A Napster for Financial Data? A Boon to Financial Planners and Individual Investors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Imagine this: any time of the day or night, and with just a few clicks of the mouse, a financial planner or an individual investor can access a company's present and past financial reports in extraordinary detail. In addition, an array of instant analyses of those data can be performed, which displays them either graphically or as conventional financial statements. Within seconds a user can compare a company's balance sheets with those of several competitors, examine an enterprise's debt-to-equity ratio in any fiscal period, chart its stock price history and download an audit client's major nonfinancial news. As if that's not enough, the same range of analyses can be performed for any public company worldwide and the information even can be converted into any currency. No way, you're probably thinking. At least not in my lifetime. Think again. All the underlying technology--XBRL (Extensible Business Reporting Language)--for it is available right now. All that's missing is a standardized protocol to implement it--and the first steps for creating such a protocol to perform those feats have been taken. A team comprising the Nasdaq Stock Market, Microsoft and PricewaterhouseCoopers just launched a pilot project designed to demonstrate that the concept of making both the data and the analysis tools available on the Internet is not only feasible but both practical and sought by financial professionals, investors and regulators. If you want to see what the future looks like, just point your Web browser to www.nasdaq.com/xbrl (see exhibit 1, below) and download a free demonstration file called the Excel Investor's Assistant. You'll need Excel 2000 or later to run the demo. [EXHIBIT 1 OMITTED] Once you're at the site, follow the instructions to install the pilot. When done, click on the file, Excel Investors Assistant.xls. You will be asked whether you want Excel to enable macros; you do, so click on the Enable Macros box (see exhibit 2, at right). [EXHIBIT 2 OMITTED] That will bring up the opening screen of Investor's Assistant (see exhibit 3, below). [EXHIBIT 3 OMITTED] The Investor's Assistant file contains two components: an Excel spreadsheet with built-in data analysis macros and formulas and a linked database that contains five years of financial information on 21 companies. The financial information, however, is not just raw data--that is, it's not just a compilation of financial numbers. Instead, each item in the database has been labeled with an XBRL tag that identifies the item as, for example, revenue, profit or short-range debt. The XBRL tags are based on standardized accounting definitions customized for various industries. (For more on XBRL, see Finally, Business Talks the Same Language, JofA, Aug.00, page 24.) A growing number of accounting software developers are incorporating XBRL into their products so a tag automatically gets attached to each item of financial information as it is entered and subsequently calculated by the accounting software. The tags eventually will be useful for anyone compiling both internal and external financial reports and tax returns. Because many industries have unique categories of financial data, an international consortium of more than 170 companies is preparing customized XBRL dictionaries, called taxonomies, that optimize the XBRL definitions so the tags can handle any special reporting structure. The goal is to make XBRL a fully universal financial information language that is both automatically attached to the data and transparent to the viewer. The XBRL International consortium was founded by the AICPA in 1999 and currently has active chapters in Australia, Canada, Germany, Japan, the United Kingdom, United States and Singapore. Chapters are being developed in Belgium, Hong Kong, India, Ireland, the Netherlands, New Zealand, South Africa, Spain, Sweden and Taiwan. …
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it