financial-nlp

KB국민은행에서 제공하는 경제/금융 도메인에 특화된 한국어 ALBERT 모델

The EarningsCall Python library provides convenient access to the EarningsCall API.

A symbolic benchmark for verifiable chain-of-thought financial reasoning. Includes executable templates, 58 topics across 12 domains, and ChainEval metrics.

Structured market intelligence for Indian financial events, macro context, and sector impact.

Research on all kind of NLP in market forecasting, expert estimation, etc.

Interaction-centric behavioral modeling for earnings calls using multimodal neural fusion, text-audio divergence, and Q&A interaction dynamics.

🎯 Fine-tuning LLMs using LlamaFactory for financial intent understanding | Evaluating open-source models on OpenFinData benchmark | Full implementation with multiple models (Qwen2.5/ChatGLM3/Baichuan2/Llama3)

An open-source sell-side analyst that never sleeps. Screens stocks, runs DCF + reverse DCF, extracts earnings call signals, and ships an institutional-grade research note ;automatically.

The EarningsCall JavaScript library provides convenient access to the EarningsCall API from applications written in the JavaScript language (with TypeScript support).

7-signal financial text classifier for Reddit posts and market news — sentiment, directionality, quality, sarcasm, relevance, sector rotation. Free tier, no credit card.

Resource-efficient LLM distillation: Improving sustainability and reducing computational costs of Large Language Models in financial analytics through knowledge distillation.

Thinking-aware baselines & low-data LoRA/QLoRA post-training on FinQA — a controlled Qwen3-4B vs Qwen3-8B numerical-reasoning study.

AI-powered crypto sentiment analysis platform with real-time news monitoring, dual VADER/FinBERT models, FastAPI backend, Next.js dashboard, and Flutter mobile app.

Competition entry: end-to-end OfficeQA pipeline for Sentient Arena — retrieval, ledger extraction, and LLM reasoning over 10k+ financial documents

Living Literature Review on Memestock identification using NLP

Curated papers and datasets for large language models in finance

NLP pipeline that detects linguistic deception in earnings calls using FinBERT, sentence-BERT Q&A evasion scoring, and XGBoost trained on SEC restatement history.

This repository contains code for fine-tuning a BERT-based model for financial sentiment analysis. The project uses the Financial PhraseBank dataset to train a model that can classify financial texts as positive, neutral, or negative.

Point-in-time SEC filing retrieval: hybrid search, citation-verified answers with abstention, typed financial facts, filing diffs, and reproducible signal studies. Live at thefdre.com.

MCP server with a RAG pipeline over SEC EDGAR 10-K filings: section-aware chunking, BGE embeddings, ChromaDB, Loughran-McDonald sentiment, and year-over-year filing comparison.