A time-series stock price prediction and asset portfolio optimization model based on long- and short-term memory networks
Bibliographic record
Abstract
Aiming to address shortcomings in existing time series prediction models, this paper proposes an LSTM model enhanced by fused multi-scale convolutional attention (MCA-LSTM). We design the experimental parameters, construct a stock price dataset, and model the improved LSTM using individual stock closing prices, with prediction accuracy evaluated via RMSE, MAPE, and MAD. To assess the arbitrage and generalization performance of the MCA-LSTM portfolio model, we compare the application of the MCA-LSTM-BL model. Furthermore, within the framework of a mean semi-absolute deviation (MSAD) portfolio optimization model, we develop a new portfolio optimization approach based on return forecasting (MCA-LSTM+MSAD). The asset values and return predictions of various portfolio models are analyzed under transaction cost considerations, and the proposed MCA-LSTM+MSAD model achieves an excess return of 56.98%, consistently maintaining the highest portfolio value throughout the trading period. Overall, our findings indicate that the MCA-LSTM+MSAD model is a promising tool for portfolio optimization and warrants further development for real investment applications.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".