Neural Network-based Market Fluctuation Prediction Model Construction and Corporate Strategy Research in Business Administration
Bibliographic record
Abstract
Based on the complexity and nonlinear characteristics of market volatility, this paper proposes a market volatility prediction model that combines MA filtering method, autoregressive moving average (ARMA), and long-short-term memory (LSTM) neural network.And the back-propagation (BP) neural network is utilized to quantitatively solve the problem of corporate strategy formulation, and a corporate strategy formation model is established to determine the corporate strategic choice through the corporate strategic environment and strategic capabilities.In the ablation experiment, the combined model MA-ARMA-LSTM reduces its MSE, RMSE, MAE and MAPE by 0.0007, 0.0131, 0.0074 and 1.57%, respectively, compared to the ARMA model.Compared with common market volatility prediction models, the combined model has the smallest error in each assessment index.The output of BP neural network for corporate strategy selection is consistent with the expert ranking, which is verified to be in line with the actual business situation, indicating that the method in this paper can provide a reasonable corporate strategy.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".