Bibliographic record
Abstract
This paper develops an economic setting to rationalize the use of the “least absolute shrinkage and selection operator” (LASSO) in estimating asset returns. In this setting, multiple traders engage in trading based on information extracted from historical asset prices. Facing model uncertainty in forecasting asset returns, these traders adopt robust-trading strategies. Within this context, the use of LASSO for estimating asset returns emerges endogenously as an equilibrium outcome. We further extend our analysis to rationalize the application of elastic-net estimation. Although LASSO-type strategies enhance traders’ profits by mitigating competition among them, they also introduce biases in trading decisions, which can adversely affect profitability. This dual effect highlights the nuanced tradeoffs associated with employing such estimation techniques in financial markets. This paper has been This paper was accepted by Will Cong for the Special Issue on AI for Finance and Business Decisions. Funding: This work was supported by the Social Sciences and Humanities Research Council of Canada [Grant 435-2021-0040] and the Bank of Canada [Fellowship]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2024.06127 .
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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 | 0.022 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".