Bridging the Gap Between Natural Language and Market Dynamics via High-Dimensional Representation Learning
arXiv:2605.30652v1 Announce Type: new Abstract: Traditional multi-modal financial forecasting often relies on scalar sentiment scores, which fail to capture the nuances of financial news. To address this information loss, this paper explores high-dimensional representation learning by replacing discrete polarity ratings with dense FinBERT embeddings within a Transformer-based forecasting architecture. We benchmarked various embedding strategies on the FNSPID dataset, including raw embeddings, attention-weighted aggregation, and a custom Siamese network. While the attention-based mechanism stru
The increasing sophistication of large language models and representation learning techniques allows for more nuanced analysis of unstructured financial data than previously possible.
Sophisticated financial forecasting is crucial for identifying market trends and making informed investment decisions, impacting various sectors from hedge funds to corporate strategy.
This research moves beyond scalar sentiment to high-dimensional embeddings for financial news, potentially leading to more accurate and granular market predictions.
- · Quantitative hedge funds
- · Financial data providers
- · AI/ML researchers in finance
- · High-frequency traders
- · Traditional sentiment analysis firms
- · Discretionary fundamental analysts
Improved accuracy in predicting market movements based on natural language inputs.
Increased efficiency and potential automation in parts of the financial analysis workflow.
Widened gap between firms leveraging advanced AI and those relying on older, less sophisticated methods, leading to competitive advantages.
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Read at arXiv cs.LG