Financial NLP

In financial sentiment analysis, the devil has always been in the labeling. Most datasets — even the industry-standard Financial-Phrasebank — ask human annotators to tag headlines as positive, negative, or neutral. But here’s the problem: the market often disagrees. Take a headline reporting widening losses. Annotators marked it “negative.” Yet the stock rose the next day. Welcome to the disconnect. Enter FinMarBa, a bold new dataset that cuts out the middleman — the human — and lets the market itself do the labeling. Developed by Lefort et al. (2025), this 61,252-item dataset uses next-day price reactions to classify financial news, creating a labeling method that is empirically grounded, scalable, and (critically) aligned with investor behavior. ...

In the wake of the GameStop and AMC frenzies, financial firms and researchers have been racing to decode one question: Can social media sentiment predict stock prices? A new paper from researchers at Wrocław University of Science and Technology provides a sobering answer: not really. Despite employing advanced sentiment models—including a ChatGPT-annotated and emoji-savvy version of Financial-RoBERTa—the study found only weak and inconsistent relationships between sentiment and price movement for GME and AMC. ...

In finance, the devil isn’t just in the details—it’s in the narrative. That’s what makes this new study by Md Talha Mohsin both timely and essential: it directly evaluates how five top-tier LLMs—GPT-4, Claude 4 Opus, Perplexity, Gemini, and DeepSeek—perform in interpreting the most linguistically dense and strategically revealing part of corporate disclosures: the Business section of 10-K filings from the “Magnificent Seven” tech giants. Rather than focusing on raw numbers or sentiment snippets, the study asks: can these LLMs extract strategic intent, infer risk, and assess future outlooks the way human analysts do? ...

In the race to make language models financial analysts, a new benchmark is calling bluff on the hype. FinanceBench, introduced by a team of researchers from Amazon and academia, aims to test LLMs not just on text summarization or sentiment analysis, but on their ability to think like Wall Street professionals. The results? Let’s just say GPT-4 may ace the chatroom, but it still struggles in the boardroom. The Benchmark We Actually Needed FinanceBench isn’t your typical leaderboard filler. Unlike prior datasets, which mostly rely on news headlines or synthetic financial prompts, this one uses real earnings call transcripts from over 130 public companies. It frames the task like a genuine investment analyst workflow: ...