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Transformer model pre-trained on TSX improves stock prediction

Researchers have developed a transformer-based model for stock return prediction, utilizing pre-training on a market index to enhance performance. The model, pre-trained on the Toronto Stock Exchange Index (TSX) and then fine-tuned on individual stocks, showed improved loss metrics compared to baseline models. While achieving lower mean squared error in regression tasks, its ability to generate higher average daily returns was surpassed by ensemble and XGBoost models. A practical application was also created to provide real-time trading support. AI

IMPACT Demonstrates potential for advanced ML techniques to refine quantitative finance strategies and trading tools.

RANK_REASON Academic paper detailing a novel application of transformer models to financial forecasting. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Marie Soehl Coolsaet, Roberto Gallardo, Zhen Gao ·

    From Index to Equity: Pre-Training Transformers for Stock Return Prediction

    arXiv:2605.23962v1 Announce Type: cross Abstract: This research aims to leverage machine learning to improve stock price prediction and support informed investment decisions related to buying, selling, and holding assets. Specifically, this work investigates transformer-based mod…