Forecasting the volatility of educational firms based on HAR model and LSTM models considering sentiment and educational policy
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".