Deep Learning Modelling of Systemic Financial Risk
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper attempts to improve the ability to prevent systemic financial risk (SFR). Based on the generation mechanism of China's SFR, this paper presents an evaluation index system for financial risks, and then sets up a deep learning (DL) model for SFR prewarning. The proposed model inherits the merits of the DL in nonlinear approximation and selflearning, and overcomes the defects of conventional neural network (NN) model. Our model can capture the multi-dimensional changes in risk evaluation indices, and make accurate prewarning of the SFR. Our model can capture the multi-dimensional changes in risk evaluation indices, and make accurate prewarning of the SFR. Finally, empirical analysis proves that our model can retain much of the original features, and achieve highly accurate prewarning of the SFR. The research results provide technical support to risk regulation and decision-making of financial authorities.
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.
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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it