Bibliographic record
Abstract
EXCHANGE-TRADED FUNDS 167 A round the world, exchange-traded funds have captured the interest of investment managers and exchanges. Both the number of products and places where they can be found has expanded tremendously in 2001 alone. Investors have much to learn about the investment opportunities of ETFs. Even in the U.S., where ETFs have been available since 1993, and where equity investing, especially passive investing is an accepted investment style, the retail investor is only just beginning to use ETFs. Far and away, the U.S. leads the ETF global market. Of the almost $90 billion invested globally, the U.S. share is 84.0%. Asia has a 7.6% share, Canada 4.3%, and Europe 3.5%. Average value traded shows an even stronger U.S. dominance, with approximately $4.2 billion traded daily, or 92.0% of global activity. Most of this trading comes from the QQQ, or Nasdaq-100 Tracking Stock, with an average value traded of $2.3 billion; $1.3 billion is traded for the Standard & Poor’s Depositary Receipts, or SPDRs (SPY). Europe, Asia, and Canada see approximately $174 million, $157 million, and $33 million, respectively, traded daily. The U.S. has had much more time to build assets, while other markets, especially Europe, have had to overcome regulatory, tax, and distribution hurdles. In Japan, securities laws had to be changed before ETFs could be introduced, and even then ETFs on only four local indexes are offered. In Europe, the seven exchanges and many more ruling authorities have significantly increased product development and marketing time and costs, but the same product will be cross-listed on many exchanges. For example, there are currently eight versions of a Dow Jones Euro STOXX 50 ETF, and more are planned. Some of these, like the Merrill Lynch (LDRS) products, are fully fungible across five exchanges. How multiple versions diminish liquidity, if they do, remains to be seen. Exchanges themselves are often the driving force behind multiple listings. The American Stock Exchange currently has agreements with the Singapore Exchange, EuroNext, and the Tokyo Stock Exchange. The thinking is that local investors are more likely to grow products on local indexes, and then foreigners can leverage that liquidity. The concept is the same whether a U.S. investor is investing in a CAC 40-based product or whether Singapore investors are investing in an S&P-500 based product. Multiple time zones also expand trading periods. Currency issues and regulatory differences may complicate cross-regional listings, yet many industry experts agree that 24-hour trading of selected ETFs will happen in less than three years. The future of ETF development looks bright. Launches of new ETFs or expansion of current ETFs to new places and inaugurations by exchanges offering ETFs for the first time will continue at a strong pace. More Global Survey of Exchange-Traded Funds
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.014 | 0.024 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".