified based on its relevance to the specific query. For example, if the user is interested in analyzing Amazon, it does not make sense to look at the company as a whole, rather, the need is to look at the different sections and their specific activities. The app will allow to examine each business section in itself and compare it to the relevant section in other companies. If the user is interested in examining Amazon's cloud activities, AWS, the data from that activity alone will be presented and compared to Microsoft Azure and Google Cloud Platform. Any specific transactions for these sections will also be presented and related to the company's Strategy.

4. More extensive use and disaggregation of Fair Value Accounting—one common concern is that, in the absence of market prices, fair value has “too much” estimation error (Barth 2018). However, as reflected in equity share prices and returns, fair value is more relevant and correlated with investors' valuation decisions than historical cost prices (Barth, Beaver, and Landsman 2001). An app that displays the separate components of fair value changes and allows the user to examine and “play” with the numbers and their effects might solve the problem of relying on those estimates presented by management.

5. Risk and uncertainties—financial reports contain qualitative information about risk (mainly market, liquidity, and credit). More detailed and tagged quantitative information might allow users to make better decisions. Badia, Barth, Duro, and Ormazabal (2020) find that required disclosure in Canada about the dispersion of the value of oil and gas reserves can aid investors in predicting risk for these firms.

6. Judgment—many accounting reporting decisions require management to make a judgment call. Firms should disclose which reporting decisions required such a call and provide information about how the information could have been presented differently and its effect.

Traditional financial reporting does not satisfy the needs of the modern stakeholders of a business, nor does it utilize the technologies necessary to present a retrospective, current, or predictive impression of modern businesses. Furthermore, the emergence of stakeholders' preoccupation with ESG requires a major expansion of the variety of data being employed, the frequency of reporting, and the mode of informative presentations. Traditional variables, such as owners' equity, intangibles, and a multiplicity of footnotes, fail to facilitate comparability among entities, the measurement of relevant variables, the base for prediction analytics, and timely action by firms stockholders, localities, and so on.

This paper attempts to rethink and restructure the reporting by demonstrating how traditional variables can be collected and integrated into a flexible reporting app with both required disclosures and exogenous data. The proposed Extended Business Reporting app allows for flexible disclosure and comparison among entities, reduces information asymmetries between stakeholders and reduces the cost of accessing, communicating and comprehending data about a business.