Large language models in finance: what is financial sentiment?
Paper reviews financial sentiment analysis using large language models comparing BERT and GPT architectures for enhanced market forecasting and trading.
What it examines
The paper examines financial sentiment through large language models. It explores improvements in sentiment analysis via BERT-style and GPT-style models compared to traditional lexicon methods. The study analyzes market data, textual sources, and algorithmic trading applications, aiming to enhance real-time sentiment extraction for better investment decisions.
What it concludes
The study confirms that large language models improve financial sentiment analysis. BERT-based models excel in structured tasks while GPT-type models generate real-time sentiment narratives. Applications include stock market prediction, algorithmic trading, and portfolio management. Future work may extend to multimodal analysis and domain-specific financial language models.
Evidence objects
Researchers demonstrate that large language models, including BERT-based FinBERT and RoBERTa, achieve breakthrough performance in quantifying financial sentiment with structured classification, outperforming traditional lexicon methods and delivering precise predictions consistently.
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Surprisingly, GPT-based models excel in real-time sentiment forecasting by capturing contextually nuanced language, distinguishing primary drivers from mitigated negative terms, thereby enhancing more accurate predictions of market movements with precision.
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The study introduces a hybrid approach combining static classification with generative techniques, utilizing transformer models and self-attention on earnings transcripts and social media, advancing quantitative finance despite market language challenges.
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This paper examines financial sentiment analysis using LLMs, comparing BERT-type and GPT-type models. Its extensive review and comparative approach offer perspectives on market sentiment extraction, emphasizing novelty in methodology despite incremental advancements. Readers gain valuable insights bridging finance and computational linguistics, making the work both engaging and impactful, scientifically compelling.
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Raw abstract and provenance
Abstract: …A comparative analysis of bidirectional and autoregressive transformer architectures highlights their respective roles in investor sentiment analysis, algorithmic trading, and financial decision-making. By exploring what financial sentiment is and how it is estimated within LLMs, we provide insights into the growing role of… ▽ More Financial sentiment has become a crucial yet complex concept in finance, increasingly used in market forecasting and investment strategies. Despite its growing importance, there remains a need to define and understand what financial sentiment truly represents and how it can be effectively measured. We explore the nature of financial sentiment and investigate how large language models (LLMs) contribute to its estimation. We trace the evolution of sentiment measurement in finance, from market-based and lexicon-based methods to advanced natural language processing techniques. The emergence of LLMs has significantly enhanced sentiment analysis, providing deeper contextual understanding and greater accuracy in extracting sentiment from financial text. We examine how BERT-based models, such as RoBERTa and FinBERT, are optimized for structured sentiment classification, while GPT-based models, including GPT-4, OPT, and LLaMA, excel in financial text generation and real-time sentiment interpretation. A comparative analysis of bidirectional and autoregressive transformer architectures highlights their respective roles in investor sentiment analysis, algorithmic trading, and financial decision-making. By exploring what financial sentiment is and how it is estimated within LLMs, we provide insights into the growing role of AI-driven sentiment analysis in finance. △ Less
Source row: 1167 · abstract type: unknown