Hybrid neural network-based metaheuristics for prediction of financial markets: a case study on global gold market
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
Abstract Technical analysis indicators are popular tools in financial markets. These tools help investors to identify buy and sell signals with relatively large errors. The main goal of this study is to develop new practical methods to identify fake signals obtained from technical analysis indicators in the precious metals market. In this paper, we analyze these indicators in different ways based on the recorded signals for 10 months. The main novelty of this research is to propose hybrid neural network-based metaheuristic algorithms for analyzing them accurately while increasing the performance of the signals obtained from technical analysis indicators. We combine a convolutional neural network and a bidirectional gated recurrent unit whose hyperparameters are optimized using the firefly metaheuristic algorithm. To determine and select the most influential variables on the target variable, we use another successful recently developed metaheuristic, namely, the moth-flame optimization algorithm. Finally, we compare the performance of the proposed models with other state-of-the-art single and hybrid deep learning and machine learning methods from the literature. Finally, the main finding is that the proposed neural network-based metaheuristics can be useful as a decision support tool for investors to address and control the enormous uncertainties in the financial and precious metals markets.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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