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 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
The proliferation of advanced AI models like transformers and increasing computational power allows for more sophisticated applications in financial markets, making this research timely.
Sophisticated stock prediction models could offer significant advantages to institutional investors and potentially alter market dynamics by providing more accurate forecasting capabilities.
The ability to pre-train large language models on broad market data and fine-tune them for individual equities suggests a new methodological approach for quantitative finance.
- · Hedge Funds
- · Quantitative Trading Firms
- · AI/ML Research Firms
- · Institutional Investors
- · Traditional Equity Analysts
- · Less Technologically Advanced Investment Firms
Increased adoption of transformer-based models in high-frequency and algorithmic trading strategies.
Improved market efficiency and reduced arbitrage opportunities as more participants gain access to advanced predictive tools.
Potential for new forms of market manipulation or flash crashes if powerful AI models interact in unpredictable ways.
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Read at arXiv cs.LG