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 models for stock prediction and examines the impact of pre-training strategies on forecasting performance. A transformer model was first pre-trained on the Toronto Stock Exchange Index (TSX) to predict intra-day return direction and subsequently fine-tuned on individual TSX stocks. The model was further adapted for return-value regression tasks. Performance was benchmarked against Long Short-Term Memory (LSTM) and XGBoost models. Pre-training on the market index improved the binary cross-entropy loss for individual stock prediction from 0.69 to 0.64. The fine-tuned transformer regression model achieved lower mean squared error than the benchmark models, although the ensemble and XGBoost models achieved higher average daily returns. In addition, a practical application was developed to deliver real-time stock predictions for trading support. Future work will focus on increasing transformer model capacity, incorporating broader global technical indicators, and filtering out stocks with low predictability.
| Subjects: | Statistical Finance (q-fin.ST); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.23962 [q-fin.ST] |
| (or arXiv:2605.23962v1 [q-fin.ST] for this version) | |
| https://doi.org/10.48550/arXiv.2605.23962 arXiv-issued DOI via DataCite (pending registration) |
From: Zhen Gao [view email]
[v1]
Tue, 12 May 2026 13:18:48 UTC (521 KB)
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