Draft:Financial sentiment analysis
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| Financial sentiment analysis | |
|---|---|
| Subfields | Natural language processing, Machine learning, Finance |
| Applications | Stock market, Cryptocurrency trading, Financial forecasting, Risk assessment |
Financial sentiment analysis (FSA) is the application of sentiment analysis techniques to finance-related text, including corporate filings, financial news, and social-media messages.[1] By extracting positive, negative, or neutral opinions, FSA seeks to quantify investor sentiment and relate it to market sentiment, supporting tasks such as trading, forecasting, and risk management.[1]
Overview
[edit]Financial sentiment analysis combines natural language processing with concepts from financial economics to measure attitudes expressed in market-relevant documents.[2] Typical sources include annual reports, earnings-call transcripts, newswire stories, and social-media posts. Because financial language differs from everyday usage, for example, liability is neutral in accounting contexts, FSA systems use domain-specific lexicons and models.[3]
History
[edit]Early 2000s studies mined internet message boards for investor mood and documented links with equity returns.[4] The first financial lexicons—Henry’s Financial Dictionary followed by the Loughran–McDonald list—were created to capture finance-specific language.[3] The availability of labeled datasets in the 2010s enabled supervised classifiers such as support-vector machines and random forests.[5] Advances in deep learning and transformer models, including BERT and its domain-adapted variant FinBERT, now set state-of-the-art benchmarks.[6]
Techniques
[edit]- Lexicon-based methods – Score text using finance-specific word lists such as Loughran–McDonald.[3]
- Traditional supervised learning – Employ algorithms including naïve Bayes, random forests, and support-vector machines trained on labeled financial corpora.[2]
- Deep learning – Use convolutional neural networks, recurrent neural networks, or transformers to model contextual cues.[5]
- Hybrid and ensemble models – Combine lexicon features with machine-learning or neural outputs for improved robustness.[5]
Applications
[edit]- Market prediction – Sentiment metrics derived from news and social media help forecast short-term price movements and volatility.[7]
- Algorithmic trading – Funds incorporate real-time sentiment signals into automated trading strategies.[8]
- Risk assessment and portfolio management – Analysts gauge managerial tone in filings and calls to inform credit and valuation models.[1]
Trends
[edit]Large language models—including FinGPT[9], BloombergGPT[10], and FinLlama[11]—are being explored for few-shot or zero-shot financial sentiment tasks. Multimodal approaches combine textual sentiment with market microstructure data, and explainable-AI techniques aim to increase interpretability in regulated environments.[1]
References
[edit]- ^ a b c d Du, Kelvin; Xing, Frank; Mao, Rui; Cambria, Erik (April 2024). "Financial Sentiment Analysis: Techniques and Applications". ACM Computing Surveys. 56 (9): 220. doi:10.1145/3649451.
- ^ a b Xing, Frank Z.; Malandri, Lorenzo; Zhang, Yue; Cambria, Erik (December 2020). Financial Sentiment Analysis: An Investigation into Common Mistakes and Silver Bullets. Proceedings of the 28th International Conference on Computational Linguistics. Barcelona. pp. 978–987.
- ^ a b c Loughran, Tim; McDonald, Bill (2011). "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks". The Journal of Finance. 66 (1): 35–65. doi:10.1111/j.1540-6261.2010.01625.x.
- ^ Antweiler, Wolfgang; Frank, Murray Z. (2004). "Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards". The Journal of Finance. 59 (3): 1259–1294. doi:10.1111/j.1540-6261.2004.00662.x.
- ^ a b c Sohangir, Sahar; Wang, Dingding; Pomeranets, Anna; Khoshgoftaar, Taghi M. (2018). "Big Data: Deep Learning for Financial Sentiment Analysis". Journal of Big Data. 5 (3): 1–25. doi:10.1186/s40537-017-0111-6.
- ^ Araci, Dogu (2019). "FinBERT: Financial Sentiment Analysis with Pre-trained Language Models". arXiv preprint. arXiv:1908.10063.
- ^ Chen, Tianyu (2024). "EFSA: Towards Event-Level Financial Sentiment Analysis". Proceedings of ACL 2024: 7455–7467.
- ^ Iacovides, Giorgos; Konstantinidis, Thanos; Xu, Mingxue; Mandic, Danilo (2024). FinLlama: LLM-Based Financial Sentiment Analysis for Algorithmic Trading. 5th ACM International Conference on AI in Finance. pp. 134–142. doi:10.1145/3677052.3698696.
- ^ Yang, Zhen; Zhang, Hongyang; Liu, Jingwei; Wang, Shuai; Chen, Yifan; Wu, Si; Zhang, Ruoyu (2023). "FinGPT: Open-Source Financial Large Language Model for Text-Based Financial Applications". arXiv preprint. arXiv:2306.05429.
- ^ Wu, Shawn; Sun, Raymond; Goyal, Prashant; Gupta, Devanshu; Huang, Zhengping; Luong, Minh; Alcocer, Travis (2023). "BloombergGPT: A Large Language Model for Finance". arXiv preprint. arXiv:2303.17564.
- ^ Iacovides, Giorgos; Konstantinidis, Thanos; Xu, Mingxue; Mandic, Danilo (2024). FinLlama: LLM-Based Financial Sentiment Analysis for Algorithmic Trading. 5th ACM International Conference on AI in Finance. pp. 134–142. doi:10.1145/3677052.3698696.