Neural Titans in market prediction: MLP, transformer, & hybrid models across G-7 and China
Bibliographic record
Abstract
This study aims to conduct a comparative evaluation of eight state-of-the-art forecasting models – TimeMixer, PatchTST, iTransformer, NHITS, NBEATS, SOFTS, RMoK, and BiTCN – representing diverse deep learning architectures, neural basis expansion techniques, and hybrid approaches, for predicting stock market prices. We assess their performance across seven major global indices: the Shanghai Stock Exchange, S&P/TSX Composite, FTSE 100, DAX, CAC 40, S&P 500, and Nikkei 225, using rigorous metrics (MAE, SMAPE and RMSE). Our findings indicate that neural basis expansion models (NHITS, NBEATS) achieve superior overall accuracy (aggregated MAE: 0.013–0.014), particularly in North American and Asian markets. In contrast, transformer-based architectures exhibit market-specific strengths, with iTransformer delivering exceptional performance on Canada’s S&P/TSX (MAE: 0.003). Notably, European indices (DAX, CAC 40) present significant challenges, where BiTCN and RMoK underperform (MAE: 0.032–0.038), suggesting limitations in modelling abrupt volatility shifts characteristic of these markets. These results highlight critical regional performance variations and provide insights into architectural efficacy under diverse market conditions.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".