Are You Better Off Trading Blocks in Volatile Markets? <i>Yes</i>
Why this work is in the frame
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Bibliographic record
Abstract
The volatility that gripped the market during the latter half of 2008 led many traders to change their trading behavior. The relative volume of block trades that were executed in the market decreased during this period. Many traders moved away from trading large blocks, to trading strategies that enabled them to spread their orders throughout the trading day, closer to a VWAP style, in order to reduce their risk. From an implementation shortfall perspective, however, the risk of spreading the order throughout the day is higher than executing the order quickly, as in the case of executing a large block. The author suggests that the fundamental reason for the difference in risk perspectives among traders, and the theoretical risk based on implementation shortfall measures, is the way traders are measured and compensated. Traders measured versus a VWAP benchmark see a greater variance in their performance in more volatile markets. In order to manage their career risk, they seek to match their trading behavior with their performance benchmark. The increased volatility in the market exacerbated the problem. The article compares the incurred risk of several strategies to demonstrate that less-aggressive strategies, such as VWAP, incur more risk than aggressive strategies that trade blocks opportunistically as they become available. The author suggests that the best way to address the misalignment between the investment objectives of the firm and the trading objectives of the desk is to measure traders’ performance using an implementation shortfall benchmark rather than a VWAP benchmark. He also suggests that incorporating risk into the post-trade measurement process leads to a harmonization of the objectives of traders and portfolio managers. <bold>TOPICS:</bold> <ext-link>Volatility measures</ext-link>, <ext-link>VAR and use of alternative risk measures of trading risk</ext-link>, <ext-link>performance measurement</ext-link>
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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