Intraday Predictability of Market Microstructure Statistics and Technical Trading Rules
Bibliographic record
Abstract
this paper examines the intraday predictability of price changes and quote revisions by using publicly available information on quotation and trading history for individual stocks, and technical trading rules on historical intraday price movements. The studied sample consists of the 35 stocks that constitute the Toronto Stock Exchange (TSE) 35 Index. The data are summarized into 30-minute intervals over a one year period (1990.7 - 1991.6). We extend previous work by using a more comprehensive information set of market microstructure statistics to capture the quotation and trading behaviors of market makers and public investors. Unlike previous studies, we assess the predictive power of market statistics, and the addition of technical trading rules. To this end, we first formulate econometric prediction models for quote revision and transaction price changes that reflect various micro structure theories. The information set of market statistics includes the most frequently used measures in the literature such as signed trading volumes, changes in the number of trades, effective bid/ask spread, depth imbalance, heavy and thin trading indicators, variables to examine the change in trading mechanism and week-end effect, The TSE opens with batch auction and switches to continuous trading for the reminder of the trading session. The weekend effect is due to information gathering during this period of no trading
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".