An automated financial indices-processing scheme for classifying market liquidity regimes
Bibliographic record
Abstract
A multivariate hidden Markov model (HMM)-based approach is developed to capture simultaneously the regime-switching dynamics of four financial market indicators: Treasury-Euro Dollar rate spread, US dollar index, volatility index and S&P 500 bid-ask spread. These indicators exhibit stochasticity, mean reversion, spikes and state memory, and they are deemed to drive the main characteristics of liquidity risk and regarded to mirror financial markets' liquidity levels. In this paper, an online system is proposed in which observed indicators are processed and the results are then interfaced with an advanced alert mechanism that gives out appropriate measures. In particular, two stochastic models, with HMM-modulated parameters switching between liquidity regimes, are integrated to capture the evolutions of the four time series or their transformations. Parameter estimation is accomplished by deriving adaptive multivariate filters. Indicators' joint empirical characteristics are captured well and useful early warnings are obtained for occurrence prediction of illiquidity episodes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".