Revealing the Driving Factors of the Chinese Baijiu Stock Market Based on Machine Learning
Bibliographic record
Abstract
Stock market volatility significantly impacts investors, policymakers, and industry development. While previous studies have identified key influencing factors, they have largely overlooked the unique volatility of the Chinese Baijiu stock market. This study adopts a complex network perspective, integrating transfer entropy, Peter and Clark momentary conditional independence (PCMCI), and interpretable machine learning methods to reveal the key drivers and mechanisms behind the market's volatility. The research identifies 10 critical factors spanning four dimensions: resources and agriculture, industry and manufacturing, services and consumption, and cross-domain sustainable development. Our findings indicate that the volatility of the Chinese Baijiu stock market is driven by a combination of agricultural, industrial, and sustainable development factors, highlighting the importance of industrial synergies. In addition to confirming the significance of traditional factors, this study also reveals the direct positive causal relationships of emerging industry variables, such as construction decoration, environmental protection, and energy, with the Chinese Baijiu stock market. These findings not only enhance the understanding of the dynamics of the Chinese Baijiu stock market but also provide a transferable research framework and methodology for other industries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".