Novel Financial Network Models Using Neuro Correlations and Applications
Bibliographic record
Abstract
This work introduces a novel approach to study financial networks and portfolio optimization by addressing the limitations of Pearson correlation, which only captures linear relationships between assets. The novelty of this study is to propose pairwise neuro correlations, which emphasize nonlinear relationships using non-linear neural network autoregressive (NNAR) models. The methodology involves, modeling non-stationary/non-linear stock prices using ARIMA and NNAR models, defining neuro correlations as the correlations of the innovations from NNAR models and constructing adjacency matrices for financial networks using neuro correlations. The study employs network-based community detection method to uncover hidden market structures and applies them to portfolio selection. Experimental results demonstrate that combining neural networks of stock prices and financial networks (for capturing asset relationships) enhances portfolio optimization, yielding portfolios with improved cumulative returns. This approach highlights the strengths of both techniques: neural networks' ability to model non-linearity and financial networks' ability to capture the relationship structure of assets in modeling complex financial relationships.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".