Saturday, December 21, 2024

papers

 [1]

O. Aljaloudi, M. Thiam, M. Qader, M. K. S. Al-Mhdawi, A. Qazi, and N. Dacre, “Examining the Integration of Generative AI Models for Improved Risk Management Practices in the Financial Sector,” Nov. 2024.
[2]
G. Babaei and P. Giudici, “GPT classifications, with application to credit lending,” Machine Learning with Applications, vol. 16, p. 100534, Jun. 2024, doi: 10.1016/j.mlwa.2024.100534.
[3]
G. Bhatia, E. M. B. Nagoudi, H. Cavusoglu, and M. Abdul-Mageed, “FinTral: A Family of GPT-4 Level Multimodal Financial Large Language Models,” Jun. 14, 2024, arXiv: arXiv:2402.10986. doi: 10.48550/arXiv.2402.10986.
[4]
O. Campesato, Python 3 and Machine Learning Using ChatGPT/GPT-4. Walter de Gruyter GmbH & Co KG, 2024.
[5]
A. P. Desai, G. S. Mallya, M. Luqman, T. Ravi, N. Kota, and P. Yadav, “Opportunities and Challenges of Generative-AI in Finance,” Nov. 22, 2024, arXiv: arXiv:2410.15653. doi: 10.48550/arXiv.2410.15653.
[6]
A. P. Desai, G. S. Mallya, M. Luqman, T. Ravi, N. Kota, and P. Yadav, “Opportunities and Challenges of Generative-AI in Finance,” Nov. 22, 2024, arXiv: arXiv:2410.15653. doi: 10.48550/arXiv.2410.15653.
[7]
A. P. Desai, G. S. Mallya, M. Luqman, T. Ravi, N. Kota, and P. Yadav, “Opportunities and Challenges of Generative-AI in Finance,” Nov. 22, 2024, arXiv: arXiv:2410.15653. doi: 10.48550/arXiv.2410.15653.
[8]
N. Dulam, V. Gosukonda, and M. Ankam, “GPT-4 and Beyond: The Role of Generative AI in Data Engineering,” Journal of Bioinformatics and Artificial Intelligence, vol. 4, no. 1, Art. no. 1, Feb. 2024.
[9]
B. Fazlija, M. Ibraimi, A. Forouzandeh, and A. Fazlija, “Implementing Financial Regulations Using Large Language Models,” Nov. 05, 2024, Social Science Research Network, Rochester, NY: 5010694. Accessed: Dec. 19, 2024. [Online]. Available: https://papers.ssrn.com/abstract=5010694
[10]
A. Hinterleitner, T. Bartz-Beielstein, R. Schulz, S. Spengler, T. Winter, and C. Leitenmeier, “Enhancing Feature Selection and Interpretability in AI Regression Tasks Through Feature Attribution,” Sep. 25, 2024, arXiv: arXiv:2409.16787. doi: 10.48550/arXiv.2409.16787.
[11]
M. Hofert, “Assessing ChatGPT’s Proficiency in Quantitative Risk Management,” Risks, vol. 11, no. 9, Art. no. 9, Sep. 2023, doi: 10.3390/risks11090166.
[12]
J. P. Inala et al., “Data Analysis in the Era of Generative AI,” Sep. 27, 2024, arXiv: arXiv:2409.18475. doi: 10.48550/arXiv.2409.18475.
[13]
C. Jeong, “Fine-tuning and Utilization Methods of Domain-specific LLMs,” jiis, vol. 30, no. 1, pp. 93–120, Mar. 2024, doi: 10.13088/jiis.2024.30.1.093.
[14]
D. P. Jeong, Z. C. Lipton, and P. Ravikumar, “LLM-Select: Feature Selection with Large Language Models,” Jul. 02, 2024, arXiv: arXiv:2407.02694. doi: 10.48550/arXiv.2407.02694.
[15]
S. Jomthanachai, W. P. Wong, and K. W. Khaw, “An application of machine learning regression to feature selection: a study of logistics performance and economic attribute,” Neural Comput & Applic, vol. 34, no. 18, pp. 15781–15805, Sep. 2022, doi: 10.1007/s00521-022-07266-6.
[16]
M. Khoja, “AI and Bond Values: How Large Language Models Predict Default Signals,” Sep. 20, 2024, Social Science Research Network, Rochester, NY: 4965227. doi: 10.2139/ssrn.4965227.
[17]
J. Lee, N. Stevens, S. C. Han, and M. Song, “A Survey of Large Language Models in Finance (FinLLMs),” Feb. 04, 2024, arXiv: arXiv:2402.02315. doi: 10.48550/arXiv.2402.02315.
[18]
D. Li, Z. Tan, and H. Liu, “Exploring Large Language Models for Feature Selection: A Data-centric Perspective,” Oct. 23, 2024, arXiv: arXiv:2408.12025. doi: 10.48550/arXiv.2408.12025.
[19]
N. Li, C. Gao, M. Li, Y. Li, and Q. Liao, “EconAgent: Large Language Model-Empowered Agents for Simulating Macroeconomic Activities,” in Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), L.-W. Ku, A. Martins, and V. Srikumar, Eds., Bangkok, Thailand: Association for Computational Linguistics, Aug. 2024, pp. 15523–15536. doi: 10.18653/v1/2024.acl-long.829.
