Features of realized volatility analysis and return predicting based on LGBM and RNN model
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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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".