arXiv:2606.29347v1 Announce Type: new Abstract: Adaptive Financial Transformer (AFT) is proposed for stock return prediction under non-stationary financial markets. The model incorporates a Market Regime Encoder, an Adaptive Gate Network, and an Adaptive Financial Context module to dynamically bias self-attention based on semantic relationships between financial indicators. Unlike conventional Transformer architectures that treat all input features uniformly, the proposed approach groups 95 engineered financial features into 11 semantic categories and adapts attention according to latent marke
The increasing sophistication of AI models and the non-stationary nature of financial markets are driving demand for more adaptive and robust prediction systems.
Advanced AI for financial prediction can significantly alter market dynamics, providing new avenues for alpha generation and risk management, potentially shifting competitive landscapes among financial institutions.
Traditional quantitative models may be increasingly outmatched by AI systems that can dynamically adapt to market regimes and leverage an expanded set of semantic financial indicators.
- · Quantitative hedge funds with strong AI capabilities
- · Financial data providers
- · AI research labs focused on finance
- · High-frequency trading firms
- · Traditional active fund managers
- · Legacy financial institutions slow to adopt AI
- · Retail investors without advanced tools
Increased efficiency and potentially higher returns for institutions employing such AI models.
Accelerated adoption of similar adaptive AI architectures across various sectors beyond finance, as their capabilities are proven.
Elevated market volatility or flash crashes due to rapid, AI-driven market movements, necessitating new regulatory frameworks.
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Read at arXiv cs.LG