[20]
N. Li, C. Gao, Y. Li, and Q. Liao, “Large Language Model-Empowered Agents for Simulating Macroeconomic Activities,” Oct. 13, 2023, Social Science Research Network, Rochester, NY: 4606937. doi: 10.2139/ssrn.4606937.
[21]
J. Ludwig, S. Mullainathan, and A. Rambachan, “Large Language Models: An Applied Econometric Framework,” Dec. 09, 2024, arXiv: arXiv:2412.07031. doi: 10.48550/arXiv.2412.07031.
[22]
N. Nascimento, C. Tavares, P. Alencar, and D. Cowan, “GPT in Data Science: A Practical Exploration of Model Selection,” in 2023 IEEE International Conference on Big Data (BigData), Dec. 2023, pp. 4325–4334. doi: 10.1109/BigData59044.2023.10386503.
[23]
M. Sanz-Guerrero and J. Arroyo, “Credit Risk Meets Large Language Models: Building a Risk Indicator from Loan Descriptions in P2P Lending,” Aug. 05, 2024, arXiv: arXiv:2401.16458. doi: 10.48550/arXiv.2401.16458.
[24]
E. Sharkey and P. Treleaven, “BERT vs GPT for financial engineering,” Apr. 24, 2024, arXiv: arXiv:2405.12990. doi: 10.48550/arXiv.2405.12990.
[25]
A. C. Teixeira, V. Marar, H. Yazdanpanah, A. Pezente, and M. Ghassemi, “Enhancing Credit Risk Reports Generation using LLMs: An Integration of Bayesian Networks and Labeled Guide Prompting,” in Proceedings of the Fourth ACM International Conference on AI in Finance, in ICAIF ’23. New York, NY, USA: Association for Computing Machinery, Nov. 2023, pp. 340–348. doi: 10.1145/3604237.3626902.
[26]
Y. Wang, J. Zhao, and Y. Lawryshyn, “GPT-Signal: Generative AI for Semi-automated Feature Engineering in the Alpha Research Process,” Oct. 24, 2024, arXiv: arXiv:2410.18448. doi: 10.48550/arXiv.2410.18448.
[27]
“Generative-AI in Finance : Opportunities and Challenges † - Google Search.” Accessed: Dec. 21, 2024. [Online]. Available: https://www.google.com/search?client=ubuntu-sn&channel=fs&q=Generative-AI+in+Finance+%3A+Opportunities+and+Challenges+%E2%80%A0
[28]
“GPT-4 and Beyond: The Role of Generative AI in Data Engineering | Journal of Bioinformatics and Artificial Intelligence.” Accessed: Dec. 21, 2024. [Online]. Available: https://biotechjournal.org/index.php/jbai/article/view/142
[29]
“How to Train Generative AI Using Your Company’s Data.” Accessed: Dec. 21, 2024. [Online]. Available: https://hbr.org/2023/07/how-to-train-generative-ai-using-your-companys-data
[30]
“Proprietary data, your competitive edge in generative AI | IBM.” Accessed: Dec. 21, 2024. [Online]. Available: https://www.ibm.com/think/insights/proprietary-data-gen-ai-competitive-edge

Tuesday, December 3, 2024

Vice President, Quant Development

 


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Saturday, March 20, 2021

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Wednesday, June 3, 2020

MOBILE APP DEVELOPMENT NYC

Mobile Development NYC’s mission is to provide a caring and insightful team. We encourage the strong positive mental attitudes of all of our clients and employees to insure your visions, ideas and focus for your mobile development project manifest into fruition. Our services and the commitment of our team provide the support needed to see your vision through till the end.



Our Site -
mobileappdevelopmentnyc.site

Contact Us - Info@MobiledevNYC
Shivagan Joshi : +1(929) 356-5046
Shivgan3@Gmail.Com

MOBILE APP DEVELOPMENT NYC

Mobile Development NYC’s mission is to provide a caring and insightful team. We encourage the strong positive mental attitudes of all of our clients and employees to insure your visions, ideas and focus for your mobile development project manifest into fruition. Our services and the commitment of our team provide the support needed to see your vision through till the end.



Our Site - mobileappdevelopmentnyc.site

Contact Us - Info@MobiledevNYC
Shivagan Joshi : +1(929) 356-5046
Shivgan3@Gmail.Com