Features of realized volatility analysis and return predicting based on LGBM and RNN model
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 proposes a method of predicting the realized volatility of financial assets using LGBM and RNN models. The study utilizes Convolutional Neural Networks to construct sub-indicators capturing the liquidity and volatility of financial assets. These sub-indicators are used to develop comprehensive measures of liquidity and volatility. Lognormal random walk theory is applied to each asset dimension to price volatility for multiple assets, and the value of European options independent of path is obtained via multiple integration. Monte Carlo method is applied to solve the integral, which becomes inefficient in the case of high dimension and orthogonality. This study also involves leveraging LGB and other models to efficiently exploit data to create high returns and achieve the highest sharp ratio. The current dataset, which comes from a recognized international market maker, includes stock market data that is important for trade execution in the financial markets, particularly snapshots of the order book and executed trades. The study shows that the proposed method can accurately predict the realized volatility of financial assets.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it