Bibliographic record
Abstract
This study adopts the textual network to describe the coordination among the interplay of words, where nodes represent words and nodes are connected if the corresponding words have co-occurrence pattern across documents. To study stock movements, we further proposed the sparse laplacian shrinkage logistic model (SLS_L) which can properly take into account the network connectivity structure. By using this approach, we investigated the relationship between Shenwan index and analysts' research reports. The securities analysts' research reports are crawled by a famous financial website in China: EastMoney, and are then parsed into time-series textual data. The empirical results show that the proposed SLS_L model outperforms alternatives including Lasso-Logistics (L_L) and MCP-Logistic (MCP_L) models by having better prediction performance. Besides, we search published literature and find the identified keywords with more lucid interpretations. Our study unveils some interesting findings that the efficient use of textual network is important to improve the predictive power as well as the semantic interpretability in stock market analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".