FinTexTS: Financial Text-Paired Time-Series Dataset via Semantic-Based and Multi-Level Pairing
arXiv:2603.02702v3 Announce Type: replace-cross Abstract: The financial domain involves a variety of important time-series problems. Recently, time-series analysis methods that jointly leverage textual and numerical information have gained increasing attention. Accordingly, numerous efforts have been made to construct text-paired time-series datasets in the financial domain. However, financial markets are characterized by complex interdependencies, in which a company's stock price is influenced not only by company-specific events but also by events in other companies and broader macroeconomic
The increasing sophistication of AI models for financial analysis necessitates richer, multi-modal datasets to capture complex market dynamics that pure numerical time-series or pure text data alone cannot.
This development is crucial for financial institutions seeking to gain an analytical edge by integrating qualitative textual data with quantitative time-series data, improving predictive models and risk assessment.
The availability of FinTexTS will likely accelerate research and development in AI models capable of processing and synthesizing diverse financial information, moving beyond traditional quantitative analysis.
- · AI researchers in finance
- · Quantitative hedge funds
- · Financial data providers
- · Algorithmic trading firms
- · Traditional financial analysts (without AI integration)
- · Purely quantitative financial models
- · Legacy financial data systems
Improved AI models for financial forecasting and sentiment analysis become more widely adopted across the financial industry.
Enhanced market efficiency and reduced arbitrage opportunities as AI models quickly process and react to new information.
Potential for new financial instruments or trading strategies based on deeper, multi-modal market understanding, leading to increased market volatility or stability depending on model collective behavior.
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Read at arXiv cs.LG