textsfinance
Textsfinance is an interdisciplinary concept describing the use of textual data for financial analysis and decision making. It encompasses methods and practices that extract structured information from unstructured texts to support investment research, risk management, regulatory compliance, and market monitoring. The term often refers to the application of natural language processing, machine learning, and information extraction to sources such as financial news articles, earnings call transcripts, regulator filings (for example, company annual reports and 10-K/10-Q documents), central bank communications, analyst reports, and social media posts.
Typical workflows include data collection and cleaning, sentiment or event detection, topic modeling, and the construction
Challenges include data quality and noise, labeling scarcity, domain-specific language, imbalanced signals, multilingual text, and regulatory
The field has grown with advances in NLP and access to large financial text corpora, but it