An Optimized Machine Learning Model for Stock Trend Anticipation
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
Security market is an economical-volatile in nature as it is driven by not only based on historical prices various unpredictable external factors like financial news, changes in socio-political issues and natural calamities happened in real world; hence its forecasting is a challenging task for traders. To gain profits and to overcome any crisis in financial market, it is essential to have a very accurate calculation of future trends by for the investors. The trend prediction results can be used as recommendations for investors as to whether they should buy or sell. Feature selection, dimensionality reduction and optimization techniques can be integrated with emerging advanced machine learning models, to get improvised prediction in terms of quality, performance, security and for effective assessment external factors role in stock market nonlinear signals. In this empirical research work, a set of hybrid models were built and their predictive abilities were compared to find consistent model. This work implies the base model, boosted model and deep learning model along with optimization techniques. From the experimental result, the optimized deep learning model, ABC-LSTM was turned out superior to all other considered financial models LSSVM, Gradient Boost, LSTM, ABC-LSSVM, ABC-Gradient Boost by showing best Mean Absolute Percentage Error (MAPE) value, which was low.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 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