Cross-Border Trading and Price Discovery
Bibliographic record
Abstract
Many major corporations of the wolrd have cross-listed their stocks in overseas exchanges, with their equilibrium stock prices determined in multiple locations. This paper examines the contribution of cross-listings to the price discovery for internationally traded securities and discusses policy implications. Specifically, using a sample of Canadian stocks listed on the Toronto Stock Exchange (TSE) that are also listed in the U.S., we study the contribution of U.S. exchange to the price discovery for these stocks. The main findings are as follows. First, the prices on both TSE and U.S. exchange are non-stationary with a unit root. However, they are cointegrated with the equality of prices holding as an equilibrium relationship. Second, adjustments maintaining the cointegration equilibrium between the prices on TSE and U.S. exchange occur on both exchanges, implying that both exchanges contribute to the price discovery for sample stocks. That is, not only do the U.S. prices adjust to the Toronto prices but they also provide feedback to the latter so that the TSE prices adjust to the U.S. prices. The relative contribution of U.S. exchange to the price discovery ranges from 0.3 % to 98.0%, with an average of 38.5%. Third, regression analysis indicates that the TSE share of total adjustment in prices is directly related to the U.S. share of total trading in a stock and to the U.S. share of firm’s sales, and inversely related to the ratio of bid-ask spreads on U.S. exchange and TSE. We do not find the venue of U.S. listing, NYSE or NASDAQ, to affect the TSE share of total adjustment
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".