Forecasting the volatility of educational firms based on HAR model and LSTM models considering sentiment and educational policy
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 study aims to investigate the impact of sentiment and policy on the volatility of educational stock prices by using HAR (Heterogeneous Auto Regressive) and LSTM (Long Short-Term Memory) models. We construct a weighted educational index volatility composed of nine publicly traded educational companies from the Shenzhen Stock Exchange and Shanghai Stock Exchange, and analyze the impact of sentiment and policy variables on the volatility of educational stock prices. We use OLS regression models and LSTM prediction models to analyze the data by developing various of models to investigate the impact of sentiment, education policies and their intersection effect. The empirical results show that the sentiment index and policy index have significant impacts on different time horizons of educational stock price volatility. The LSTM model confirms the effectiveness of including sentiment and policy variables in predicting educational stock price volatility. These findings carry several practical implications, particularly for investors, education-listed companies, and policymakers. And this study contributes to the literature by providing new evidence on the impact of sentiment and policy on the volatility of educational stock prices and by demonstrating the usefulness of combining HAR and LSTM models in predicting stock price volatility.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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