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Recent advances in large language models (LLMs) have unlocked novel opportunities for machine learning applications in the financial domain.
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Y. Wang, N. Lipka, R. A. Rossi, A. Siu, R. Zhang, and T. Derr, “Knowledge graph prompting for multi-document question answering,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 17, 2024, pp. 19 206–19 214
2024
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M. Rizinski, A. Jankov, V. Sankaradas, E. Pinsky, I. Mishkovski, and D. Trajanov, “Comparative analysis of NLP-based models for company classification,” Information , vol. 15, no. 2, p. 77, 2024
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D. Kanelis and P. L. Siklos, “The ECB press conference statement: deriving a new sentiment indicator for the euro area,” International Journal of Finance & Economics , 2024
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A. Mody and M. Nedeljkovic, “Central bank policies and financial markets: Lessons from the euro crisis,” Journal of Banking & Finance , vol. 158, p. 107033, 2024
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T. Zhou, P. Niu, L. Sun, R. Jin et al. , “One fits all: Power general time series analysis by pretrained LM,” Advances in Neural Information Processing Systems , vol. 36, 2024
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N. Gruver, M. Finzi, S. Qiu, and A. G. Wilson, “Large language models are zero-shot time series forecasters,” Advances in Neural Information Processing Systems , vol. 36, 2024
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T. Bosancic, Y. Nie, and J. M. Mulvey, “Regime-aware factor allocation with optimal feature selection,” Available at SSRN 4825234 , 2024
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2024
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Z. Huang, C. Che, H. Zheng, and C. Li, “Research on generative artificial intelligence for virtual financial robo-advisor,” Academic Journal of Science and Technology , vol. 10, no. 1, pp. 74–80, 2024
2024
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R. Ramyadevi and G. Sasidharan, “Cogniwealth: Revolutionizing finance, empowering investors, and shaping the future of wealth management,” in 2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT) , vol. 5. IEEE, 2024, pp. 378–381
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H. Ko and J. Lee, “Can ChatGPT improve investment decisions? from a portfolio management perspective,” Finance Research Letters , vol. 64, p. 105433, 2024
2024
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M. Noguer i Alonso and H. Dupouy, “Evaluating LLMs in financial tasks-code generation in trading strategies,” Hanane, Evaluating LLMs in Financial Tasks-Code Generation in Trading Strategies (March 8, 2024) , 2024
2024
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A. Kim, M. Muhn, and V. V. Nikolaev, “Financial statement analysis with large language models,” Chicago Booth Research Paper Forthcoming, Fama-Miller Working Paper , 2024
2024
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2024
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G.-Y. Choi and A. G. Kim, “Firm-level tax audits: A Generative AI-based measurement,” Chicago Booth, Research Paper 23-23, 2024. [Online]. Available: https://ssrn.com/abstract=4645865
2024
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I. Bhattacharya and A. Mickovic, “Accounting fraud detection using contextual language learning,” International Journal of Accounting Information Systems , vol. 53, p. 100682, 2024
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D. Yadav, S. Zhang, T. Jin, P. Krishnan, and D. Clarke, “Generative AI based virtual assistant for reconciliation research,” 2024
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Y. Yu, H. Li, Z. Chen, Y. Jiang, Y. Li, D. Zhang, R. Liu, J. W. Suchow, and K. Khashanah, “FinMem: A performance-enhanced LLM trading agent with layered memory and character design,” in Proceedings of the AAAI Symposium Series , vol. 3, no. 1, 2024, pp. 595–597
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S. K. Sarkar and K. Vafa, “Lookahead bias in pretrained language models,” Available at SSRN , 2024
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Y. Yao, J. Duan, K. Xu, Y. Cai, Z. Sun, and Y. Zhang, “A survey on large language model (LLM) security and privacy: The good, the bad, and the ugly,” High-Confidence Computing , p. 100211, 2024
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2024
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Association for Computing Machinery, “ACM Code of Ethics and Professional Conduct,” 2024. [Online]. Available: https://www.acm.org/code-of-ethics
2024
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E. Commission, “Regulatory framework for AI,” 2024. [Online]. Available: https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai
2024
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