Study on the Nonlinear Causal Impact of Investor Sentiment on Futures Pricing Efficiency: Based on Generalized Random Forest and Dual Machine Learning Methods
Bibliographic record
Abstract
This study explores the impact of investor sentiment on futures pricing efficiency and employs dual machine learning (DML) and generalized random forest (GRF) methods for causal inference analysis. By constructing an investor sentiment index and combining it with pricing efficiency indicators for the futures market, empirical results demonstrate that sentiment has a significant positive impact on futures pricing efficiency, particularly in contexts of high market volatility, where the impact of sentiment fluctuations on pricing bias is more pronounced. Furthermore, the study reveals the heterogeneity of sentiment effects across different market phases, with the impact of sentiment on pricing efficiency being more pronounced in bull markets and relatively weaker in bear and volatile markets. This study provides new empirical evidence for understanding the relationship between investor sentiment and futures market pricing efficiency and offers theoretical support for future market regulation and policymaking.
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How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".