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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".