Neural titans in market prediction: MLP, transformer, & hybrid models across G-7 and China

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info:eu-repo/semantics/openAccess

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Organizasyon Birimi
Yönetim Bilimleri Fakültesi, İktisat Bölümü
İktisat Bölümü, başta Türkiye ve çevre ülkeler olmak üzere küresel ekonomileri anlayan, var olan sorunları analiz ederken, iktisadi kuramları ve kavramları yetkin ve özgün bir şekilde kullanma becerisine sahip bireyler yetiştirmeyi amaçlamaktadır.

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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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Anahtar Kelimeler

Financial Market Forecasting, TSMixer, iTransformer, N-BEATS, N-HiTS

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Applied Economics Letters

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Hassan, A. D. & Ibrahim, M. M. (2025). Neural titans in market prediction: MLP, transformer, & hybrid models across G-7 and China. Applied Economics Letters, 1-6. https://www.doi.org/10.1080/13504851.2025.2497429

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