AI Applications and Limitations Across the Quantitative Investment Process
Summary
This overview surveys the use of large language models and other AI methods across quantitative investing. It describes extracting sentiment and information from financial text, combining text with time-series data for market and risk forecasts, supporting portfolio construction, and assisting with trading and order execution. It also discusses multi-agent systems, multimodal data that may include text, numerical and relational information, knowledge graphs, and images, alongside methods such as graph neural networks and reinforcement learning.
The review emphasizes that these applications face noisy and incomplete data, market shifts and nonlinear behavior, limited interpretability, demands for robustness and real-time processing, and substantial computational costs. It points toward finance-specific models, better multimodal integration, adaptive and causal methods, explainable research tools, and more coordinated agents. The supplied text summarizes a survey rather than reporting a single tested strategy or comparative results; it offers no evidence that the described systems reliably produce alpha or improve investment performance.
Key ideas
- Language models can extract sentiment and information from financial text for use as research features.
- AI systems may combine text, time series, relational data, knowledge graphs, and images.
- Potential applications include forecasting, portfolio support, trading decisions, and order execution.
- Data quality, changing market behavior, explainability, real-time requirements, and compute costs limit deployment.
- The overview proposes finance-specialized, adaptive, multimodal, and more explainable AI as research directions.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.